From df1af6391fdd59ce0e7fbd7acfc71bf33f69c6b1 Mon Sep 17 00:00:00 2001 From: James Nightingale Date: Sun, 4 Oct 2026 08:46:52 +0100 Subject: [PATCH] feat: coordinate inference campaigns and capture project evidence --- .claude/hooks/end-at-deliverable.sh | 82 + .claude/hooks/session-start.sh | 755 ++ .claude/settings.json | 25 + .github/workflows/dashboard_refresh.yml | 108 + .github/workflows/lint.yml | 92 + .github/workflows/pages_dashboard.yml | 62 + .gitignore | 19 + AGENTS.md | 73 + CHECKIN.md | 118 + CLAUDE.md | 1 + LICENSE | 21 + README.md | 21 +- REFERENCE.md | 111 + badge.json | 6 + bin/pyauto-insight | 11 + campaigns.yaml | 151 + dashboard.html | 62 + dashboard.md | 9636 +++++++++++++++++ insight/__init__.py | 5 + insight/board.py | 410 + insight/campaigns.py | 256 + insight/cli.py | 309 + insight/ingest.py | 340 + insight/registry.py | 281 + insight/summary.py | 347 + insight/theme.py | 4 + migration.yaml | 547 + receipts/lens.json | 16 + registry.yaml | 9 + ruff.toml | 46 + snapshots/lens.json | 9114 ++++++++++++++++ state.json | 18 + tasks/drop_1e3_prior_centring_after_631.md | 10 + tasks/ep_campaign.md | 152 + tasks/ep_scoping.md | 272 + .../gradient_sampler_validated_likelihoods.md | 1 + tasks/graphical_scoping.md | 221 + tasks/reconcile_cortex_ledger_references.md | 33 + tests/conftest.py | 163 + tests/fixtures/duplicate_id.json | 410 + tests/fixtures/galaxy_summary_v1.json | 225 + tests/fixtures/lens_summary_v1.json | 225 + tests/fixtures/unsupported_schema.json | 225 + tests/fixtures/valid_empty.json | 37 + tests/test_board.py | 82 + tests/test_campaigns.py | 89 + tests/test_cli.py | 38 + tests/test_ingest.py | 144 + tests/test_registry.py | 103 + tests/test_summary.py | 174 + 50 files changed, 25658 insertions(+), 2 deletions(-) create mode 100755 .claude/hooks/end-at-deliverable.sh create mode 100755 .claude/hooks/session-start.sh create mode 100644 .claude/settings.json create mode 100644 .github/workflows/dashboard_refresh.yml create mode 100644 .github/workflows/lint.yml create mode 100644 .github/workflows/pages_dashboard.yml create mode 100644 .gitignore create mode 100644 AGENTS.md create mode 100644 CHECKIN.md create mode 100644 CLAUDE.md create mode 100644 LICENSE create mode 100644 REFERENCE.md create mode 100644 badge.json create mode 100755 bin/pyauto-insight create mode 100644 campaigns.yaml create mode 100644 dashboard.html create mode 100644 dashboard.md create mode 100644 insight/__init__.py create mode 100644 insight/board.py create mode 100644 insight/campaigns.py create mode 100644 insight/cli.py create mode 100644 insight/ingest.py create mode 100644 insight/registry.py create mode 100644 insight/summary.py create mode 100644 insight/theme.py create mode 100644 migration.yaml create mode 100644 receipts/lens.json create mode 100644 registry.yaml create mode 100644 ruff.toml create mode 100644 snapshots/lens.json create mode 100644 state.json create mode 100644 tasks/drop_1e3_prior_centring_after_631.md create mode 100644 tasks/ep_campaign.md create mode 100644 tasks/ep_scoping.md create mode 100644 tasks/gradient_sampler_validated_likelihoods.md create mode 100644 tasks/graphical_scoping.md create mode 100644 tasks/reconcile_cortex_ledger_references.md create mode 100644 tests/conftest.py create mode 100644 tests/fixtures/duplicate_id.json create mode 100644 tests/fixtures/galaxy_summary_v1.json create mode 100644 tests/fixtures/lens_summary_v1.json create mode 100644 tests/fixtures/unsupported_schema.json create mode 100644 tests/fixtures/valid_empty.json create mode 100644 tests/test_board.py create mode 100644 tests/test_campaigns.py create mode 100644 tests/test_cli.py create mode 100644 tests/test_ingest.py create mode 100644 tests/test_registry.py create mode 100644 tests/test_summary.py diff --git a/.claude/hooks/end-at-deliverable.sh b/.claude/hooks/end-at-deliverable.sh new file mode 100755 index 0000000..1e52f75 --- /dev/null +++ b/.claude/hooks/end-at-deliverable.sh @@ -0,0 +1,82 @@ +#!/usr/bin/env bash +# GENERATED — canonical source: PyAutoMind/policy/end_at_deliverable_hook.sh +# Installed into every checked-out repo as .claude/hooks/end-at-deliverable.sh by +# `python3 PyAutoMind/scripts/repos_sync.py --write`, and drift-checked by +# `--check`. Edit the canonical file, never a copy. +# +# --------------------------------------------------------------------------- +# Sessions end at their deliverable (PyAutoMind/policy/end_at_deliverable.md). +# +# A PreToolUse guard on the tools that outlive the turn. It exists because the +# prose rule was already written and was still broken twice: five batch members +# armed hourly check-ins on 2026-08-31 (fixed for batch members only), and on +# 2026-09-02/03 a mobile `/prm` re-armed a 60-minute `send_later` hourly from +# 02:39 to 12:11 UTC with no task active, leaving twenty fired one-shots and a +# drained usage window. A rule a session can talk itself past is not a rule, so +# this one is enforced by the harness. +# +# Registered under `hooks.PreToolUse` with the matcher +# ^(send_later|subscribe_pr_activity|ScheduleWakeup|CronCreate|RemoteTrigger|mcp__.*(send_later|subscribe_pr_activity).*)$ +# +# Allowed through: +# * `RemoteTrigger` with action list / get / list_runs / get_run_log — reading +# what already exists never outlives the turn; +# * anything at all when PYAUTO_ALLOW_TIMERS=1 — the human-authorised escape +# for a routine the human actually asked for. +# +# Everything else exits 2 (the harness treats stderr as the reason and blocks +# the call). An unreadable payload also exits 2: this fails closed, because the +# failure it guards against is silent and costs a night of usage. +set -uo pipefail + +# Human-authorised routine: the one way past this guard, and it has to be set +# deliberately in the environment. +[ "${PYAUTO_ALLOW_TIMERS:-}" = "1" ] && exit 0 + +payload="$(cat)" + +# python3 rather than jq: python3 is present everywhere this hook is installed +# (it is what the SessionStart hook guarantees), jq is not. The payload rides in +# the environment rather than on stdin so the heredoc keeps its own stdin. +PYAUTO_HOOK_PAYLOAD="$payload" python3 <<'PY' +import json +import os +import sys + +# Read-only RemoteTrigger actions: they inspect what exists and schedule nothing. +READ_ONLY_REMOTE_TRIGGER = {"list", "get", "list_runs", "get_run_log"} + +REASON = ( + "policy end_at_deliverable: sessions end at their deliverable " + "— {tool} would outlive the turn.\n" + "Report and stop; the human re-runs /prm. Set PYAUTO_ALLOW_TIMERS=1 only " + "for a routine the human asked for.\n" +) + + +def block(tool): + sys.stderr.write(REASON.format(tool=tool)) + raise SystemExit(2) + + +try: + event = json.loads(os.environ.get("PYAUTO_HOOK_PAYLOAD", "")) + if not isinstance(event, dict): + raise ValueError("PreToolUse payload is not a JSON object") + tool = event.get("tool_name") + if not isinstance(tool, str) or not tool: + raise ValueError("PreToolUse payload carries no tool_name") + tool_input = event.get("tool_input") + if not isinstance(tool_input, dict): + tool_input = {} +except Exception: + # Fail closed. A payload this hook cannot read is a call it cannot clear. + block("an unreadable tool call") + +if "RemoteTrigger" in tool: + action = tool_input.get("action") + if isinstance(action, str) and action in READ_ONLY_REMOTE_TRIGGER: + raise SystemExit(0) + +block(tool) +PY diff --git a/.claude/hooks/session-start.sh b/.claude/hooks/session-start.sh new file mode 100755 index 0000000..591c2d7 --- /dev/null +++ b/.claude/hooks/session-start.sh @@ -0,0 +1,755 @@ +#!/usr/bin/env bash +# GENERATED — canonical source: PyAutoMind/policy/session_start_hook.sh +# Installed into every checked-out repo as .claude/hooks/session-start.sh by +# `python3 PyAutoMind/scripts/repos_sync.py --write`, and drift-checked by +# `--check`. Edit the canonical file, never a copy. +# +# --------------------------------------------------------------------------- +# Python 3.12 is the default in Claude Code web/mobile sessions. +# +# The remote container ships /usr/local/bin/python{,3} -> /usr/bin/python3.11, +# a pip whose shebang is #!/usr/bin/python3, and a uv-managed tool set (pytest, +# ruff, black, mypy, pyright, flake8, poetry) every one of which was built on +# 3.11 — one minor version below the floor the organism set for itself in the +# Python 3.12 floor campaign, and below every CI leg it runs. The image's own +# `use-python 3.12` does not fix it: it moves the update-alternatives links +# under /usr/bin, which /usr/local/bin/python{,3} then shadow. +# +# This hook makes a session 3.12 on three surfaces: +# +# 1. a 3.12 virtualenv first on PATH — python, python3, pip, pytest; +# 2. the /usr/local/bin/python{,3} symlinks repointed at 3.12, so anything +# resolving PATH without this session's env (a subprocess with a scrubbed +# environment, a `#!/usr/bin/env python3` script) also gets 3.12; +# 3. the uv-managed tools rebuilt on 3.12 — mypy and flake8 read the +# interpreter's version, so on 3.11 they judged code against 3.11 rules — +# and then repaired, because (2) is what breaks each tool env's own +# `bin/python`, and a rebuild cannot fix a link it is handed. +# +# What it deliberately does NOT touch: the update-alternatives links under +# /usr/bin. Scripts with a literal `#!/usr/bin/python3` shebang follow those, +# and some of the image's own tools (conan) are installed for 3.11 only — a +# flip there breaks them for no gain the three surfaces above don't already +# give. +# +# Remote-only (a local checkout keeps whatever the developer's shell provides), +# idempotent (everything is skipped once it already reads 3.12, so the second +# repo's copy in the same session costs ~0.2s), and non-blocking: every step +# degrades to a logged warning rather than failing the session start. +# +# Per-repo dependencies: a repo that needs more than pytest + PyYAML declares it +# in .claude/session-python.txt — one pip argument per line (`-e .`, a package +# spec, `-r requirements.txt`; `#` comments ignored). Installed additively into +# the shared venv, so the file stays out of this generated hook and the hook +# stays byte-identical in every repo. +set -euo pipefail + +[ "${CLAUDE_CODE_REMOTE:-}" = "true" ] || exit 0 + +VENV="${PYAUTO_SESSION_VENV:-$HOME/.pyauto/session-py312}" +# pytest-xdist is a base dep, not a nicety: a remote container has 4 cores and +# the Brain suite takes 96s on one of them and 28s on four. A session that has +# to `pip install` before it can run tests fast will simply not run them fast. +BASE_DEPS=(pytest PyYAML pytest-xdist) + +# Which checkout is this copy of the hook installed in? +# +# NOT $CLAUDE_PROJECT_DIR. That is the session's project directory, which equals +# the repo only when the session holds exactly ONE repo. A session scoped to +# several organs clones them side by side under the project directory +# (/home/user/PyAutoMind, /home/user/PyAutoBrain, ...), and then +# $CLAUDE_PROJECT_DIR is their parent — so reading session-python.txt from it +# found nothing, silently, in exactly the sessions that hold the most repos. +# +# Derive it from where this script actually is, in both the installed and the +# canonical location. +HOOK_SELF="$(readlink -f "$0")" +case "$(dirname "$HOOK_SELF")" in + */.claude/hooks) REPO_DIR="$(cd "$(dirname "$HOOK_SELF")/../.." && pwd)" ;; + */policy) REPO_DIR="$(cd "$(dirname "$HOOK_SELF")/.." && pwd)" ;; + *) REPO_DIR="${CLAUDE_PROJECT_DIR:-$PWD}" ;; +esac +EXTRAS_FILE="$REPO_DIR/.claude/session-python.txt" + +# stderr, not stdout: a SessionStart hook's stdout is fed to the agent as +# session context. +log() { printf '[session-start] %s\n' "$*" >&2; } + +# And where is the WORKSPACE ROOT — the directory holding the checkouts? +# +# NOT `dirname "$REPO_DIR"`, which is what this was. That is the root only in a +# FLAT workspace. Group the checkouts into family directories and the parent of +# a repo is a family directory, which really exists, really is writable and +# really is not a repo — so every guard below passes and all three consumers go +# wrong in silence: $PYAUTO_ROOT is exported to the whole session as the family +# directory, a second `.claude/` root is written INTO it, and the unshallow +# sweep and the generated fan-out reach that one family instead of every +# checkout. +# +# The rule is the one `PyAutoBrain/bin/_pyauto_root.sh` already ships, in the +# same order, so the session and the Brain's resolvers cannot disagree: +# +# 1. an explicit $PYAUTO_ROOT — the operator's word, taken verbatim; +# 2. the nearest ANCESTOR holding a .pyauto-root marker — the only rule that +# survives the workspace growing subdirectories, because "does this +# directory hold a checkout?" is answered yes by a family directory the +# moment the checkouts move into one; +# 3. the parent of this checkout when it holds a sibling organ; +# 4. the parent regardless, reported as unverified. +# +# The resolver itself is used where it is reachable, so there is one +# implementation to correct. But it CANNOT be a dependency: this hook is +# installed into every repo and runs in containers that hold a single checkout, +# no PyAutoBrain and no marker anywhere above them — the session that most +# needs the hook is the one least likely to have a workspace around it. So the +# same rule runs inline wherever the resolver is absent, steps 3 and 4 never +# require a marker, and the resolver is read in a subshell that reports only +# its two answers: a resolver that is broken, or from a version this copy of +# the hook has never seen, can then never fail a session start. +PYAUTO_ROOT_MARKER=".pyauto-root" + +# The nearest ancestor of $1 holding the marker. A checkout is never its own +# workspace root, so the walk starts one level up. +marked_root() { + local d + d="$(dirname "$1")" + while [ "$d" != "/" ]; do + if [ -f "$d/$PYAUTO_ROOT_MARKER" ]; then + printf '%s' "$d" + return 0 + fi + d="$(dirname "$d")" + done + if [ -f "/$PYAUTO_ROOT_MARKER" ]; then + printf '%s' "/" + return 0 + fi + return 1 +} + +# A directory counts as a workspace root if it holds an organ checkout. Any +# organ: a remote session may hold exactly one, and it is still a root. +holds_an_organ() { + [ -d "$1/PyAutoBrain" ] || [ -d "$1/PyAutoMind" ] || [ -d "$1/PyAutoCortex" ] \ + || [ -d "$1/PyAutoMemory" ] || [ -d "$1/PyAutoEyes" ] || [ -d "$1/PyAutoHeart" ] \ + || [ -d "$1/PyAutoHands" ] || [ -d "$1/PyAutoPulse" ] || [ -d "$1/PyAutoInsight" ] || [ -d "$1/PyAutoNerves" ] \ + || [ -d "$1/PyAutoGut" ] \ + || [ -d "$1/organs/PyAutoBrain" ] || [ -d "$1/organs/PyAutoMind" ] \ + || [ -d "$1/organs/PyAutoCortex" ] || [ -d "$1/organs/PyAutoMemory" ] \ + || [ -d "$1/organs/PyAutoEyes" ] || [ -d "$1/organs/PyAutoHeart" ] \ + || [ -d "$1/organs/PyAutoHands" ] || [ -d "$1/organs/PyAutoPulse" ] || [ -d "$1/organs/PyAutoInsight" ] \ + || [ -d "$1/organs/PyAutoNerves" ] || [ -d "$1/organs/PyAutoGut" ] +} + +# Assigns WORKSPACE_ROOT and WORKSPACE_ROOT_REASON rather than printing: one +# decision produces two answers, and a command substitution would drop the +# reason — which is the whole lesson of a defect whose every consequence was +# silent. +resolve_workspace_root() { + local helper answer + # 1. The operator's word, never second-guessed. The marker only decides + # how the reason reads, so a wrong value from a caller stays visible. + if [ -n "${PYAUTO_ROOT:-}" ]; then + WORKSPACE_ROOT="$PYAUTO_ROOT" + if [ -f "$WORKSPACE_ROOT/$PYAUTO_ROOT_MARKER" ]; then + WORKSPACE_ROOT_REASON="PYAUTO_ROOT" + else + WORKSPACE_ROOT_REASON="PYAUTO_ROOT (unverified - no $PYAUTO_ROOT_MARKER marker)" + fi + return 0 + fi + # The shared resolver, if this checkout can see one: either this checkout + # IS the Brain, or the Brain is beside it. + # + # Its answer is ACCEPTED ONLY IF IT CONTAINS THIS CHECKOUT, because the + # resolver anchors on the BRAIN checkout, not on this one. In a worktree + # bundle the bundle's PyAutoBrain is a symlink into the canonical + # workspace, so the resolver follows it and answers with the canonical + # root — and this session would then export somebody else's workspace to + # the whole session, write a `.claude/` root into it and unshallow its + # repos. Measured on this very bundle: the resolver said + # the canonical workspace for a hook running in a task bundle beside it. + # `scripts/session_bootstrap.sh` guards the same trap the same way; the + # test is "is it an ancestor", not "is the checkout directly under it", so + # a checkout inside a family directory still passes. + local repo_real root_real + repo_real="$(readlink -f "$REPO_DIR" 2>/dev/null || printf '%s' "$REPO_DIR")" + for helper in "$REPO_DIR/bin/_pyauto_root.sh" \ + "$(dirname "$REPO_DIR")/PyAutoBrain/bin/_pyauto_root.sh" \ + "$(dirname "$REPO_DIR")/organs/PyAutoBrain/bin/_pyauto_root.sh" \ + "$(dirname "$(dirname "$REPO_DIR")")/organs/PyAutoBrain/bin/_pyauto_root.sh"; do + [ -r "$helper" ] || continue + # `|` separates the two answers; no reason the resolver states contains + # one. The subshell drops this script's `set -euo pipefail` so that a + # resolver which fails, or which one day needs something this container + # does not have, degrades to the inline rule instead of to a dead + # session. + answer="$( ( + set +euo pipefail + # shellcheck source=/dev/null + . "$helper" >/dev/null 2>&1 + printf '%s|%s' "${PYAUTO_ROOT:-}" "${PYAUTO_ROOT_REASON:-}" + ) 2>/dev/null )" || answer="" + [ -n "${answer%%|*}" ] && [ -d "${answer%%|*}" ] || continue + root_real="$(readlink -f "${answer%%|*}" 2>/dev/null || printf '%s' "${answer%%|*}")" + case "$repo_real" in + "${root_real%/}"/*) ;; + *) continue ;; + esac + WORKSPACE_ROOT="${answer%%|*}" + WORKSPACE_ROOT_REASON="${answer#*|}" + return 0 + done + # 2. A marked ancestor — the root naming itself, which stays right however + # deep under it this checkout sits. + if answer="$(marked_root "$REPO_DIR")"; then + WORKSPACE_ROOT="$answer" + WORKSPACE_ROOT_REASON="$PYAUTO_ROOT_MARKER marker" + return 0 + fi + # 3. Beside this checkout — nothing above us says anything, so what sits + # beside us is all there is: a single-repo remote session, a CI matrix, + # a spawned template. 4. Failing that, the parent anyway: the best guess + # available, and a real path a diagnostic can name. + local parent + parent="$(dirname "$REPO_DIR")" + [ "$(basename "$parent")" = organs ] && parent="$(dirname "$parent")" + WORKSPACE_ROOT="$parent" + if holds_an_organ "$WORKSPACE_ROOT"; then + WORKSPACE_ROOT_REASON="beside this checkout" + else + WORKSPACE_ROOT_REASON="unverified (no sibling organ beside this checkout)" + fi +} +resolve_workspace_root + +is_py312() { + [ -x "$1" ] && "$1" -c 'import sys; raise SystemExit(sys.version_info[:2] != (3, 12))' >/dev/null 2>&1 +} + +find_base_python() { + local candidate + for candidate in /usr/bin/python3.12 /usr/local/bin/python3.12 \ + "$(command -v python3.12 2>/dev/null || true)"; do + if [ -n "$candidate" ] && is_py312 "$candidate"; then + printf '%s\n' "$candidate" + return 0 + fi + done + # No system 3.12 (a future base image could drop it) — uv can fetch one. + if command -v uv >/dev/null 2>&1; then + log "no system python3.12; asking uv to install one" + uv python install 3.12 >&2 || return 1 + candidate="$(uv python find 3.12 2>/dev/null || true)" + if [ -n "$candidate" ] && is_py312 "$candidate"; then + printf '%s\n' "$candidate" + return 0 + fi + fi + return 1 +} + +venv_ready() { + is_py312 "$VENV/bin/python" \ + && [ -x "$VENV/bin/pip" ] \ + && "$VENV/bin/python" -c 'import pytest, yaml, xdist' >/dev/null 2>&1 +} + +# 1. The interpreter a session types: python, python3, pip, pytest. +ensure_venv() { + local base_python + if venv_ready; then + log "reusing $VENV ($("$VENV/bin/python" -V 2>&1))" + return 0 + fi + if ! base_python="$(find_base_python)"; then + log "WARNING: no Python 3.12 in this container; PATH left unchanged" + return 1 + fi + log "building $VENV on $base_python" + rm -rf "$VENV" + mkdir -p "$(dirname "$VENV")" + # --system-site-packages makes the venv a strict SUPERSET of the base + # interpreter. That is what lets `point_system_default` make this venv the + # session's `python3` without silently removing anything the image + # installed: an isolated venv would swap one set of missing modules for + # another, and the session would find out through a ModuleNotFoundError + # that reads like broken code. + if command -v uv >/dev/null 2>&1; then + # --seed puts pip inside the venv too, so `pip install` targets 3.12 + # rather than falling through to the container's 3.11 /usr/bin/pip. + uv venv --seed --system-site-packages --python "$base_python" "$VENV" >&2 + uv pip install --python "$VENV/bin/python" --quiet "${BASE_DEPS[@]}" >&2 + else + "$base_python" -m venv --system-site-packages "$VENV" >&2 + "$VENV/bin/python" -m pip install --quiet --upgrade pip >&2 + "$VENV/bin/python" -m pip install --quiet "${BASE_DEPS[@]}" >&2 + fi + venv_ready || { log "WARNING: could not build a 3.12 venv at $VENV"; return 1; } +} + +# This repo's own dependencies, if it declares any. Additive and marked, so a +# session holding several repos installs each repo's set exactly once. +ensure_repo_extras() { + [ -r "$EXTRAS_FILE" ] || return 0 + local marker args=() + marker="$VENV/.extras-$(cksum <"$EXTRAS_FILE" | tr -d ' /')" + [ -e "$marker" ] && return 0 + while IFS= read -r line; do + line="${line%%#*}" + line="$(printf '%s' "$line" | tr -d '\r' | sed -e 's/^[[:space:]]*//' -e 's/[[:space:]]*$//')" + [ -n "$line" ] && args+=("$line") + done <"$EXTRAS_FILE" + [ ${#args[@]} -gt 0 ] || return 0 + log "installing this repo's declared deps: ${args[*]}" + if (cd "$(dirname "$(dirname "$EXTRAS_FILE")")" && "$VENV/bin/python" -m pip install --quiet "${args[@]}" >&2); then + : >"$marker" + else + log "WARNING: $EXTRAS_FILE install failed; continuing without it" + fi +} + +# 2. What PATH means without this session's env file. +# +# This leg is the one that matters most, because the env file is exactly what a +# multi-repo session does NOT get: Claude Code registers project hooks from the +# session's project directory, that directory is the repos' parent, and no hook +# runs there (see leg 5). So every Bash call in such a session resolves whatever +# the image put on PATH, and these two names are the fallback that has to be +# right on its own. +# +# It used to point them at the BASE interpreter — `point_system_default +# "$(readlink -f "$VENV/bin/python")"` resolved the venv's python straight +# through to /usr/bin/python3.12. That satisfied the version question and lost +# everything else: `python3 -m pytest` answered "No module named pytest", and +# the session's own venv, sitting one directory away with pytest in it, was +# unreachable from any shell. +# +# Two details make the fix work: +# +# * A wrapper script, not a symlink. Python resolves a symlinked executable +# before looking for `pyvenv.cfg`, so a symlink to $VENV/bin/python lands on +# the base interpreter's prefix and the venv is lost again — silently, and +# in the same shape as the bug being fixed. `exec`ing it keeps the venv. +# * --system-site-packages on the venv (see ensure_venv), so pointing the +# system default here adds the session's packages without removing the +# image's. +# Bounded, because this predicate is what runs a candidate interpreter — and +# the one failure mode worth surviving is an interpreter that never returns. +venv_backed() { + [ -x "$1" ] || return 1 + [ "$(timeout 20 "$1" -c 'import sys; print(sys.prefix)' 2>/dev/null)" = "$VENV" ] +} + +# Does following $1's symlinks pass through $2? A venv's python legitimately +# RESOLVES to the same base interpreter a system default points at, so comparing +# endpoints refuses safe rewrites. The question that matters is narrower: is the +# path being rewritten a link in the target's own chain — because then the +# wrapper execs itself. +links_through() { + local node="$1" needle="$2" hops=0 + needle="$(cd "$(dirname "$needle")" 2>/dev/null && printf '%s/%s' "$(pwd -P)" "$(basename "$needle")")" + while [ -L "$node" ] && [ "$hops" -lt 40 ]; do + node="$(cd "$(dirname "$node")" && cd "$(dirname "$(readlink "$node")")" 2>/dev/null && printf '%s/%s' "$(pwd -P)" "$(basename "$(readlink "$node")")")" + [ "$node" = "$needle" ] && return 0 + hops=$((hops + 1)) + done + return 1 +} + +# `rm -f` first, and it is not tidiness. /usr/local/bin/python3 is a SYMLINK to +# the real interpreter, and a redirect opens the link's TARGET: `cat >` there +# overwrites /usr/bin/python3.12 itself with this wrapper. The venv's own +# python symlinks to that same file, so the wrapper then execs itself — the +# container loses its interpreter and every `python3` spins at 100% CPU. Writing +# a fresh file at the path replaces the link instead of following it. +# +# `links_through` is the second half of the same guard: a target that reaches +# the destination through its own symlink chain builds the identical loop by the +# other route, so refuse rather than write it. +write_venv_shim() { + local dest="$1" target="$2" + [ -w "$(dirname "$dest")" ] || { log "WARNING: cannot rewrite $dest (not writable)"; return 1; } + [ -x "$target" ] || { log "WARNING: $target is not executable; leaving $dest alone"; return 1; } + if links_through "$target" "$dest"; then + log "WARNING: refusing to point $dest at $target — it links back through $dest" + return 1 + fi + rm -f "$dest" + cat >"$dest" < $VENV/bin/python (venv, with pytest and yaml)" \ + || log "WARNING: $bin/python3 is still $(timeout 20 "$bin/python3" -V 2>&1)" +} + +# 2b. The `pytest` that PATH actually finds. +# +# $HOME/.local/bin precedes /usr/local/bin, and it holds uv's tool shims — so +# fixing python3 alone still leaves bare `pytest` resolving to uv's ISOLATED +# pytest, which by design cannot see PyYAML or a repo's own extras. In this +# workspace that made `pytest` exit on four collection ImportErrors that read +# like broken source, in a session where the suite was in fact green. +# +# A session should have exactly one pytest, and it should be the venv's. +# Confined to uv's own shim directory: a distro-packaged pytest is not ours to +# overwrite. Runs after retool_uv_tools, which rewrites these same shims. +point_pytest_at_venv() { + local shim_dir="${HOME}/.local/bin" name path + [ -x "$VENV/bin/pytest" ] || return 0 + for name in pytest py.test; do + path="$(command -v "$name" 2>/dev/null)" || continue + [ -n "$path" ] || continue + case "$path" in + "$VENV"/*) continue ;; + "$shim_dir"/*) ;; + *) log "WARNING: $name resolves to $path, outside uv's shim dir — left alone"; continue ;; + esac + write_venv_shim "$path" "$VENV/bin/$name" \ + && log "$path -> $VENV/bin/$name (so \`$name\` sees this repo's deps)" + done +} + +# 2c. Every OTHER console script the venv owns. +# +# `pytest` and `python3` survive without the venv on PATH because 2 and 2b give +# each one its own shim. Nothing else does — and PATH is exactly what a +# multi-repo session never gets, because the env file that exports it is written +# by Claude Code around a hook it never registers (see leg 5). So a dependency +# declared in `.claude/session-python.txt` that ships a CONSOLE SCRIPT rather +# than an importable module was installed into the venv and then invisible. +# +# Measured, not hypothetical: PyAutoHands shells out to `ipynb-py-convert`, and +# with the package installed its suite still failed five tests with +# `FileNotFoundError: ipynb-py-convert` — the binary sitting in $VENV/bin, +# unreachable, while `--check` called the session healthy. A missing binary and +# an unreachable one are the same symptom, and neither says "environment". +# +# The claim policy is 2b's, because the reasoning is 2b's: a name we can reach +# only through uv's shim dir or not at all is ours to provide; a name the image +# owns elsewhere is not ours to overwrite. +point_venv_scripts_at_venv() { + local shim_dir="${HOME}/.local/bin" entry name path + [ -d "$VENV/bin" ] || return 0 + mkdir -p "$shim_dir" 2>/dev/null || return 0 + for entry in "$VENV"/bin/*; do + [ -f "$entry" ] && [ -x "$entry" ] || continue + name="$(basename "$entry")" + case "$name" in + # python/pip are legs 2 and the venv's own plumbing; pytest is 2b. + # The activate family is meant to be sourced, never executed. + python*|pip*|pytest|py.test|activate*|deactivate*|*.bat|*.ps1|*.csh|*.fish|*.nu) continue ;; + esac + path="$(command -v "$name" 2>/dev/null)" || path="" + case "$path" in + "$VENV"/*) continue ;; + "") ;; + "$shim_dir"/*) ;; + *) log "WARNING: $name resolves to $path, outside uv's shim dir — left alone"; continue ;; + esac + write_venv_shim "$shim_dir/$name" "$entry" \ + && log "$shim_dir/$name -> $entry (venv console script)" + done +} + +# 3. The uv-managed tools — rebuilt on 3.12 only where they are not already. +# +# Pinned to the version already installed: this hook changes the INTERPRETER a +# tool runs on, and nothing else. Unpinned, `uv tool install --force` fetches +# the latest release, which quietly moved mypy across a major version (1.19 -> +# 2.3) the first time this ran — a lint-result change nobody asked for, riding +# in on a Python upgrade. Falls back to unpinned only if the pin cannot be +# resolved for 3.12. +retool_uv_tools() { + command -v uv >/dev/null 2>&1 || return 0 + local tools_dir tool name version spec + tools_dir="$(uv tool dir 2>/dev/null || echo "$HOME/.local/share/uv/tools")" + [ -d "$tools_dir" ] || return 0 + for tool in "$tools_dir"/*/; do + name="$(basename "$tool")" + is_py312 "$tool/bin/python" && continue + version="$(uv tool list 2>/dev/null | awk -v n="$name" '$1 == n {print substr($2, 2); exit}')" + spec="$name" + [ -n "$version" ] && spec="$name==$version" + if uv tool install --python 3.12 --force "$spec" >/dev/null 2>&1 \ + || uv tool install --python 3.12 --force "$name" >/dev/null 2>&1; then + log "rebuilt ${spec} on 3.12" + else + log "WARNING: could not rebuild $name on 3.12; it stays on $("$tool/bin/python" -V 2>&1)" + fi + done +} + +# 3b. The link uv's rebuild cannot fix from inside. +# +# uv creates each tool env with `bin/python` as a SYMLINK to whatever `python3` +# was at install time — here `/usr/local/bin/python3`, which leg 2 has already +# replaced with a wrapper that `exec`s the session venv. Every tool env's python +# then resolves its prefix to the VENV: `sys.prefix` is the venv, the tool's own +# site-packages never reaches `sys.path`, and the console script dies with +# `ModuleNotFoundError: No module named 'flake8'` — with flake8 sitting +# installed two directories away. +# +# TWO paths reach that state, which is why this runs after leg 3 rather than +# only on the envs leg 3 rebuilt: leg 3 rebuilds a 3.11 tool and hands the new +# env the hijacked path, and leg 2 separately breaks every PRE-EXISTING tool env +# that already pointed there and that leg 3 skips (`is_py312 … && continue`). +# Running here covers both, because leg 2 runs before leg 3. +# +# Measured 2026-08-27, post-bootstrap: mypy, flake8, black, poetry and pyright +# all dead this way; `ruff` survived (a native binary) and `pytest` survived +# (leg 2b points its shim straight at the venv, which has pytest). The +# bootstrap's `--check` called every one of them "3.12 OK", because the +# interpreter they reach IS 3.12 — it is simply the wrong one. A session then +# lints clean by not linting at all, and CI is the thing that finds out. +# +# The fix is one link: a venv's `bin/python` must resolve to a BASE interpreter, +# never to a path this hook hijacks. +# +# This lived in `scripts/session_bootstrap.sh` for one pass, which is the wrong +# home. The bootstrap is what a MULTI-repo session runs; a SINGLE-repo session +# registers this hook and never calls the bootstrap, so it got leg 2's breakage +# and none of the repair. +repair_uv_tools() { + local tools_dir base tool link prefix + base="$("$VENV/bin/python" -c 'import sys, os; print(os.path.join(sys.base_prefix, "bin", "python3.12"))' 2>/dev/null)" || base="" + [ -x "$base" ] || base="$(command -v python3.12 2>/dev/null)" || base="" + [ -x "$base" ] || return 0 + tools_dir="${PYAUTO_UV_TOOLS_DIR:-$(uv tool dir 2>/dev/null || echo "$HOME/.local/share/uv/tools")}" + [ -d "$tools_dir" ] || return 0 + for tool in "$tools_dir"/*/; do + link="${tool}bin/python" + [ -L "$link" ] || continue + # Ask the interpreter where it thinks it lives, rather than tracing the + # link: `/usr/local/bin/python3` is a WRAPPER SCRIPT (a symlink there + # would lose the venv — leg 2's whole note), so `readlink -f` stops at + # the wrapper and reports nothing about the venv behind it. sys.prefix + # is the outcome; anything else is the mechanism. + prefix="$("$link" -c 'import sys; print(sys.prefix)' 2>/dev/null)" || continue + [ -n "$prefix" ] || continue + [ "$prefix" = "${tool%/}" ] && continue # resolves to its own env: correct + # Non-fatal like every other leg: an unwritable tools dir is a warning, + # never a failed session start. + ln -sfn "$base" "$link" || { + log "WARNING: could not repoint $(basename "${tool%/}") at $base" + continue + } + prefix="$("$link" -c 'import sys; print(sys.prefix)' 2>/dev/null)" || prefix="" + if [ "$prefix" = "${tool%/}" ]; then + log "repointed $(basename "${tool%/}") at $base (it resolved into $VENV, not its own env)" + else + log "WARNING: $(basename "${tool%/}") still resolves to ${prefix:-nothing} — it will not run" + fi + done +} + +# 4. Honest git history. +# +# A remote session clones shallow. `git merge-base --is-ancestor` then LIES +# across the graft boundary: it reports "not an ancestor" for a commit whose +# ancestry is simply not in the clone, and the ship/close-out skills act on that +# answer. A completion record already logged this the hard way +# (complete/2026/08/status-sh-repos-missing-source.md, "environment note"). +# +# These repos are small (single-digit MB of .git), so unshallowing costs a +# couple of seconds once per container and removes a whole class of wrong +# answer. Bounded and non-fatal: a slow or blocked network leaves a shallow +# clone and a warning, never a failed session start. +unshallow_checkout() { + local repo + repo="${1%/}/" # the callers glob, but do not rely on the trailing slash + [ -e "${repo}.git/shallow" ] || return 0 + log "unshallowing $(basename "${repo%/}") (shallow clones make ancestry checks lie)" + if timeout 120 git -C "$repo" fetch --unshallow --quiet 2>/dev/null \ + || timeout 120 git -C "$repo" fetch --depth=2147483647 --quiet 2>/dev/null; then + log " $(basename "${repo%/}"): full history ($(git -C "$repo" rev-list --count HEAD 2>/dev/null) commits)" + else + log " WARNING: $(basename "${repo%/}") is still shallow — run 'git fetch --unshallow' before trusting any ancestry check" + fi +} + +# Two levels, not one. A workspace is `/` or, once its checkouts +# are grouped, `//` — and a family directory has no `.git`, +# so a one-level walk steps straight over every checkout inside it and leaves +# them shallow while reporting nothing. A directory that IS a checkout is never +# descended into (a repo's own subdirectories are not siblings), and the walk +# stops at two levels because that is the shape of the workspace: an unbounded +# walk over every checked-out repo is not something a session start can afford. +ensure_full_clone() { + local entry child + for entry in "$WORKSPACE_ROOT"/*/; do + [ -d "$entry" ] || continue + if [ -e "${entry}.git" ]; then + unshallow_checkout "$entry" + continue + fi + for child in "$entry"*/; do + [ -d "$child" ] && [ -e "${child}.git" ] || continue + unshallow_checkout "$child" + done + done +} + +# 5. Make the NEXT session in this container start correctly. +# +# Claude Code registers project hooks from the session's project directory. In a +# one-repo session that IS the repo, and `/.claude/settings.json` is found. +# In a session holding several organs the project directory is their parent, +# which is not a repo and has no `.claude/` — so none of the per-repo hooks are +# registered and none of this script runs. That is why a multi-repo session came +# up on the container's Python 3.11: not a broken hook, an unreachable one. +# +# A repo cannot ship a file into its own parent, so install it at run time. The +# settings we write fan out to every sibling repo's own hook, so the layout +# stays "each repo owns its hook" and this file stays generated-from-one-source. +# It takes effect on the next session start in this container. +install_workspace_settings() { + # Target the WORKSPACE ROOT, derived from this checkout — not + # $CLAUDE_PROJECT_DIR, and not only when the two differ. + # + # The early return this replaces ("only in the multi-repo layout") made the + # whole leg unreachable. Writing the fan-out requires the hook to be + # running; the hook runs only in a session whose project dir is a repo — + # that is, a SINGLE-repo session — and the early return then skipped it as + # having nothing to add. So the one session type that could seed the + # container never did, and the multi-repo session that needed it never ran + # the hook to find out. Observed directly: a container with two single-repo + # sessions behind it still had no workspace-root settings, and the next + # session — three organs, project dir /home/user — fired no hook, ran on + # PATH's pytest, and left both clones shallow for three minutes until an + # unrelated verb happened to knock on session_bootstrap.sh. + # + # A single-repo session has the same sibling layout one directory up, so it + # can seed the root for free. Skip only if the root is itself a repo (then + # it owns its own hook) or is not writable. + local root="$WORKSPACE_ROOT" + [ -n "$root" ] && [ -d "$root" ] || return 0 + [ -d "$root/.git" ] && return 0 + + local settings="$root/.claude/settings.json" + local fanout="$root/.claude/hooks/session-start.sh" + [ -w "$root" ] || { log "WARNING: $root not writable ($WORKSPACE_ROOT_REASON); multi-repo sessions will keep skipping the hook"; return 0; } + + mkdir -p "$(dirname "$fanout")" + rm -f "$fanout" # never write through a symlink; see write_venv_shim + cat >"$fanout" <<'FANOUT' +#!/usr/bin/env bash +# GENERATED at session start by a PyAuto repo's own session-start hook. +# Runs every sibling repo's hook, because the workspace root is not a repo and +# therefore has no hook of its own. Each repo's hook is idempotent, so the +# second and later ones cost ~0.2s. +set -u +ROOT="$(cd "$(dirname "$(readlink -f "$0")")/../.." && pwd)" + +# Run one checkout's hook. Non-zero only when there is no hook to run, which is +# how a family directory is told apart from a checkout. +run_repo_hook() { + hook="${1%/}/.claude/hooks/session-start.sh" + [ -x "$hook" ] || return 1 + CLAUDE_PROJECT_DIR="${1%/}" "$hook" || \ + printf '[session-start] WARNING: %s failed\n' "$hook" >&2 + return 0 +} + +# Two levels: the checkouts directly under the root, and those one level down +# inside a family directory once the workspace groups them. Anything that +# already carries a hook is run, never walked into. +for entry in "$ROOT"/*/; do + [ -d "$entry" ] || continue + run_repo_hook "$entry" && continue + for child in "$entry"*/; do + [ -d "$child" ] || continue + run_repo_hook "$child" || true + done +done +FANOUT + chmod 0755 "$fanout" + + if [ ! -f "$settings" ]; then + cat >"$settings" <<'SETTINGS' +{ + "hooks": { + "SessionStart": [ + { + "hooks": [ + { + "type": "command", + "command": "$CLAUDE_PROJECT_DIR/.claude/hooks/session-start.sh" + } + ] + } + ] + } +} +SETTINGS + log "installed $settings ($WORKSPACE_ROOT_REASON) — multi-repo sessions in this container now run the hook" + fi +} + +# Git history and hook reachability have nothing to do with the interpreter, so +# they run first and unconditionally: a container where the 3.12 venv cannot be +# built still wants honest ancestry and a hook that fires next session. +# PYAUTO_SESSION_DEFINE_ONLY=1 defines every function and performs no action — +# the seam this hook's own tests use to drive one leg at a time against a +# temporary directory, instead of against the container they run in. Sourcing +# returns; executing exits. +if [ "${PYAUTO_SESSION_DEFINE_ONLY:-}" = "1" ]; then + return 0 2>/dev/null || exit 0 +fi + +# Run leg 3b on its own, without a session start. The door +# `scripts/session_bootstrap.sh` knocks on after it has run every repo's hook — +# a subprocess rather than a source, so this script's `set -euo pipefail` never +# leaks into a caller that is contractually "a bootstrap, never a gate". +if [ "${1:-}" = "--repair-uv-tools" ]; then + repair_uv_tools + exit 0 +fi + +ensure_full_clone +install_workspace_settings + +# PYAUTO_SESSION_SKIP_PYTHON=1 stops here — the seam the hook's own tests use to +# exercise the legs above without building an interpreter. +if [ "${PYAUTO_SESSION_SKIP_PYTHON:-}" = "1" ]; then + log "PYAUTO_SESSION_SKIP_PYTHON=1 — leaving the interpreter alone" + exit 0 +fi + +if ensure_venv; then + ensure_repo_extras + point_system_default + retool_uv_tools + repair_uv_tools + point_pytest_at_venv + point_venv_scripts_at_venv + # Every repo in the session registers this hook, so the second copy must not + # prepend the venv a second time. + if [ -n "${CLAUDE_ENV_FILE:-}" ] && ! grep -qs 'PYAUTO_SESSION_PY312=' "$CLAUDE_ENV_FILE"; then + { + echo "export PYAUTO_SESSION_PY312=\"$VENV\"" + echo "export VIRTUAL_ENV=\"$VENV\"" + echo "export PATH=\"$VENV/bin:\$PATH\"" + # The workspace root, so the Brain's shell/python resolvers agree + # with the session instead of each re-deriving it — and how it was + # reached, because an unverified root that a consumer inherits is + # exactly the answer that used to be wrong without saying so. + echo "export PYAUTO_ROOT=\"$WORKSPACE_ROOT\"" + echo "export PYAUTO_ROOT_REASON=\"$WORKSPACE_ROOT_REASON\"" + } >>"$CLAUDE_ENV_FILE" + fi + log "default python is now $("$VENV/bin/python" -V 2>&1) ($VENV/bin)" +fi diff --git a/.claude/settings.json b/.claude/settings.json new file mode 100644 index 0000000..5578c7d --- /dev/null +++ b/.claude/settings.json @@ -0,0 +1,25 @@ +{ + "hooks": { + "SessionStart": [ + { + "hooks": [ + { + "type": "command", + "command": "$CLAUDE_PROJECT_DIR/.claude/hooks/session-start.sh" + } + ] + } + ], + "PreToolUse": [ + { + "matcher": "^(send_later|subscribe_pr_activity|ScheduleWakeup|CronCreate|RemoteTrigger|mcp__.*(send_later|subscribe_pr_activity).*)$", + "hooks": [ + { + "type": "command", + "command": "$CLAUDE_PROJECT_DIR/.claude/hooks/end-at-deliverable.sh" + } + ] + } + ] + } +} diff --git a/.github/workflows/dashboard_refresh.yml b/.github/workflows/dashboard_refresh.yml new file mode 100644 index 0000000..4d8f55d --- /dev/null +++ b/.github/workflows/dashboard_refresh.yml @@ -0,0 +1,108 @@ +name: Dashboard Refresh + +# Re-ingests every registered instance (resolve its branch to one commit, read +# its summary at that SHA, validate, write receipts/ and the last-good +# snapshots/), re-renders dashboard.md / dashboard.html / badge.json / +# state.json, and commits them to main as github-actions[bot] when they +# changed. (Eyes/Cortex pattern.) +# +# Triggers: +# - repository_dispatch `insight-refresh`: to be fired by each project repo's +# pages_dashboard.yml after it publishes a new summary (sender is implemented by autolens_inference/inference-summary.yml). The payload is only logged: the board +# always re-resolves the branch itself, so a dispatch can never make the +# dashboard disagree with the project's main. +# - a nightly cron: catches any dispatch that did not arrive. +# - push to main touching the registry or the reader, and workflow_dispatch. +# - pull_request: a dry run (ingest + render, no commit) so a reader change +# is exercised against the live feeds before it merges. +# +# Receipts and snapshots carry their `fetched_at` while nothing changes, the +# pages carry no wall-clock stamp, and state.json's `updated` moves only when +# the feed's content does, so a night with no new project commit commits +# nothing. A failed fetch keeps the committed snapshot (shown as cached, never +# re-aged). The body map is read from a checkout of PyAutoMind (public). The +# bot commit uses GITHUB_TOKEN, which never triggers other workflows, so after +# a commit this workflow dispatches pages_dashboard.yml explicitly. + +on: + repository_dispatch: + types: [insight-refresh] + schedule: + - cron: "55 3 * * *" + workflow_dispatch: + push: + branches: [main] + paths: + - "registry.yaml" + - "campaigns.yaml" + - "tasks/**" + - "migration.yaml" + - "insight/**" + pull_request: + +permissions: + contents: write + actions: write + +# Every workflow in this repo that pushes to main shares one queue. +concurrency: + group: ${{ github.event_name == 'pull_request' && format('insight-refresh-pr-{0}', github.ref) || 'insight-main-writers' }} + cancel-in-progress: false + +jobs: + refresh: + runs-on: ubuntu-latest + timeout-minutes: 10 + steps: + - uses: actions/checkout@v4 + + - name: Log the trigger + env: + PAYLOAD: ${{ toJson(github.event.client_payload) }} + run: | + echo "event: ${GITHUB_EVENT_NAME}" + if [ "${GITHUB_EVENT_NAME}" = "repository_dispatch" ]; then echo "payload: ${PAYLOAD}"; fi + + - name: PyAutoMind (the body map that resolves each instance's repo) + uses: actions/checkout@v4 + with: + repository: ${{ github.repository_owner }}/PyAutoMind + path: _mind + + - name: Dependencies + run: python3 -m pip install --quiet pyyaml + + - name: Dry run (pull request) + if: github.event_name == 'pull_request' + env: + GH_TOKEN: ${{ github.token }} + run: | + bin/pyauto-insight board --mind _mind + git status --short + + - name: Ingest, render and commit the dashboard (only when changed) + if: github.event_name != 'pull_request' + env: + GH_TOKEN: ${{ github.token }} + run: | + git config user.name "github-actions[bot]" + git config user.email "41898282+github-actions[bot]@users.noreply.github.com" + FILES="dashboard.md dashboard.html badge.json state.json receipts snapshots" + for attempt in 1 2 3; do + git fetch -q origin main && git reset -q --hard origin/main + bin/pyauto-insight board --mind _mind + git add $FILES + if git diff --cached --quiet; then + echo "dashboard unchanged: nothing to commit" + exit 0 + fi + git commit -q -m "insight: refresh dashboard (${GITHUB_EVENT_NAME}) [skip ci]" + if git push -q origin HEAD:main; then + gh workflow run pages_dashboard.yml --ref main \ + || echo "::warning::could not dispatch pages_dashboard.yml; Pages lags until its next trigger" + exit 0 + fi + echo "push rejected (main moved); retrying on the new tip" + done + echo "::error::could not push the refreshed dashboard after 3 attempts" + exit 1 diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml new file mode 100644 index 0000000..1acb75c --- /dev/null +++ b/.github/workflows/lint.yml @@ -0,0 +1,92 @@ +# lint.yml: the PR / push-to-main gate for the PyAutoInsight organ. +# +# 1. ruff check + ruff format --check +# 2. pytest tests/ (hermetic: registry, summary reader, ingest + receipts, +# board, CLI — every network call is refused; state.json is validated +# against the Brain contract because PyAutoBrain is checked out below) +# 3. `bin/pyauto-insight check --offline`: registry valid against the Mind body +# map, every committed snapshot still validates under this reader, each +# receipt records its snapshot's commit, the committed dashboard was +# built from those receipts, and state.json meets the Brain contract. +# Offline on purpose: a project commit landing mid-PR must not redden an +# unrelated organ PR (dashboard_refresh.yml re-reads the network nightly). +# 4. lychee link-rot over the prose *.md. raw.githubusercontent.com, +# github.com blob pages (GitHub answers non-browser page views with 503; +# a token does not help — the links stay for humans, only the gate skips them), +# dashboard.md (hundreds of commit-pinned evidence links), the organ's own +# Pages URL (until Pages is enabled and the first deploy lands) and +# GitHub issue/pull/commit URLs are excluded. + +name: lint + +on: + pull_request: + push: + branches: [main] + workflow_dispatch: + +permissions: + contents: read + +jobs: + lint: + runs-on: ubuntu-latest + timeout-minutes: 10 + steps: + - uses: actions/checkout@v4 + + - name: PyAutoMind (the body map that resolves each instance's repo) + uses: actions/checkout@v4 + with: + repository: ${{ github.repository_owner }}/PyAutoMind + path: _mind + + - name: PyAutoBrain (the state.json contract validator) + uses: actions/checkout@v4 + with: + repository: ${{ github.repository_owner }}/PyAutoBrain + path: _brain + + - name: Set up Python 3.12 + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Install tools + run: | + python -m pip install --quiet ruff pytest pyyaml + curl -sSfL https://github.com/lycheeverse/lychee/releases/latest/download/lychee-x86_64-unknown-linux-gnu.tar.gz \ + | tar -xz -C /usr/local/bin --strip-components=1 lychee-x86_64-unknown-linux-gnu/lychee + + - name: ruff check + run: ruff check --exclude _mind --exclude _brain . + + - name: ruff format --check + run: ruff format --check --exclude _mind --exclude _brain . + + - name: pytest (hermetic) + env: + PYAUTO_BRAIN: ${{ github.workspace }}/_brain + run: python -m pytest tests -q + + - name: pyauto-insight check --offline + env: + PYAUTO_BRAIN: ${{ github.workspace }}/_brain + run: bin/pyauto-insight check --offline --mind _mind + + - name: lychee (markdown link-rot) + env: + # github.com serves 503 to unauthenticated page fetches (rate limit); + # with a token lychee checks github.com links through the API instead. + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: | + lychee \ + --github-token "${GITHUB_TOKEN}" \ + --exclude '^https://raw\.githubusercontent\.com/' \ + --exclude '^https://github\.com/.*/blob/' \ + --exclude '^https://pyautolabs\.github\.io/PyAutoInsight' \ + --exclude '^https://github\.com/.*/(issues|pull|commit|releases)/' \ + --no-progress \ + --accept '200..=299,429' \ + --max-redirects 5 \ + $(find . -name '*.md' -not -name 'dashboard.md' -not -path './.claude/*' -not -path './.git/*' -not -path './_mind/*' -not -path './_brain/*') diff --git a/.github/workflows/pages_dashboard.yml b/.github/workflows/pages_dashboard.yml new file mode 100644 index 0000000..74b95c3 --- /dev/null +++ b/.github/workflows/pages_dashboard.yml @@ -0,0 +1,62 @@ +name: Pages Dashboard + +# Publishes the generated `dashboard.html` as this repo's GitHub Pages index: +# https://pyautolabs.github.io/PyAutoInsight/ +# +# The page is rendered by `bin/pyauto-insight board` and kept fresh by +# dashboard_refresh.yml. This workflow only publishes it, with badge.json (the +# one-line headline other boards read) and state.json (the organ-cockpit feed, +# contract PyAutoBrain/board/state_schema.json) beside it, and nothing else: +# no summary, receipt or snapshot is copied into the site, and every evidence +# link points at the project repo at the captured commit. (Eyes/Cortex pattern.) + +on: + push: + branches: [main] + paths: + - "dashboard.html" + - "badge.json" + - "state.json" + workflow_dispatch: + +permissions: + contents: read + pages: write + id-token: write + +concurrency: + group: pages + cancel-in-progress: true + +jobs: + deploy: + runs-on: ubuntu-latest + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + steps: + - uses: actions/checkout@v4 + # enablement: true creates the Pages site on first run where the token + # may; otherwise enable Pages (source: GitHub Actions) once by hand. + - uses: actions/configure-pages@v5 + with: + enablement: true + - name: Stage the dashboard as the site index + run: | + mkdir -p _site + cp dashboard.html _site/index.html + cp badge.json _site/badge.json + cp state.json _site/state.json + # The contract is owned by the Brain (board/_state.py); the Insight only + # emits it. A feed the cockpit cannot read fails loudly here. + - uses: actions/checkout@v4 + with: + repository: PyAutoLabs/PyAutoBrain + path: PyAutoBrain + - name: Validate the cockpit feed against the Brain contract + run: python3 PyAutoBrain/board/_state.py _site/state.json + - uses: actions/upload-pages-artifact@v3 + with: + path: _site + - id: deployment + uses: actions/deploy-pages@v4 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..ace699e --- /dev/null +++ b/.gitignore @@ -0,0 +1,19 @@ +__pycache__/ +*.pyc +.ruff_cache/ +.pytest_cache/ +*.log + +# Local virtualenvs +.venv/ +venv/ + +# Pages staging +_site/ + +# PyAutoMind checkout the refresh workflow reads the body map from +_mind/ + +# Half-written receipt/snapshot files (written then renamed into place) +receipts/*.tmp +snapshots/*.tmp diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..799c391 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,73 @@ +# PyAutoInsight — agent instructions + +Insight owns inference campaign intent, pending domain tasks, the instance registry, +the inference-summary read contract, ingest receipts and this evidence dashboard. +Read CHECKIN.md for single-chat operation, REFERENCE.md for contracts, campaigns.yaml +and tasks/ for intent. Mind owns bounded implementation issues/PRs and repository +claims. Cortex owns authoritative science runs, observations and human conclusions. +Projects own exporters, raw results, samples and execution drivers. Do not create +an inference conductor, select defaults or turn process completion into scientific +acceptance. Refreshing the board neither checks in nor changes task status. + +Before edits fetch and read current instructions. Before PR run ruff check ., +ruff format --check ., python -m pytest tests -q and bin/pyauto-insight check --offline. +No scientific libraries are imported by this reader. No automatic compute submission. + + +**You are one organ of the PyAuto organism** — an agentic ecosystem for +human-led, natural-language software development. The organs below are +peer repositories; this repo is one of them, not a part of another. +Canonical boundaries live in `PyAutoBrain/ORGANISM.md`; the full body map +(every repo, not just organs) is `PyAutoMind/repos.yaml`. + +| Organ | Repo | Role | +|-------|------|------| +| **Brain** | PyAutoBrain | Reasoning/orchestration layer; how work is decomposed and routed; the specialist agents. | +| **Mind** | PyAutoMind | Intent, goals, priorities, workflow state; every task starts as a markdown prompt here. | +| **Cortex** | PyAutoCortex | The Cortex — where the organism keeps track of what is true: the science body map (`projects.yaml`) and one ledger per science project (what was run, what came back, what was learned, where to pick up); the science mirror of the Mind (runs and a dated log, not prompts and PRs). | +| **Memory** | PyAutoMemory | Long-term scientific/software/project knowledge (see science pointer below). | +| **Eyes** | PyAutoEyes | The Eyes — where the organism sees what its figures look like: the cross-project visualization dashboard over the `_visualization` project repos (autolens_visualization, autogalaxy_visualization, autofit_visualization and autocti_visualization) — the registry of those repos, the tracked-manifest read contract (`gallery/viz_manifest.yaml`) and the Pages board that links to their PNGs as the single point of contact for the visual behaviour of the whole ecosystem. Renders nothing and copies no figures (the project repos render and hold them); never judges them (the Brain's Eyes conductor does) and never edits library plot code (critiques route through intake). | +| **Ears** | PyAutoEars | The Ears — the community listening organ: owns read-only public conversation collection, the versioned community snapshot contract, coverage receipts and the dashboard. GitHub conversations remain authoritative; Brain’s Community conductor owns judgement, reply drafts and development routing, and Mind owns task state. Never posts replies, labels or issues, exports raw transcripts or private sources, or treats unknown coverage as no work. | +| **Heart** | PyAutoHeart | Health/readiness — the authoritative "is it safe to release?" verdict. | +| **Hands** | PyAutoHands | Packaging, tagging, notebook generation, PyPI release execution. | +| **Pulse** | PyAutoPulse | The Pulse — where the organism feels how fast it runs: the cross-project profiling dashboard over the `_profiling` project repos (today autolens_profiling) — campaign intent and pending domain tasks, the instance registry, the versioned `profiling-summary` read contract (v1 live at `autolens_profiling/dashboard/summary.json`), the ingest receipts (resolved commit per project per render) and the Pages board. Validates the exchange contract only; never moves pins, combines unmatched timings, applies the compile threshold to runtime, computes an ecosystem-wide speed score or issues a Heart verdict, and never judges (the Brain's profiling conductor does); the project repos keep their producers, results, drift policy and their own Pages page. | +| **Insight** | PyAutoInsight | Owns inference campaign intent and pending domain tasks, the cross-project inference instance registry, versioned `inference-summary` read contract, ingest receipts and evidence dashboard. Projects own execution, producers and raw samples; Cortex owns scientific run records, observations and human conclusions; Mind owns bounded implementation lifecycle and repository claims. Never infers scientific acceptance from execution, ranks incompatible runs or submits compute on refresh. | +| **Nerves** | PyAutoNerves | The Nerves — the configuration/serialization layer connecting workspace conventions to libraries (layered config, version handshake, test_mode), delivered as the `autonerves` package. | +| **Gut** | PyAutoGut | Owns the lifecycle of condemned self-material (stale branches, stashes, dead code/tests): holds it as durable, recoverable git refs through a transit window and voids it on a sweep. The storage mirror of Memory (retention vs release). | + +Call chain (always this order): **Brain → Heart (gate) → Build (execute)**. Brain agents are **conductors** (front-door; a human drives them; they decide *and* act) or **faculties** (read-only opinions the conductors consult; they judge and stop). New capability grows as a faculty, not a new organ, unless it owns state or effects no existing organ can. + +Generated from `PyAutoMind/repos.yaml` + `PyAutoBrain/ORGANISM.md`; edit there, then run `python3 PyAutoMind/scripts/repos_sync.py --write`. + + +## Never rewrite history + +Never rewrite pushed history on any repo with a remote — no `git init` over a +tracked repo, no force-push to `main`, no fresh-start "Initial commit", no +`filter-repo` / `filter-branch` / `rebase -i` on pushed branches. To get a +clean tree: `git fetch origin && git reset --hard origin/main && git clean -fd`. + + +## Sessions end at their deliverable + +A session ends when it reports its deliverable — never arm anything that +outlives the turn to wait for CI, a review or a merge: no `send_later`, no +`subscribe_pr_activity`, no `CronCreate`, no `ScheduleWakeup`, no `/loop`, no +`RemoteTrigger` create/update/run. Judge once, report, stop; the human re-runs +`/prm` (or the batch review) when it is green. Measured: five batch members +armed hourly check-ins on 2026-08-31, and a mobile `/prm` re-armed a 60-minute +`send_later` hourly all night on 2026-09-03 with no task active, draining usage. + + +## Where to file + +Questions, help with code or an analysis, ideas, bug reports and results from a +user or collaborator — or an agent acting for one — go to + in the matching category +(Help & Questions, Ideas & Proposals, Bugs & Errors, Show and tell; +Announcements is maintainers-only), never to this repo's Issues. An agent never +runs `gh issue create` for such a report: it drafts the title, category and +body and hands them to the human (sessions cannot create Discussions). Only the +development flow — Mind prompt → `/start_dev` → `/create_issue` → one issue per +task → PR — opens issues here. Why: `PyAutoMind/policy/community_surface.md`. + diff --git a/CHECKIN.md b/CHECKIN.md new file mode 100644 index 0000000..4a467bc --- /dev/null +++ b/CHECKIN.md @@ -0,0 +1,118 @@ +# All inference campaigns in one chat + +The editable prompt at the top of the dashboard is the entry point. Use it +unchanged for a complete sweep. A campaign focus or idea supplied before or +after the prompt, or appended inside it, changes emphasis but never removes +the overall sweep. Copying text does not launch a job or grant an approval. +The dashboard is a static evidence view, not a scheduler or a running agent. + +## Check-in procedure + +1. Fetch Insight, Mind, Cortex and the relevant project default branches; read + their instructions. Read `campaigns.yaml`, linked task contracts and + `migration.yaml`. Resolve old Mind paths through that mapping. Read the + last durable check-in report, if any; `last_checkin: null` means no complete + sweep has yet been recorded. The initial migration is not a check-in. +2. Inspect current project ledgers, published `inference-summary` feeds, + relevant issue/PR/release state and Cortex `projects/.md` Now/Runs/Log + records. Pinned links identify the last reviewed evidence; check current + branches for later changes. Do not use archived Cortex tasks or rulings as + a live workflow. Do not revive the retired `inference_programme`. +3. Where authorized access exists, use the existing project's pull/status + driver to obtain scheduler state and outputs. Record which source revision, + scheduler query time, job IDs and result/archive locations were actually + inspected. Otherwise explicitly say unavailable: old `running` ledger rows + and active campaign labels are not current job evidence. Missing output is + neither success nor failure. Keep failed/stopped seeds, partial pipelines + and missing diagnostics or sample archives in the coverage report. +4. Compare with the previous report and give one concise campaign table: + verified changes, current coordination state, current job evidence, + blockers and next bounded step. Separate execution completion from + convergence and human scientific acceptance. Respect declared target, + dataset, model, sampler, seed and parity identities; never rank incompatible + targets or cherry-pick the best seed. Keep setup, sampling and total wall + definitions visible and do not sum overlapping stage timings. +5. Apply the user's ideas and focus to the proposed priority order, retaining + every open campaign in the sweep. An idea remains a hypothesis. Preserve + blocked, parked, decision-gated and needs-slicing states until their + explicit evidence or human decision changes. Check Mind claims before a + bounded development phase. Use existing project drivers and Brain/faculty + routes; no new inference conductor is required. A check-in grants no + compute, default change, scientific threshold, merge or release approval. +6. Write `checkins/YYYY-MM-DDTHHMMSSZ.md` with the template below. Update + `campaigns.yaml` only with verified coordination changes, dated sources and + next actions; link the report in each changed row (`checkin_report`). Set + `last_checkin` to the completed sweep's ISO UTC timestamp and + `last_checkin_report` to that report. Log inaccessible sources and their + consequences even if no verified change occurred. Do not advance evidence + measurement dates or cached ages. A board refresh must not stamp a + check-in, change task state or imply scientific acceptance. +7. Run `bin/pyauto-insight board --offline` and + `bin/pyauto-insight check --offline`, then the repository's required CI. + Persist the report/ledger/generated board through the normal issue/PR + workflow and report its URL and merge state. Another chat resumes from + these files, not remembered conversation state. + +## Durable report template + +```markdown +# Inference check-in — + +Previous: +Scope: all open campaigns; optional user focus +User decisions: + +## Sources inspected + +| Source | Commit / query time | Evidence | Availability | +|---|---|---|---| +| | | | | + +## Campaign sweep + +| Campaign | Change since previous | Coordination state | Job evidence | Blocker | Next bounded step | +|---|---|---|---|---|---| + +## Coverage and limits + +Include failed seeds, partial stages, unknown provenance, missing diagnostics, +sample/archive access limits and stale cached producers. Link scientific +observations and human conclusions in Cortex; do not write competing verdicts. + +## Decisions and handoff + +Record proposals separately from authorized actions. Link Mind implementation +issues/PRs and completion records. State explicitly whether any compute was +submitted, and under what separate authorization; default is none. +``` + +## Ownership and migration + +Insight owns inference campaign intent, pending domain tasks and their next +steps. `campaigns.yaml` is the live coordination ledger; task IDs are stable. +The open task table includes blocked/parked work and is not an execution queue. +Historical `Status`, `Unattended` or routing headers in preserved task text +are original evidence, not a second independently editable schedule. + +The original migrated task bytes are immutable and hashed in `migration.yaml`. +Add subsequent decisions, implementation links and updated next steps to the +ledger and durable reports rather than changing those originals. New tasks +may have their own maintained contract. For mixed campaign maps, completed +phases remain historical and are not reissued; deferred human gates survive. +The EP map's profiling bootstrap/scoping references route to Pulse; library +implementation phases route to Mind. Neither constitutes another organ queue. + +Mind retains bounded implementation prompts, issue/PR lifecycle and repository +claims. Reuse existing issues; a broad campaign map is never issued wholesale. +Link the canonical Insight task from a small implementation prompt and link +its completion record back after close-out. Cortex retains authoritative run +records, observations and human conclusions in its current Now/Runs/Log +contract; project ledgers retain detailed experiment evidence and commentary. +Where a historic project document conflicts with that contract, follow Cortex. +No task migration moves samples, outputs or scientific records. + +The initial audit in `migration.yaml` records all screened pending Mind +candidates, retained implementation/dependency ownership and pinned source +revisions. Destination files must land before deleting source task files or +removing the migrated ideas line. Keep historical completion records intact; +repair current routing references to canonical Insight URLs after landing. diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..43c994c --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1 @@ +@AGENTS.md diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..7624cb6 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2018 Jammy2211 + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md index 5f465ea..a689032 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,22 @@ # PyAutoInsight -Inference campaign coordination and evidence dashboard. +One dashboard and one ongoing chat for inference campaigns. -Implementation tracked in #1. No scientific results or job status are asserted by this bootstrap. +[Dashboard](https://pyautolabs.github.io/PyAutoInsight/) · [Check in](CHECKIN.md) · +[Contract](REFERENCE.md) · [Campaigns](campaigns.yaml) + +Copy the editable prompt at the top of the dashboard unchanged for a full sweep. +Add a campaign focus or idea before or after it; the overall sweep remains. +Tracked campaigns are not evidence of running jobs. The organ owns coordination; +Cortex records scientific observations and human conclusions, and projects retain +raw samples and execution. Mind tracks bounded implementation PRs and claims. + +```bash +python3 -m pip install pyyaml +bin/pyauto-insight board +bin/pyauto-insight check --offline +bin/pyauto-insight census +``` + +The first real producer is autolens_inference. A second-producer fixture tests +interoperability; it is not a claim of a second adopter. diff --git a/REFERENCE.md b/REFERENCE.md new file mode 100644 index 0000000..1d635d2 --- /dev/null +++ b/REFERENCE.md @@ -0,0 +1,111 @@ +# PyAutoInsight contracts + +Insight coordinates inference campaigns and pending domain tasks. The project owns +measurements, exporters and storage. Cortex owns run records, observations and human +conclusions. Mind owns bounded implementation issues, PRs and repository claims. +There is no inference conductor and no new scientific execution engine. + +## Registry and exchange + +`registry.yaml` version `schema: 1` lists instances with `instance`, `repo`, +`summary_path`, `supported_schema`, `dashboard_url`, `library_refs` and +`cortex_project`. Repository identity/location resolves from Mind's `repos.yaml`. +A source is registered once. A fixture demonstrates generic reading without being +registered as a real second adopter. Initial real producer: autolens_inference. + +The reader supports `schema: inference-summary`, integer `version: 1`. +Required envelope keys: project, scope, generated_at, evidence_updated_at, +producer_revision, coverage, records, comparisons, comparison_policy, limitations. +`project` equals the registry's Mind identity. Times are UTC; unknown evidence time +has an explicit reason. Producer revision is the producing code's full commit, +separate from the ingest receipt commit containing the generated file. Null +producer revision requires a reason. `valid_until` is optional; absent means +freshness policy unspecified. Generation time never substitutes for measurement time. +Additive fields are allowed. Breaking versions require consumer support first. + +Each record preserves id, parent_run_id, stage, target, dataset, model, pipeline, +sampler, configuration, backend, hardware, precision, seed, dependency_revisions, +execution, scientific, timings, diagnostics, comparison, samples, evidence_paths, +measured_at and archived. Unknown values remain null with recorded limitations or +unknown_reasons. Pipeline parent and stage records are distinct; record counts +are not counts of independent runs. + +Execution status and completed markers do not certify convergence or acceptance. +Scientific assessment remains `not_assessed` unless explicitly provided by the +producer's protocol. No universal threshold, default-sampler promotion or best-seed +selection is performed. Failed/stopped/incomplete records remain visible. Historical +project records are labelled archived and separated from current results; the retired +inference programme is never imported. Coverage exclusions preserve invalid rows +with reasons instead of silently losing them. Unknown expected coverage is explicit. + +Four clocks (`setup_s`, `sampling_s`, `total_s`, `compile_s`) each have a textual +definition. Null does not mean zero. The reader neither subtracts clocks to derive +setup nor sums stages. Diagnostics carry `status` and `values`. Samples carry +availability and access limitations; safe output paths are hints, not verified +public archive links. Large samples remain in project storage. + +Comparisons declare id, records and protocol. The reader refuses changed scientific +target/dataset/model/pipeline/stage/seed identities and incompatible clock definitions. +Unknown controls are not proof of parity. It displays refusals without ratios or +rankings. The initial producer declares target/seed/stage context but no certified +comparison pairs. Detailed original evidence remains linked at the capture commit. + +## Ingest and output + +`bin/pyauto-insight fetch` resolves each project branch once and fetches the declared +summary at that full SHA. It validates the contract and project identity, then writes +`receipts/.json`; a good read also writes `snapshots/.json`. +A failed fetch never overwrites a last-good capture. Cached evidence retains its +original commit, capture time and evidence time with the new failure visible. +Unchanged fetches preserve capture timestamps. Offline reads validate stored inputs. +Malformed versions, identities, dates, nonfinite numbers and unsafe evidence paths +are invalid, never scientific verdicts. A valid empty feed says no measurements. + +`board` emits dashboard.md, dashboard.html, state.json and badge.json. The state +feed uses Brain's cockpit v1 contract, organ `insight`, repo `PyAutoInsight`. +Its updated timestamp describes monitoring output, never a scientific check-in. +Integrity, freshness and scientific assessment remain separate. No invented TTL +applies when the producer has not declared a deadline. + +`board --offline` reproduces captures without network. `--from lens=PATH` is an +explicit local preview, labelled with checkout revision and dirty state; it writes +no capture receipts. `check --offline` validates registry, snapshots, migration, +campaigns, dashboard input digests and the Brain state contract. It detects stale +campaign and evidence input renders. It does not query scheduler state. + +## Campaign and migration ledgers + +`campaigns.yaml` version 1 owns campaigns and tasks. Rows require stable id, title, +status, reviewed date `updated`, next step; task rows name a campaign and safe existing +tasks/ path. Campaigns also retain recent_progress, blockers and job_status. Tracked +active is not a running job. Blocked, parked, needs-decision and needs-slicing remain +open; complete/superseded remain in history but leave the open table. + +`migration.yaml` pins source repo/commit, original path and SHA256 for each migrated +task (including a verbatim source fragment where declared). Original bytes are frozen: +subsequent decisions go into check-in reports and ledger fields, not rewritten origin +text. The audit lists retained mixed/library work and authoritative Cortex links. +Destination files must merge before Mind deletes any original; current references +then resolve to Insight. Historical records are not rewritten. + +`last_checkin` is null until a real sweep. A timestamp requires a durable report at +`last_checkin_report` under checkins/. CHECKIN.md defines verified-source recording, +changes since prior sweep, missing access and handoffs. A refresh never stamps this +field or changes tasks. The editable prompt always retains the complete sweep even +with campaign-specific direction. Clipboard copies textarea.value with a selectable +fallback. Copying a prompt grants no compute/default/approval authority. + +## Workflows + +The project publication workflow writes its summary then sends `insight-refresh` +using the existing organisation PAT. Payload source_commit identifies the published +project commit; summary_source_commit identifies producing code/evidence history. +The organ independently resolves its registered branch, records the actual ingest +commit and refreshes the board. Nightly/manual refresh catches missed events. +The receiver explicitly dispatches Pages after its bot commit because GITHUB_TOKEN +pushes do not trigger another workflow. Pages deploys only generated HTML, badge +and state; links continue to point into project repositories. + +Before shipping: Ruff, formatting, full pytest and offline contract validation. +Integration CI validates Brain's feed contract. Actual sender/receiver success and +receipt revision are deployment evidence, distinct from mocked workflow tests. diff --git a/badge.json b/badge.json new file mode 100644 index 0000000..ec61199 --- /dev/null +++ b/badge.json @@ -0,0 +1,6 @@ +{ + "schemaVersion": 1, + "label": "insight", + "message": "1 projects \u00b7 56 records \u00b7 0 cached", + "color": "yellow" +} diff --git a/bin/pyauto-insight b/bin/pyauto-insight new file mode 100755 index 0000000..047b4a9 --- /dev/null +++ b/bin/pyauto-insight @@ -0,0 +1,11 @@ +#!/usr/bin/env python3 +"""pyauto-insight: check | board | census | fetch. See `pyauto-insight --help` and README.md.""" + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from insight.cli import main # noqa: E402 + +raise SystemExit(main()) diff --git a/campaigns.yaml b/campaigns.yaml new file mode 100644 index 0000000..cfe8ac2 --- /dev/null +++ b/campaigns.yaml @@ -0,0 +1,151 @@ +version: 1 +last_checkin: null +campaigns: +- id: slam-hst-base + title: HST SLaM base backend parity + status: active + updated: '2026-10-04' + next: Reconcile all seeds and stages with current project rows and Cortex before proposing another run. + recent_progress: Project and Cortex ledgers retain the 24 September rerun submissions; published rows require + coverage reconciliation. + blockers: Live RAL status and output/archive availability unverified. + job_status: unknown — live scheduler not queried; historical ledger states are not current job evidence + evidence: https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/autolens_inference.md + project: autolens_inference +- id: slam-hst-delaunay + title: HST SLaM Delaunay variant + status: active + updated: '2026-10-04' + next: Retrieve or account for every rerun, failed seed and missing stage; preserve the archived comparison + context. + recent_progress: Delaunay is a separate target/recipe; pre-speedup runs are archived, reruns recorded on + 24 September. + blockers: Do not merge the Delaunay target with rectangular comparisons; current job status unknown. + job_status: unknown — live scheduler not queried; historical ledger states are not current job evidence + evidence: https://github.com/PyAutoLabs/autolens_inference/blob/146a84346eaa51eda158634cae520d677f3e6213/wiki/project/state.md + project: autolens_inference +- id: point-source-admission + title: Point-source sampler admission + status: needs-decision + updated: '2026-10-04' + next: Assess a bounded gradient-sampler admission proposal with the human; no new job is authorized. + recent_progress: 2 October project record reports five Nautilus seeds; no gradient-sampler admission measurement. + blockers: Gradient cost share and sampler admission remain unmeasured; Pulse owns likelihood optimisation. + job_status: unknown — live scheduler not queried; historical ledger states are not current job evidence + evidence: https://github.com/PyAutoLabs/autolens_inference/blob/146a84346eaa51eda158634cae520d677f3e6213/wiki/project/state.md + project: autolens_inference +- id: graphical-ep + title: Graphical and EP inference coordination + status: needs-slicing + updated: '2026-10-04' + next: Reconcile current Cortex observations and shipped fixes, then select one bounded phase. + recent_progress: Mind campaign map and two scoping contracts preserved verbatim; implementation work remains + in Mind. + blockers: Old scoping contains completed phases and decision gates; not a bulk runnable queue. + job_status: unknown — live scheduler not queried; historical ledger states are not current job evidence + evidence: https://github.com/PyAutoLabs/PyAutoMind/blob/8dd1edc7e04c960daae3663513c61395823eddb0/draft/research/graphical_ep/ep_campaign.md + project: graphical_ep +- id: analytic-gaussian + title: Analytic Gaussian ensemble and control calibration + status: blocked + updated: '2026-10-04' + next: Use Cortex planned diagnostic on passing, edge and worst seeds; retain the human conclusion there. + recent_progress: Cortex records accepted wave-1 baseline and 30 September independent-review caveats. + blockers: N=25 stays unsubmitted until control/threshold diagnostic is resolved. + job_status: unknown — live scheduler not queried; historical ledger states are not current job evidence + evidence: https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/analytic_gaussian.md + project: analytic_gaussian +- id: ep-toy-gaussian + title: N=50 NUTS versus three EP repeats + status: needs-decision + updated: '2026-10-04' + next: Present the existing wave-2 proposal with current dependency evidence for decision. + recent_progress: Cortex records failed wave 1 (EMFILE/OOM), wave-2 preparation and no submission. + blockers: Explicit human go; sample entry/export and resource settings must be verified. + job_status: unknown — live scheduler not queried; historical ledger states are not current job evidence + evidence: https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/ep_toy_gaussian.md + project: ep_toy_gaussian +- id: slope-hierarchy-scale + title: Hierarchical slope recovery at scale + status: blocked + updated: '2026-10-04' + next: Verify the moments fix release and project mirror; recover missing summaries from existing samples + before a proposed rerun. + recent_progress: Cortex retains graphical baseline, post-processing OOM and stale EP hierarchical factor; + Mind records later moments fix. + blockers: Summary/sample retrieval and release/RAL mirror reconciliation outstanding; no reclassification + from stale prose. + job_status: unknown — live scheduler not queried; historical ledger states are not current job evidence + evidence: https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/slope_hierarchy_scale.md + project: slope_hierarchy_scale +- id: ic50-scale + title: IC50 EP and graphical scale ladder + status: blocked + updated: '2026-10-04' + next: Check implementation close-outs, then propose a seeded ladder with appropriate output and mean-field + diagnostics. + recent_progress: Cortex records N=5/10/25 results, failed N=50 EP rung and width-interpretation caveat. + blockers: Projection fix and mean-field width reporting require reconciliation before scientific comparison. + job_status: unknown — live scheduler not queried; historical ledger states are not current job evidence + evidence: https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/ic50_workspace.md + project: ic50_workspace +tasks: +- id: drop_1e3_prior_centring_after_631 + campaign: slam-hst-base + title: Drop the 1e-3 prior centring for shear / multipole / ell_comps in the benchmark + status: blocked + priority: normal + path: tasks/drop_1e3_prior_centring_after_631.md + updated: '2026-10-04' + next: Verify the release containing Galaxy#634 and Lens#754 before changing priors; retain zero-centre gradient + witness. + evidence: https://github.com/PyAutoLabs/PyAutoMind/blob/8dd1edc7e04c960daae3663513c61395823eddb0/draft/feature/autolens_inference/drop_1e3_prior_centring_after_631.md +- id: reconcile_cortex_ledger_references + campaign: slam-hst-base + title: Reconcile inference documentation with the current Cortex ledger contract + status: ready + priority: normal + path: tasks/reconcile_cortex_ledger_references.md + updated: '2026-10-04' + next: Reconcile Now/Runs/Log documentation via the bounded organ implementation PR; do not alter scientific + records. + evidence: https://github.com/PyAutoLabs/PyAutoMind/blob/8dd1edc7e04c960daae3663513c61395823eddb0/draft/docs/autolens_inference/reconcile_cortex_ledger_references.md +- id: ep_campaign + campaign: graphical-ep + title: EP campaign — phase map for the 2026 Q3 graphical/EP push + status: needs-decision + priority: high + path: tasks/ep_campaign.md + updated: '2026-10-04' + next: Reconcile current Cortex records and human gates; this campaign map is never issued or bulk scheduled. + evidence: https://github.com/PyAutoLabs/PyAutoMind/blob/8dd1edc7e04c960daae3663513c61395823eddb0/draft/research/graphical_ep/ep_campaign.md +- id: ep_scoping + campaign: graphical-ep + title: Expectation Propagation Scale-Up — Scoping + status: needs-slicing + priority: high + path: tasks/ep_scoping.md + updated: '2026-10-04' + next: Reconcile completed wrapper work and Pulse profiling ownership before selecting a bounded remaining + inference phase. + evidence: https://github.com/PyAutoLabs/PyAutoMind/blob/8dd1edc7e04c960daae3663513c61395823eddb0/draft/research/graphical_ep/ep_scoping.md +- id: graphical_scoping + campaign: graphical-ep + title: Graphical Model Scale-Up — Scoping + status: needs-slicing + priority: high + path: tasks/graphical_scoping.md + updated: '2026-10-04' + next: Reconcile current Cortex scale results and library dependencies; select one bounded inference phase + with a witness. + evidence: https://github.com/PyAutoLabs/PyAutoMind/blob/8dd1edc7e04c960daae3663513c61395823eddb0/draft/research/graphical_ep/graphical_scoping.md +- id: gradient_sampler_validated_likelihoods + campaign: point-source-admission + title: NUTS/HMC trial on validated likelihoods + status: needs-decision + priority: normal + path: tasks/gradient_sampler_validated_likelihoods.md + updated: '2026-10-04' + next: 'An unscoped idea, not permission to submit: reconcile point-source admission with imaging and weak-lensing + candidate targets.' + evidence: https://github.com/PyAutoLabs/PyAutoMind/blob/8dd1edc7e04c960daae3663513c61395823eddb0/ideas.md diff --git a/dashboard.html b/dashboard.html new file mode 100644 index 0000000..473a7ca --- /dev/null +++ b/dashboard.html @@ -0,0 +1,62 @@ +PyAutoInsight dashboard

PyAutoInsight

Question. Infer. Understand.

Check in on all inference work

One prompt, one ongoing chat. Add a campaign focus or idea here, or tell the chat before or afterwards.

Last check-in: not recorded yet. Ledger dates are review dates, not measurement freshness.

Active campaigns

CampaignStatusOpen tasksRecent progressBlockersJob statusNext stepReviewed
HST SLaM base backend parityactive2Project and Cortex ledgers retain the 24 September rerun submissions; published rows require coverage reconciliation.Live RAL status and output/archive availability unverified.unknown — live scheduler not queried; historical ledger states are not current job evidenceReconcile all seeds and stages with current project rows and Cortex before proposing another run.2026-10-04
HST SLaM Delaunay variantactive0Delaunay is a separate target/recipe; pre-speedup runs are archived, reruns recorded on 24 September.Do not merge the Delaunay target with rectangular comparisons; current job status unknown.unknown — live scheduler not queried; historical ledger states are not current job evidenceRetrieve or account for every rerun, failed seed and missing stage; preserve the archived comparison context.2026-10-04
Point-source sampler admissionneeds-decision12 October project record reports five Nautilus seeds; no gradient-sampler admission measurement.Gradient cost share and sampler admission remain unmeasured; Pulse owns likelihood optimisation.unknown — live scheduler not queried; historical ledger states are not current job evidenceAssess a bounded gradient-sampler admission proposal with the human; no new job is authorized.2026-10-04
Graphical and EP inference coordinationneeds-slicing3Mind campaign map and two scoping contracts preserved verbatim; implementation work remains in Mind.Old scoping contains completed phases and decision gates; not a bulk runnable queue.unknown — live scheduler not queried; historical ledger states are not current job evidenceReconcile current Cortex observations and shipped fixes, then select one bounded phase.2026-10-04
Analytic Gaussian ensemble and control calibrationblocked0Cortex records accepted wave-1 baseline and 30 September independent-review caveats.N=25 stays unsubmitted until control/threshold diagnostic is resolved.unknown — live scheduler not queried; historical ledger states are not current job evidenceUse Cortex planned diagnostic on passing, edge and worst seeds; retain the human conclusion there.2026-10-04
N=50 NUTS versus three EP repeatsneeds-decision0Cortex records failed wave 1 (EMFILE/OOM), wave-2 preparation and no submission.Explicit human go; sample entry/export and resource settings must be verified.unknown — live scheduler not queried; historical ledger states are not current job evidencePresent the existing wave-2 proposal with current dependency evidence for decision.2026-10-04
Hierarchical slope recovery at scaleblocked0Cortex retains graphical baseline, post-processing OOM and stale EP hierarchical factor; Mind records later moments fix.Summary/sample retrieval and release/RAL mirror reconciliation outstanding; no reclassification from stale prose.unknown — live scheduler not queried; historical ledger states are not current job evidenceVerify the moments fix release and project mirror; recover missing summaries from existing samples before a proposed rerun.2026-10-04
IC50 EP and graphical scale ladderblocked0Cortex records N=5/10/25 results, failed N=50 EP rung and width-interpretation caveat.Projection fix and mean-field width reporting require reconciliation before scientific comparison.unknown — live scheduler not queried; historical ledger states are not current job evidenceCheck implementation close-outs, then propose a seeded ladder with appropriate output and mean-field diagnostics.2026-10-04

Active tasks

Open means tracked, not necessarily running. Blockers and decisions still apply.

TaskCampaignStatusPriorityNext step
Drop the 1e-3 prior centring for shear / multipole / ell_comps in the benchmarkslam-hst-baseblockednormalVerify the release containing Galaxy#634 and Lens#754 before changing priors; retain zero-centre gradient witness.
Reconcile inference documentation with the current Cortex ledger contractslam-hst-basereadynormalReconcile Now/Runs/Log documentation via the bounded organ implementation PR; do not alter scientific records.
EP campaign — phase map for the 2026 Q3 graphical/EP pushgraphical-epneeds-decisionhighReconcile current Cortex records and human gates; this campaign map is never issued or bulk scheduled.
Expectation Propagation Scale-Up — Scopinggraphical-epneeds-slicinghighReconcile completed wrapper work and Pulse profiling ownership before selecting a bounded remaining inference phase.
Graphical Model Scale-Up — Scopinggraphical-epneeds-slicinghighReconcile current Cortex scale results and library dependencies; select one bounded inference phase with a witness.
NUTS/HMC trial on validated likelihoodspoint-source-admissionneeds-decisionnormalAn unscoped idea, not permission to submit: reconcile point-source admission with imaging and weak-lensing candidate targets.

Completed and superseded tasks remain in the ledger.

Inference evidence

Execution completion is separate from convergence and scientific acceptance. Cortex retains scientific conclusions. Evidence remains in project storage.

1Projects
56Records
0Comparisons
0Cached
0Failed

autolens_inference

Integrity: ok · Freshness: freshness policy unspecified · evidence time unknown · Scientific qualification: 0/56 accepted by producer; remaining assessments shown below

Captured source branch: feature/insight-producer. Revision: 9c362a7a298238d766f61e812c694813e94830d1. Capture time: 2026-10-04T07:45:10Z. Latest attempt: 2026-10-04T07:45:10Z.

Scope: inference. Producer revision: 4ead1799b05b2218a59fca062e3419891c71c82c. Generated: 2026-10-04T07:41:04Z. Source commit timestamp; not a measurement or check-in time

Project dashboard · Ingest receipt

Coverage and limitations

{"excluded": [], "expected": {"reason": "No complete expected-run manifest; unrecorded jobs cannot be enumerated", "runs": null}, "observed": {"archived_records": 50, "execution_complete": 55, "execution_stopped_early": 1, "records": 56, "runs": 15}}
  • No scientific acceptance or convergence inferred from process completion
  • Sample archives remain in project storage; current availability unknown
  • No evidence from the retired inference programme is imported
  • Archived rows belong to this project's September 2026 runs and are historical, not current baselines

Current results

TargetStageSamplerConfigurationSeedExecutionConvergence / acceptanceSampler clock (s)Fit call (s)DiagnosticsSamplesEvidence
simple/source_plane_solved/seed0single searchnautilushpc_a100_jax_cpu_dense_fp640completenot_assessed / not_assessed56.5600078105926565.21035292901797availableunknownresult
simple/source_plane_solved/seed1single searchnautilushpc_a100_jax_cpu_dense_fp641completenot_assessed / not_assessed54.1813416481018162.80253079999238availableunknownresult
simple/source_plane_solved/seed2single searchnautilushpc_a100_jax_cpu_dense_fp642completenot_assessed / not_assessed53.76466560363769562.157571617048234availableunknownresult
simple/source_plane_solved/seed3single searchnautilushpc_a100_jax_cpu_dense_fp643completenot_assessed / not_assessed50.3830430507659958.97957983799279availableunknownresult
simple/source_plane_solved/seed4single searchnautilushpc_a100_jax_cpu_dense_fp644completenot_assessed / not_assessed58.5075080394744967.16032414801884availableunknownresult
simple/source_plane_solved/seed0single searchnautiluslocal_jax_cpu_dense_fp640completenot_assessed / not_assessed60.4314727783203169.44111120000161availableunknownresult

Historical project results

TargetStageSamplerConfigurationSeedExecutionConvergence / acceptanceSampler clock (s)Fit call (s)DiagnosticsSamplesEvidence
hst/slam5_delaunay_1250/seed0pipeline parentunknownhpc_a100_jax_gpu_dense_fp640completenot_assessed / not_assessedunknownunknownmissingunknownresult
hst/slam5_delaunay_1250/seed0source_lp[1]unknownhpc_a100_jax_gpu_dense_fp640completenot_assessed / not_assessed820.3699300289154965.2785516558215availableunknownresult
hst/slam5_delaunay_1250/seed0source_pix[1]unknownhpc_a100_jax_gpu_dense_fp640completenot_assessed / not_assessed2047.430355310442128.72460657591availableunknownresult
hst/slam5_delaunay_1250/seed0source_pix[2]unknownhpc_a100_jax_gpu_dense_fp640completenot_assessed / not_assessed111.54320764541626176.4244288089685availableunknownresult
hst/slam5_delaunay_1250/seed0light[1]unknownhpc_a100_jax_gpu_dense_fp640completenot_assessed / not_assessed406.49094557762146472.91616048291326availableunknownresult
hst/slam5_delaunay_1250/seed0mass_total[1]unknownhpc_a100_jax_gpu_dense_fp640completenot_assessed / not_assessed1383.8430266380311475.3449251349084availableunknownresult
hst/slam5_delaunay_1250/seed1pipeline parentunknownhpc_a100_jax_gpu_dense_fp641completenot_assessed / not_assessedunknownunknownmissingunknownresult
hst/slam5_delaunay_1250/seed1source_lp[1]unknownhpc_a100_jax_gpu_dense_fp641completenot_assessed / not_assessed882.93300962448121023.6260516527109availableunknownresult
hst/slam5_delaunay_1250/seed1source_pix[1]unknownhpc_a100_jax_gpu_dense_fp641completenot_assessed / not_assessed2589.4734685420992668.5737252449617availableunknownresult
hst/slam5_delaunay_1250/seed1source_pix[2]unknownhpc_a100_jax_gpu_dense_fp641completenot_assessed / not_assessed116.28890442848206176.19057126017287availableunknownresult
hst/slam5_delaunay_1250/seed1light[1]unknownhpc_a100_jax_gpu_dense_fp641completenot_assessed / not_assessed457.26988673210144530.5928531419486availableunknownresult
hst/slam5_delaunay_1250/seed1mass_total[1]unknownhpc_a100_jax_gpu_dense_fp641completenot_assessed / not_assessed1048.92924714088441138.162050106097availableunknownresult
hst/slam5_delaunay_1250/seed0pipeline parentunknownhpc_a100_jax_gpu_sparse_fp640completenot_assessed / not_assessedunknownunknownmissingunknownresult
hst/slam5_delaunay_1250/seed0source_lp[1]unknownhpc_a100_jax_gpu_sparse_fp640completenot_assessed / not_assessed749.7595109939575888.2430416657589availableunknownresult
hst/slam5_delaunay_1250/seed0source_pix[1]unknownhpc_a100_jax_gpu_sparse_fp640completenot_assessed / not_assessed6124.6702384948736221.422692308202availableunknownresult
hst/slam5_delaunay_1250/seed0source_pix[2]unknownhpc_a100_jax_gpu_sparse_fp640completenot_assessed / not_assessed112.84573483467102178.6054911534302availableunknownresult
hst/slam5_delaunay_1250/seed0light[1]unknownhpc_a100_jax_gpu_sparse_fp640completenot_assessed / not_assessed353.50146985054016426.6312530930154availableunknownresult
hst/slam5_delaunay_1250/seed0mass_total[1]unknownhpc_a100_jax_gpu_sparse_fp640completenot_assessed / not_assessed1771.12478852272031861.7668606671505availableunknownresult
hst/slam5_delaunay_1250/seed1pipeline parentunknownhpc_a100_jax_gpu_sparse_fp641completenot_assessed / not_assessedunknownunknownmissingunknownresult
hst/slam5_delaunay_1250/seed1source_lp[1]unknownhpc_a100_jax_gpu_sparse_fp641completenot_assessed / not_assessed831.8062770366669973.6843929146416availableunknownresult
hst/slam5_delaunay_1250/seed1source_pix[1]unknownhpc_a100_jax_gpu_sparse_fp641completenot_assessed / not_assessed5258.1412222385415348.343995480798availableunknownresult
hst/slam5_delaunay_1250/seed1source_pix[2]unknownhpc_a100_jax_gpu_sparse_fp641completenot_assessed / not_assessed115.64004683494568182.61417358601466availableunknownresult
hst/slam5_delaunay_1250/seed1light[1]unknownhpc_a100_jax_gpu_sparse_fp641completenot_assessed / not_assessed379.8856680393219453.2077537388541availableunknownresult
hst/slam5_delaunay_1250/seed1mass_total[1]unknownhpc_a100_jax_gpu_sparse_fp641completenot_assessed / not_assessed1900.17717885971071995.9051318671554availableunknownresult
hst/slam5/seed0pipeline parentunknownhpc_a100_jax_gpu_dense_fp640completenot_assessed / not_assessedunknownunknownmissingunknownresult
hst/slam5/seed0source_lp[1]unknownhpc_a100_jax_gpu_dense_fp640completenot_assessed / not_assessed923.29887938499451110.2745820698328availableunknownresult
hst/slam5/seed0source_pix[1]unknownhpc_a100_jax_gpu_dense_fp640completenot_assessed / not_assessed761.86904501914981096.6811571302824availableunknownresult
hst/slam5/seed0source_pix[2]unknownhpc_a100_jax_gpu_dense_fp640completenot_assessed / not_assessed131.4924976825714457.5962234721519availableunknownresult
hst/slam5/seed0light[1]unknownhpc_a100_jax_gpu_dense_fp640completenot_assessed / not_assessed237.3431966304779585.0465070647188availableunknownresult
hst/slam5/seed0mass_total[1]unknownhpc_a100_jax_gpu_dense_fp640completenot_assessed / not_assessed516.6248600482941894.5935196429491availableunknownresult
hst/slam5/seed1pipeline parentunknownhpc_a100_jax_gpu_dense_fp641completenot_assessed / not_assessedunknownunknownmissingunknownresult
hst/slam5/seed1source_lp[1]unknownhpc_a100_jax_gpu_dense_fp641completenot_assessed / not_assessed834.82565093040471005.474559458904availableunknownresult
hst/slam5/seed1source_pix[1]unknownhpc_a100_jax_gpu_dense_fp641completenot_assessed / not_assessed748.73390245437621097.2968472116627availableunknownresult
hst/slam5/seed1source_pix[2]unknownhpc_a100_jax_gpu_dense_fp641completenot_assessed / not_assessed362.2842011451721719.2182216290385availableunknownresult
hst/slam5/seed1light[1]unknownhpc_a100_jax_gpu_dense_fp641completenot_assessed / not_assessed267.35919713974618.7239170190878availableunknownresult
hst/slam5/seed1mass_total[1]unknownhpc_a100_jax_gpu_dense_fp641completenot_assessed / not_assessed650.2288322448731043.753492906224availableunknownresult
hst/slam5/seed0pipeline parentunknownhpc_a100_jax_gpu_dense_fp640stopped_earlynot_assessed / not_assessedunknownunknownmissingunknownresult
hst/slam5/seed0source_lp[1]unknownhpc_a100_jax_gpu_dense_fp640completenot_assessed / not_assessed855.37234878540041041.4455565372482availableunknownresult
hst/slam5/seed0pipeline parentunknownhpc_a100_jax_gpu_sparse_fp640completenot_assessed / not_assessedunknownunknownmissingunknownresult
hst/slam5/seed0source_lp[1]unknownhpc_a100_jax_gpu_sparse_fp640completenot_assessed / not_assessed808.1857154369354960.966922598891availableunknownresult
hst/slam5/seed0source_pix[1]unknownhpc_a100_jax_gpu_sparse_fp640completenot_assessed / not_assessed1930.7643861770632279.775213385001availableunknownresult
hst/slam5/seed0source_pix[2]unknownhpc_a100_jax_gpu_sparse_fp640completenot_assessed / not_assessed178.61591696739197535.9517216398381availableunknownresult
hst/slam5/seed0light[1]unknownhpc_a100_jax_gpu_sparse_fp640completenot_assessed / not_assessed267.51912546157837621.3747416636907availableunknownresult
hst/slam5/seed0mass_total[1]unknownhpc_a100_jax_gpu_sparse_fp640completenot_assessed / not_assessed800.2792305946351168.8711764230393availableunknownresult
hst/slam5/seed1pipeline parentunknownhpc_a100_jax_gpu_sparse_fp641completenot_assessed / not_assessedunknownunknownmissingunknownresult
hst/slam5/seed1source_lp[1]unknownhpc_a100_jax_gpu_sparse_fp641completenot_assessed / not_assessed798.179591178894944.8778721541166availableunknownresult
hst/slam5/seed1source_pix[1]unknownhpc_a100_jax_gpu_sparse_fp641completenot_assessed / not_assessed1904.311167716982245.7180267861113availableunknownresult
hst/slam5/seed1source_pix[2]unknownhpc_a100_jax_gpu_sparse_fp641completenot_assessed / not_assessed187.7734875679016523.5104860081337availableunknownresult
hst/slam5/seed1light[1]unknownhpc_a100_jax_gpu_sparse_fp641completenot_assessed / not_assessed253.89728569984436606.7501009167172availableunknownresult
hst/slam5/seed1mass_total[1]unknownhpc_a100_jax_gpu_sparse_fp641completenot_assessed / not_assessed868.11893606185911255.2648063716479availableunknownresult

Comparisons and parity groups

Targets, seeds and stages are retained. No best-seed selection, cross-target ranking or automatic convergence threshold. Declared groups do not themselves certify parity.

No certified comparison pairs supplied by this producer.

Diagnostics, timing definitions and provenance

results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json
parent_run_id
null
stage
null
target
"hst/slam5_delaunay_1250/seed0"
dataset
{"class": "imaging", "id": null, "instrument": "hst"}
model
null
pipeline
"slam"
sampler
null
configuration
{"config_name": "hpc_a100_jax_gpu_dense_fp64", "cores": 8, "image_mesh": "hilbert", "image_mesh_edge_points": 30, "image_mesh_weight_floor": 0.01, "image_mesh_weight_power": 3.5, "inversion": "dense", "memo": "off", "mesh": "delaunay", "mesh_areas_factor": 0.5, "mesh_pixels": 1250, "mesh_shape": null, "mesh_zeroed_pixels": 30, "rect_mesh": "bilinear", "regularization": "adapt_split", "stages_requested": null, "test_mode": false, "use_jax": true, "variant": "delaunay_1250"}
backend
"jax_gpu"
hardware
{"cores": 8, "device": {"backend": "gpu", "cpu_count": 124, "device": "cuda:0", "nvidia_smi": "NVIDIA A100 80GB PCIe, 37549 MiB, 81920 MiB", "omp_num_threads": null, "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false"}}
precision
"fp64"
seed
0
dependency_revisions
{}
library_version
"2026.8.17.1"
execution
{"completed": null, "reason": null, "status": "complete"}
scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
timings
{"compile_s": null, "definitions": {"compile_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", "sampling_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", "setup_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", "total_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed"}, "sampling_s": null, "setup_s": null, "total_s": null}
diagnostics
{"status": "missing", "values": {}}
comparison
{"declared_target": "hst/slam5_delaunay_1250/seed0", "group": null, "note": "Declared target is not a verified comparison group; no ranking"}
samples
{"access": "project-storage-not-published", "availability": "unknown", "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", "path": null}
measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "execution.completed": "No pipeline-wide completion marker is recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result", "timings": "Pipeline aggregate not measured; stage clocks may overlap and are never summed"}
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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null
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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measured_at
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unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
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results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "execution.completed": "No pipeline-wide completion marker is recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result", "timings": "Pipeline aggregate not measured; stage clocks may overlap and are never summed"}
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null
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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comparison
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measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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null
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{}
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{"completed": true, "reason": null, "status": "complete"}
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{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
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diagnostics
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comparison
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measured_at
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json
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null
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{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
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measured_at
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "execution.completed": "No pipeline-wide completion marker is recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result", "timings": "Pipeline aggregate not measured; stage clocks may overlap and are never summed"}
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{"cores": 8, "device": {"backend": "gpu", "cpu_count": 124, "device": "cuda:0", "nvidia_smi": "NVIDIA A100 80GB PCIe, 4813 MiB, 81920 MiB", "omp_num_threads": null, "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false"}}
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timings
{"compile_s": 10.573504409752786, "definitions": {"compile_s": "Separate likelihood warm-up probe; not added to total_s", "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", "setup_s": "Not measured separately; unknown, not total minus sampling", "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup"}, "sampling_s": 749.7595109939575, "setup_s": null, "total_s": 888.2430416657589}
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measured_at
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-1ad2b0be53ac3cb8
parent_run_id
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comparison
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measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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null
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{}
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{"completed": true, "reason": null, "status": "complete"}
scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
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comparison
{"declared_target": "hst/slam5_delaunay_1250/seed0", "group": null, "note": "Declared target is not a verified comparison group; no ranking"}
samples
{"access": "project-storage-not-published", "availability": "unknown", "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", "path": null}
measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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stage
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dataset
{"class": "imaging", "id": null, "instrument": "hst"}
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null
pipeline
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null
configuration
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{"cores": 8, "device": {"backend": "gpu", "cpu_count": 124, "device": "cuda:0", "nvidia_smi": "NVIDIA A100 80GB PCIe, 4813 MiB, 81920 MiB", "omp_num_threads": null, "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false"}}
precision
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seed
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{}
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{"completed": true, "reason": null, "status": "complete"}
scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
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diagnostics
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comparison
{"declared_target": "hst/slam5_delaunay_1250/seed0", "group": null, "note": "Declared target is not a verified comparison group; no ranking"}
samples
{"access": "project-storage-not-published", "availability": "unknown", "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", "path": null}
measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-03dd03936a025726
parent_run_id
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"hst/slam5_delaunay_1250/seed0"
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{"class": "imaging", "id": null, "instrument": "hst"}
model
null
pipeline
"slam"
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null
configuration
{"config_name": "hpc_a100_jax_gpu_sparse_fp64", "cores": 8, "free_parameters": 8, "image_mesh": "hilbert", "image_mesh_edge_points": 30, "image_mesh_weight_floor": 0.01, "image_mesh_weight_power": 3.5, "inversion": "sparse", "memo": "off", "mesh": "delaunay", "mesh_areas_factor": 0.5, "mesh_pixels": 1250, "mesh_shape": null, "mesh_zeroed_pixels": 30, "n_batch": 20, "n_live": 150, "rect_mesh": "bilinear", "regularization": "adapt_split", "resumed": false, "stages_requested": null, "test_mode": false, "use_jax": true, "variant": "delaunay_1250"}
backend
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{"cores": 8, "device": {"backend": "gpu", "cpu_count": 124, "device": "cuda:0", "nvidia_smi": "NVIDIA A100 80GB PCIe, 4813 MiB, 81920 MiB", "omp_num_threads": null, "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false"}}
precision
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seed
0
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{}
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execution
{"completed": true, "reason": null, "status": "complete"}
scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
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{"compile_s": 3.2871260661631823, "definitions": {"compile_s": "Separate likelihood warm-up probe; not added to total_s", "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", "setup_s": "Not measured separately; unknown, not total minus sampling", "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup"}, "sampling_s": 1771.1247885227203, "setup_s": null, "total_s": 1861.7668606671505}
diagnostics
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comparison
{"declared_target": "hst/slam5_delaunay_1250/seed0", "group": null, "note": "Declared target is not a verified comparison group; no ranking"}
samples
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measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json
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{"class": "imaging", "id": null, "instrument": "hst"}
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null
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"slam"
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null
configuration
{"config_name": "hpc_a100_jax_gpu_sparse_fp64", "cores": 8, "image_mesh": "hilbert", "image_mesh_edge_points": 30, "image_mesh_weight_floor": 0.01, "image_mesh_weight_power": 3.5, "inversion": "sparse", "memo": "off", "mesh": "delaunay", "mesh_areas_factor": 0.5, "mesh_pixels": 1250, "mesh_shape": null, "mesh_zeroed_pixels": 30, "rect_mesh": "bilinear", "regularization": "adapt_split", "stages_requested": null, "test_mode": false, "use_jax": true, "variant": "delaunay_1250"}
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{"cores": 8, "device": {"backend": "gpu", "cpu_count": 124, "device": "cuda:0", "nvidia_smi": "NVIDIA A100 80GB PCIe, 4811 MiB, 81920 MiB", "omp_num_threads": null, "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false"}}
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{}
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execution
{"completed": null, "reason": null, "status": "complete"}
scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
timings
{"compile_s": null, "definitions": {"compile_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", "sampling_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", "setup_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", "total_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed"}, "sampling_s": null, "setup_s": null, "total_s": null}
diagnostics
{"status": "missing", "values": {}}
comparison
{"declared_target": "hst/slam5_delaunay_1250/seed1", "group": null, "note": "Declared target is not a verified comparison group; no ranking"}
samples
{"access": "project-storage-not-published", "availability": "unknown", "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", "path": null}
measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "execution.completed": "No pipeline-wide completion marker is recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result", "timings": "Pipeline aggregate not measured; stage clocks may overlap and are never summed"}
results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-9b14006556621cdb
parent_run_id
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{"class": "imaging", "id": null, "instrument": "hst"}
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null
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"slam"
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{"config_name": "hpc_a100_jax_gpu_sparse_fp64", "cores": 8, "free_parameters": 17, "image_mesh": "hilbert", "image_mesh_edge_points": 30, "image_mesh_weight_floor": 0.01, "image_mesh_weight_power": 3.5, "inversion": "sparse", "memo": "off", "mesh": "delaunay", "mesh_areas_factor": 0.5, "mesh_pixels": 1250, "mesh_shape": null, "mesh_zeroed_pixels": 30, "n_batch": 50, "n_live": 200, "rect_mesh": "bilinear", "regularization": "adapt_split", "resumed": false, "stages_requested": null, "test_mode": false, "use_jax": true, "variant": "delaunay_1250"}
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{"cores": 8, "device": {"backend": "gpu", "cpu_count": 124, "device": "cuda:0", "nvidia_smi": "NVIDIA A100 80GB PCIe, 4811 MiB, 81920 MiB", "omp_num_threads": null, "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false"}}
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{}
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{"completed": true, "reason": null, "status": "complete"}
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{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
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measured_at
null
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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comparison
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samples
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measured_at
null
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json
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{}
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{"completed": true, "reason": null, "status": "complete"}
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{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
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measured_at
null
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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null
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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comparison
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "execution.completed": "No pipeline-wide completion marker is recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result", "timings": "Pipeline aggregate not measured; stage clocks may overlap and are never summed"}
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{"completed": true, "reason": null, "status": "complete"}
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unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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timings
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unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
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comparison
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measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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null
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seed
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{}
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{"completed": true, "reason": null, "status": "complete"}
scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
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measured_at
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unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64__rate_probe_342695/stages_seed0.json
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{}
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{"completed": null, "reason": null, "status": "stopped_early"}
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measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "execution.completed": "No pipeline-wide completion marker is recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result", "timings": "Pipeline aggregate not measured; stage clocks may overlap and are never summed"}
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{"class": "imaging", "id": null, "instrument": "hst"}
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{"cores": 4, "device": {"backend": "gpu", "cpu_count": 124, "device": "cuda:0", "nvidia_smi": "NVIDIA A100 80GB PCIe, 2621 MiB, 81920 MiB", "omp_num_threads": null, "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false"}}
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"fp64"
seed
0
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{}
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{"completed": true, "reason": null, "status": "complete"}
scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
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unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json
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null
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{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
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measured_at
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results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-9b14006556621cdb
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{"class": "imaging", "id": null, "instrument": "hst"}
model
null
pipeline
"slam"
sampler
null
configuration
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backend
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hardware
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precision
"fp64"
seed
0
dependency_revisions
{}
library_version
"2026.8.17.1"
execution
{"completed": true, "reason": null, "status": "complete"}
scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
timings
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diagnostics
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comparison
{"declared_target": "hst/slam5/seed0", "group": null, "note": "Declared target is not a verified comparison group; no ranking"}
samples
{"access": "project-storage-not-published", "availability": "unknown", "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", "path": null}
measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-1ad2b0be53ac3cb8
parent_run_id
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stage
"source_pix[1]"
target
"hst/slam5/seed0"
dataset
{"class": "imaging", "id": null, "instrument": "hst"}
model
null
pipeline
"slam"
sampler
null
configuration
{"config_name": "hpc_a100_jax_gpu_sparse_fp64", "cores": 4, "free_parameters": 10, "inversion": "sparse", "memo": "off", "mesh_shape": [28, 28], "n_batch": 20, "n_live": 150, "rect_mesh": "bilinear", "resumed": false, "stages_requested": null, "test_mode": false, "use_jax": true}
backend
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hardware
{"cores": 4, "device": {"backend": "gpu", "cpu_count": 124, "device": "cuda:0", "nvidia_smi": "NVIDIA A100 80GB PCIe, 8903 MiB, 81920 MiB", "omp_num_threads": null, "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false"}}
precision
"fp64"
seed
0
dependency_revisions
{}
library_version
"2026.8.17.1"
execution
{"completed": true, "reason": null, "status": "complete"}
scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
timings
{"compile_s": 20.21704276604578, "definitions": {"compile_s": "Separate likelihood warm-up probe; not added to total_s", "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", "setup_s": "Not measured separately; unknown, not total minus sampling", "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup"}, "sampling_s": 1930.764386177063, "setup_s": null, "total_s": 2279.775213385001}
diagnostics
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comparison
{"declared_target": "hst/slam5/seed0", "group": null, "note": "Declared target is not a verified comparison group; no ranking"}
samples
{"access": "project-storage-not-published", "availability": "unknown", "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", "path": null}
measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-122202734335fe14
parent_run_id
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stage
"source_pix[2]"
target
"hst/slam5/seed0"
dataset
{"class": "imaging", "id": null, "instrument": "hst"}
model
null
pipeline
"slam"
sampler
null
configuration
{"config_name": "hpc_a100_jax_gpu_sparse_fp64", "cores": 4, "free_parameters": 5, "inversion": "sparse", "memo": "off", "mesh_shape": [28, 28], "n_batch": 20, "n_live": 75, "rect_mesh": "bilinear", "resumed": false, "stages_requested": null, "test_mode": false, "use_jax": true}
backend
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hardware
{"cores": 4, "device": {"backend": "gpu", "cpu_count": 124, "device": "cuda:0", "nvidia_smi": "NVIDIA A100 80GB PCIe, 8903 MiB, 81920 MiB", "omp_num_threads": null, "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false"}}
precision
"fp64"
seed
0
dependency_revisions
{}
library_version
"2026.8.17.1"
execution
{"completed": true, "reason": null, "status": "complete"}
scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
timings
{"compile_s": 9.113326458726078, "definitions": {"compile_s": "Separate likelihood warm-up probe; not added to total_s", "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", "setup_s": "Not measured separately; unknown, not total minus sampling", "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup"}, "sampling_s": 178.61591696739197, "setup_s": null, "total_s": 535.9517216398381}
diagnostics
{"status": "available", "values": {"inversion_applies": true, "likelihood_evals": 3260, "log_evidence": 31615.926126596125, "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", "max_log_likelihood": 31633.55600511511, "positions_info_present": true, "posterior": {"inner_coefficient": {"lower_1": 5.026005634838336e-06, "median": 0.00016106495551258316, "sigma": 0.00197283679409024, "upper_1": 0.003950699593815318}, "outer_coefficient": {"lower_1": 34.136668837510854, "median": 38.10496820415385, "sigma": 3.1530491777730703, "upper_1": 40.442767193056994}, "signal_scale": {"lower_1": 0.07495627913991262, "median": 0.0807987228191902, "sigma": 0.008216000056908576, "upper_1": 0.09138827925372978}, "weight_floor": {"lower_1": 6.398493879092095e-05, "median": 0.0012804808023735756, "sigma": 0.012466578435110561, "upper_1": 0.024997141809012042}, "weight_power": {"lower_1": 4.1082101473022785, "median": 4.191938733518657, "sigma": 0.06508183735499928, "upper_1": 4.238373822012277}}, "truth_delta_sigma": {}}}
comparison
{"declared_target": "hst/slam5/seed0", "group": null, "note": "Declared target is not a verified comparison group; no ranking"}
samples
{"access": "project-storage-not-published", "availability": "unknown", "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", "path": null}
measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-7f695c3cdb0cdedb
parent_run_id
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stage
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target
"hst/slam5/seed0"
dataset
{"class": "imaging", "id": null, "instrument": "hst"}
model
null
pipeline
"slam"
sampler
null
configuration
{"config_name": "hpc_a100_jax_gpu_sparse_fp64", "cores": 4, "free_parameters": 6, "inversion": "sparse", "memo": "off", "mesh_shape": [28, 28], "n_batch": 20, "n_live": 150, "rect_mesh": "bilinear", "resumed": false, "stages_requested": null, "test_mode": false, "use_jax": true}
backend
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hardware
{"cores": 4, "device": {"backend": "gpu", "cpu_count": 124, "device": "cuda:0", "nvidia_smi": "NVIDIA A100 80GB PCIe, 8903 MiB, 81920 MiB", "omp_num_threads": null, "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false"}}
precision
"fp64"
seed
0
dependency_revisions
{}
library_version
"2026.8.17.1"
execution
{"completed": true, "reason": null, "status": "complete"}
scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
timings
{"compile_s": 3.147912166081369, "definitions": {"compile_s": "Separate likelihood warm-up probe; not added to total_s", "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", "setup_s": "Not measured separately; unknown, not total minus sampling", "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup"}, "sampling_s": 267.51912546157837, "setup_s": null, "total_s": 621.3747416636907}
diagnostics
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comparison
{"declared_target": "hst/slam5/seed0", "group": null, "note": "Declared target is not a verified comparison group; no ranking"}
samples
{"access": "project-storage-not-published", "availability": "unknown", "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", "path": null}
measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-03dd03936a025726
parent_run_id
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stage
"mass_total[1]"
target
"hst/slam5/seed0"
dataset
{"class": "imaging", "id": null, "instrument": "hst"}
model
null
pipeline
"slam"
sampler
null
configuration
{"config_name": "hpc_a100_jax_gpu_sparse_fp64", "cores": 4, "free_parameters": 8, "inversion": "sparse", "memo": "off", "mesh_shape": [28, 28], "n_batch": 20, "n_live": 150, "rect_mesh": "bilinear", "resumed": false, "stages_requested": null, "test_mode": false, "use_jax": true}
backend
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hardware
{"cores": 4, "device": {"backend": "gpu", "cpu_count": 124, "device": "cuda:0", "nvidia_smi": "NVIDIA A100 80GB PCIe, 8903 MiB, 81920 MiB", "omp_num_threads": null, "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false"}}
precision
"fp64"
seed
0
dependency_revisions
{}
library_version
"2026.8.17.1"
execution
{"completed": true, "reason": null, "status": "complete"}
scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
timings
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diagnostics
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results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json
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{"completed": true, "reason": null, "status": "complete"}
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{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
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null
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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null
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{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
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scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
timings
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comparison
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measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-03dd03936a025726
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null
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null
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{"config_name": "hpc_a100_jax_gpu_sparse_fp64", "cores": 4, "free_parameters": 8, "inversion": "sparse", "memo": "off", "mesh_shape": [28, 28], "n_batch": 20, "n_live": 150, "rect_mesh": "bilinear", "resumed": false, "stages_requested": null, "test_mode": false, "use_jax": true}
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1
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{}
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{"completed": true, "reason": null, "status": "complete"}
scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
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comparison
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samples
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measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "dependency_revisions": "Dependency revisions not recorded", "measured_at": "Not recorded in source result", "model": "Not recorded in source result", "sampler": "Not recorded in source result"}
results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed0.json
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null
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null
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samples
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measured_at
null
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{"dataset.id": "Dataset content identity not recorded", "measured_at": "Not recorded in source result"}
results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed1.json
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null
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null
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samples
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measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "measured_at": "Not recorded in source result"}
results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed2.json
parent_run_id
null
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null
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pipeline
null
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seed
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{"autoarray": "9428eca2", "autofit": "404b3e5f7", "autogalaxy": "c9609825", "autolens": "21b520be4", "autonerves": "bf10410"}
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diagnostics
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comparison
{"declared_target": "simple/source_plane_solved/seed2", "group": null, "note": "Declared target is not a verified comparison group; no ranking"}
samples
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measured_at
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unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "measured_at": "Not recorded in source result"}
results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed3.json
parent_run_id
null
stage
null
target
"simple/source_plane_solved/seed3"
dataset
{"class": "point_source", "id": null, "instrument": "simple"}
model
"L5: Isothermal (5 free) + PointSolved source"
pipeline
null
sampler
"nautilus"
configuration
{"config_name": "hpc_a100_jax_cpu_dense_fp64", "cores": 8, "fit_positions_cls": "FitPositionsSourceSolved", "free_parameters": 5, "inversion": "dense", "n_batch": 50, "n_live": 100, "priors": {"einstein_radius": "U(0.5, 4.0)", "others": "library-default Isothermal priors"}, "resumed": false, "test_mode": false, "use_jax": true, "variant": "source_plane_solved"}
backend
"jax_cpu"
hardware
{"cores": 8, "device": {"backend": "cpu", "cpu_count": 124, "device": "cpu:0", "omp_num_threads": null, "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false"}, "host": "euclid-ral-gpu-2", "slurm_array_job_id": "367140", "slurm_array_task_id": "3", "slurm_job_id": "367446"}
precision
"fp64"
seed
3
dependency_revisions
{"autoarray": "9428eca2", "autofit": "404b3e5f7", "autogalaxy": "c9609825", "autolens": "21b520be4", "autonerves": "bf10410"}
library_version
"2026.8.17.1"
execution
{"completed": true, "reason": null, "status": "complete"}
scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
timings
{"compile_s": 1.9380113480146974, "definitions": {"compile_s": "Separate likelihood warm-up probe; not added to total_s", "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", "setup_s": "Not measured separately; unknown, not total minus sampling", "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup"}, "sampling_s": 50.38304305076599, "setup_s": null, "total_s": 58.97957983799279}
diagnostics
{"status": "available", "values": {"inversion_applies": false, "likelihood_evals": 4700, "likelihood_share": 0.0004284008831123949, "log_evidence": -7.1422108149915955, "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", "log_likelihood_at_truth": 15.902398430038737, "max_log_likelihood": 16.442962159403347, "per_call_s": 4.592370241880417e-06, "positions_info_present": false, "posterior": {"centre_0": {"lower_1": -0.04391072851853559, "median": -0.014527938015443267, "sigma": 0.02547875767253761, "upper_1": 0.0070467868265396295}, "centre_1": {"lower_1": -0.043816453308936715, "median": -0.016769753132973745, "sigma": 0.02505543764174138, "upper_1": 0.006294421974546041}, "einstein_radius": {"lower_1": 1.5951467730554156, "median": 1.6048231816592944, "sigma": 0.011000225562784238, "upper_1": 1.617147224180984}, "ell_comps_0": {"lower_1": 0.04876829937658942, "median": 0.06453071192811381, "sigma": 0.01728396333033107, "upper_1": 0.08333622603725156}, "ell_comps_1": {"lower_1": -0.00020965181380705589, "median": 2.039972265406487e-05, "sigma": 0.0002217830213759653, "upper_1": 0.0002339142289448747}}, "timing": {"batched": {"median_s": 0.00022961851209402084, "min_s": 0.00017524900613352656, "n": 200, "p16_s": 0.00020891905296593904, "p84_s": 0.00025858799926936626, "warmup": 10}, "compile_s": 1.9380113480146974, "compile_single_s": 1.3268661400070414, "single": {"median_s": 0.0002172240347135812, "min_s": 0.00016806903295218945, "n": 200, "p16_s": 0.00020077801309525967, "p84_s": 0.00023928796872496605, "warmup": 10}, "vector": [0.0, 0.0, 0.0, 0.0, 2.25]}, "truth_delta_sigma": {"centre_0": -0.5701980529098665, "centre_1": -0.6693059356119964, "einstein_radius": 0.4384620689608398, "ell_comps_0": 0.6884493303607047, "ell_comps_1": 0.09198054263802348}, "truths": {"centre_0": 0.0, "centre_1": 0.0, "einstein_radius": 1.6, "ell_comps_0": 0.05263157894736841, "ell_comps_1": 3.2227547345982974e-18}}}
comparison
{"declared_target": "simple/source_plane_solved/seed3", "group": null, "note": "Declared target is not a verified comparison group; no ranking"}
samples
{"access": "project-storage-not-published", "availability": "unknown", "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", "path": "output/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/seed_3/nautilus/bfcfe9793f8b2cfa915170d57974952f"}
measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "measured_at": "Not recorded in source result"}
results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed4.json
parent_run_id
null
stage
null
target
"simple/source_plane_solved/seed4"
dataset
{"class": "point_source", "id": null, "instrument": "simple"}
model
"L5: Isothermal (5 free) + PointSolved source"
pipeline
null
sampler
"nautilus"
configuration
{"config_name": "hpc_a100_jax_cpu_dense_fp64", "cores": 8, "fit_positions_cls": "FitPositionsSourceSolved", "free_parameters": 5, "inversion": "dense", "n_batch": 50, "n_live": 100, "priors": {"einstein_radius": "U(0.5, 4.0)", "others": "library-default Isothermal priors"}, "resumed": false, "test_mode": false, "use_jax": true, "variant": "source_plane_solved"}
backend
"jax_cpu"
hardware
{"cores": 8, "device": {"backend": "cpu", "cpu_count": 124, "device": "cpu:0", "omp_num_threads": null, "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false"}, "host": "euclid-ral-gpu-2", "slurm_array_job_id": "367140", "slurm_array_task_id": "4", "slurm_job_id": "367140"}
precision
"fp64"
seed
4
dependency_revisions
{"autoarray": "9428eca2", "autofit": "404b3e5f7", "autogalaxy": "c9609825", "autolens": "21b520be4", "autonerves": "bf10410"}
library_version
"2026.8.17.1"
execution
{"completed": true, "reason": null, "status": "complete"}
scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
timings
{"compile_s": 1.940513722016476, "definitions": {"compile_s": "Separate likelihood warm-up probe; not added to total_s", "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", "setup_s": "Not measured separately; unknown, not total minus sampling", "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup"}, "sampling_s": 58.50750803947449, "setup_s": null, "total_s": 67.16032414801884}
diagnostics
{"status": "available", "values": {"inversion_applies": false, "likelihood_evals": 4750, "likelihood_share": 0.0004068701458214967, "log_evidence": -7.171363991071475, "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", "log_likelihood_at_truth": 15.902398430038737, "max_log_likelihood": 16.46678997877629, "per_call_s": 5.011570174247026e-06, "positions_info_present": false, "posterior": {"centre_0": {"lower_1": -0.04204305304460868, "median": -0.015202703809942616, "sigma": 0.025313908068376127, "upper_1": 0.008584763092143573}, "centre_1": {"lower_1": -0.04385515716199128, "median": -0.015818560715543373, "sigma": 0.02594592273742021, "upper_1": 0.008036688312849143}, "einstein_radius": {"lower_1": 1.5948994050053367, "median": 1.6047485261323444, "sigma": 0.010872921797065671, "upper_1": 1.616645248599468}, "ell_comps_0": {"lower_1": 0.04857850833216588, "median": 0.06434333621231575, "sigma": 0.017004965660044506, "upper_1": 0.08258843965225489}, "ell_comps_1": {"lower_1": -0.0001812031602880165, "median": 2.3331858448681895e-05, "sigma": 0.00022295095800914236, "upper_1": 0.00026469875573026825}}, "timing": {"batched": {"median_s": 0.0002505785087123513, "min_s": 0.00020097801461815834, "n": 200, "p16_s": 0.00023126800078898668, "p84_s": 0.0002849379670806229, "warmup": 10}, "compile_s": 1.940513722016476, "compile_single_s": 1.3289996769744903, "single": {"median_s": 0.0002128235064446926, "min_s": 0.00017350801499560475, "n": 200, "p16_s": 0.0001968390424735844, "p84_s": 0.00023722898913547397, "warmup": 10}, "vector": [0.0, 0.0, 0.0, 0.0, 2.25]}, "truth_delta_sigma": {"centre_0": -0.6005672363539504, "centre_1": -0.60967423959562, "einstein_radius": 0.4367295397660122, "ell_comps_0": 0.6887257227731847, "ell_comps_1": 0.10465018252005862}, "truths": {"centre_0": 0.0, "centre_1": 0.0, "einstein_radius": 1.6, "ell_comps_0": 0.05263157894736841, "ell_comps_1": 3.2227547345982974e-18}}}
comparison
{"declared_target": "simple/source_plane_solved/seed4", "group": null, "note": "Declared target is not a verified comparison group; no ranking"}
samples
{"access": "project-storage-not-published", "availability": "unknown", "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", "path": "output/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/seed_4/nautilus/a7d8d84b055356df0f0dbb3151d46885"}
measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "measured_at": "Not recorded in source result"}
results/searches/point_source/nautilus/simple/source_plane_solved/local_jax_cpu_dense_fp64/search_seed0.json
parent_run_id
null
stage
null
target
"simple/source_plane_solved/seed0"
dataset
{"class": "point_source", "id": null, "instrument": "simple"}
model
"L5: Isothermal (5 free) + PointSolved source"
pipeline
null
sampler
"nautilus"
configuration
{"config_name": "local_jax_cpu_dense_fp64", "cores": 8, "fit_positions_cls": "FitPositionsSourceSolved", "free_parameters": 5, "inversion": "dense", "n_batch": 50, "n_live": 100, "priors": {"einstein_radius": "U(0.5, 4.0)", "others": "library-default Isothermal priors"}, "resumed": false, "test_mode": false, "use_jax": true, "variant": "source_plane_solved"}
backend
"jax_cpu"
hardware
{"cores": 8, "device": {"backend": "cpu", "cpu_count": 8, "device": "cpu:0", "omp_num_threads": "8", "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false"}, "host": "DESKTOP-H143S82", "slurm_array_job_id": null, "slurm_array_task_id": null, "slurm_job_id": null}
precision
"fp64"
seed
0
dependency_revisions
{"autoarray": "9428eca2", "autofit": "404b3e5f7", "autogalaxy": "c9609825", "autolens": "21b520be4", "autonerves": "0747524"}
library_version
"2026.8.17.1"
execution
{"completed": true, "reason": null, "status": "complete"}
scientific
{"acceptance": "not_assessed", "convergence": "not_assessed", "reason": "Exporter does not make scientific rulings; consult Cortex"}
timings
{"compile_s": 2.3335995999987063, "definitions": {"compile_s": "Separate likelihood warm-up probe; not added to total_s", "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", "setup_s": "Not measured separately; unknown, not total minus sampling", "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup"}, "sampling_s": 60.43147277832031, "setup_s": null, "total_s": 69.44111120000161}
diagnostics
{"status": "available", "values": {"inversion_applies": false, "likelihood_evals": 4850, "likelihood_share": 0.0005765604992410397, "log_evidence": -7.15393737152383, "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", "log_likelihood_at_truth": 15.902398430038737, "max_log_likelihood": 16.48568552789147, "per_call_s": 7.1840000236988995e-06, "positions_info_present": false, "posterior": {"centre_0": {"lower_1": -0.042168162458740985, "median": -0.01745342426499831, "sigma": 0.025797484788372165, "upper_1": 0.009426807118003344}, "centre_1": {"lower_1": -0.04336377661022215, "median": -0.018067299498348146, "sigma": 0.025791414665263825, "upper_1": 0.008219052720305502}, "einstein_radius": {"lower_1": 1.5945063508229387, "median": 1.6056260743312727, "sigma": 0.01115243788494824, "upper_1": 1.6168112265928352}, "ell_comps_0": {"lower_1": 0.047688746799381704, "median": 0.06559594956905983, "sigma": 0.01762818058418965, "upper_1": 0.082945107967761}, "ell_comps_1": {"lower_1": -0.00019838121507798325, "median": 2.4804878302780604e-05, "sigma": 0.00022114345613303174, "upper_1": 0.00024390569718808023}}, "timing": {"batched": {"median_s": 0.000359200001184945, "min_s": 0.0003099999994446989, "n": 200, "p16_s": 0.00033690000054775737, "p84_s": 0.000389000000723172, "warmup": 10}, "compile_s": 2.3335995999987063, "compile_single_s": 1.8172457000000577, "single": {"median_s": 0.0002923499996541068, "min_s": 0.0002471000007062685, "n": 200, "p16_s": 0.0002716999988479074, "p84_s": 0.0003206000001227949, "warmup": 10}, "vector": [0.0, 0.0, 0.0, 0.0, 2.25]}, "truth_delta_sigma": {"centre_0": -0.676555269173574, "centre_1": -0.7005160334489675, "einstein_radius": 0.5044703578995735, "ell_comps_0": 0.7354344119505386, "ell_comps_1": 0.11216645853565607}, "truths": {"centre_0": 0.0, "centre_1": 0.0, "einstein_radius": 1.6, "ell_comps_0": 0.05263157894736841, "ell_comps_1": 3.2227547345982974e-18}}}
comparison
{"declared_target": "simple/source_plane_solved/seed0", "group": null, "note": "Declared target is not a verified comparison group; no ranking"}
samples
{"access": "project-storage-not-published", "availability": "unknown", "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", "path": "output/searches/point_source/nautilus/simple/source_plane_solved/local_jax_cpu_dense_fp64/seed_0/nautilus/b39f17cc6d57dba03e090e4d9682ce2b"}
measured_at
null
unknown_reasons
{"dataset.id": "Dataset content identity not recorded", "measured_at": "Not recorded in source result"}
diff --git a/dashboard.md b/dashboard.md new file mode 100644 index 0000000..17a60c1 --- /dev/null +++ b/dashboard.md @@ -0,0 +1,9636 @@ +# PyAutoInsight — inference control room + + + +| Where | Count | +|---|---:| +| [Projects](#evidence) | 1 | +| [Records](#evidence) | 56 | +| [Comparisons](#evidence) | 0 | +| [Cached](#evidence) | 0 | +| [Failed](#evidence) | 0 | + + +## Check in on all inference work + +Copy this into one chat. Add a campaign focus or idea before or after it, or leave it unchanged. + +```text +Use PyAutoInsight as the home for all inference work in this chat. Read PyAutoInsight/AGENTS.md and CHECKIN.md, then campaigns.yaml, the linked tasks and current project campaign ledgers. Check every open campaign and task for updates since the last check-in: results, jobs, PRs, releases, blockers and the next bounded step. Distinguish verified updates from stale or unavailable evidence; do not infer success from missing data. Give me one concise campaign-by-campaign summary and a proposed priority order. Apply any campaign direction or ideas I supply before or after this prompt; otherwise cover everything. Keep the overall check-in even when I focus one campaign. Update the Insight ledger with verified facts and dated source links, regenerate the board, and use the repository workflow to persist changes. Use Cortex for scientific observations and human conclusions, and the existing Brain samplers faculty for optional advice and project drivers for execution. Do not launch new compute, change defaults or baselines, or bypass a human/release gate merely to check in. Continue managing subsequent inference requests in this chat. Optional direction: [leave blank, name a campaign, or suggest an idea]. +``` + +Last check-in: not recorded yet. Ledger dates are review dates, not measurement freshness. + +## Active campaigns + +| Campaign | Status | Open tasks | Recent progress | Blockers | Job status | Next step | Reviewed | +|---|---|---:|---|---|---|---|---| +| [HST SLaM base backend parity](https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/autolens_inference.md) | active | 2 | Project and Cortex ledgers retain the 24 September rerun submissions; published rows require coverage reconciliation. | Live RAL status and output/archive availability unverified. | unknown — live scheduler not queried; historical ledger states are not current job evidence | Reconcile all seeds and stages with current project rows and Cortex before proposing another run. | 2026-10-04 | +| [HST SLaM Delaunay variant](https://github.com/PyAutoLabs/autolens_inference/blob/146a84346eaa51eda158634cae520d677f3e6213/wiki/project/state.md) | active | 0 | Delaunay is a separate target/recipe; pre-speedup runs are archived, reruns recorded on 24 September. | Do not merge the Delaunay target with rectangular comparisons; current job status unknown. | unknown — live scheduler not queried; historical ledger states are not current job evidence | Retrieve or account for every rerun, failed seed and missing stage; preserve the archived comparison context. | 2026-10-04 | +| [Point-source sampler admission](https://github.com/PyAutoLabs/autolens_inference/blob/146a84346eaa51eda158634cae520d677f3e6213/wiki/project/state.md) | needs-decision | 1 | 2 October project record reports five Nautilus seeds; no gradient-sampler admission measurement. | Gradient cost share and sampler admission remain unmeasured; Pulse owns likelihood optimisation. | unknown — live scheduler not queried; historical ledger states are not current job evidence | Assess a bounded gradient-sampler admission proposal with the human; no new job is authorized. | 2026-10-04 | +| [Graphical and EP inference coordination](https://github.com/PyAutoLabs/PyAutoMind/blob/8dd1edc7e04c960daae3663513c61395823eddb0/draft/research/graphical_ep/ep_campaign.md) | needs-slicing | 3 | Mind campaign map and two scoping contracts preserved verbatim; implementation work remains in Mind. | Old scoping contains completed phases and decision gates; not a bulk runnable queue. | unknown — live scheduler not queried; historical ledger states are not current job evidence | Reconcile current Cortex observations and shipped fixes, then select one bounded phase. | 2026-10-04 | +| [Analytic Gaussian ensemble and control calibration](https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/analytic_gaussian.md) | blocked | 0 | Cortex records accepted wave-1 baseline and 30 September independent-review caveats. | N=25 stays unsubmitted until control/threshold diagnostic is resolved. | unknown — live scheduler not queried; historical ledger states are not current job evidence | Use Cortex planned diagnostic on passing, edge and worst seeds; retain the human conclusion there. | 2026-10-04 | +| [N=50 NUTS versus three EP repeats](https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/ep_toy_gaussian.md) | needs-decision | 0 | Cortex records failed wave 1 (EMFILE/OOM), wave-2 preparation and no submission. | Explicit human go; sample entry/export and resource settings must be verified. | unknown — live scheduler not queried; historical ledger states are not current job evidence | Present the existing wave-2 proposal with current dependency evidence for decision. | 2026-10-04 | +| [Hierarchical slope recovery at scale](https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/slope_hierarchy_scale.md) | blocked | 0 | Cortex retains graphical baseline, post-processing OOM and stale EP hierarchical factor; Mind records later moments fix. | Summary/sample retrieval and release/RAL mirror reconciliation outstanding; no reclassification from stale prose. | unknown — live scheduler not queried; historical ledger states are not current job evidence | Verify the moments fix release and project mirror; recover missing summaries from existing samples before a proposed rerun. | 2026-10-04 | +| [IC50 EP and graphical scale ladder](https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/ic50_workspace.md) | blocked | 0 | Cortex records N=5/10/25 results, failed N=50 EP rung and width-interpretation caveat. | Projection fix and mean-field width reporting require reconciliation before scientific comparison. | unknown — live scheduler not queried; historical ledger states are not current job evidence | Check implementation close-outs, then propose a seeded ladder with appropriate output and mean-field diagnostics. | 2026-10-04 | + +## Active tasks + +Open means tracked, not necessarily running. Blockers and decisions still apply. + +| Task | Campaign | Status | Priority | Next step | +|---|---|---|---|---| +| [Drop the 1e-3 prior centring for shear / multipole / ell_comps in the benchmark](https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/drop_1e3_prior_centring_after_631.md) | slam-hst-base | blocked | normal | Verify the release containing Galaxy#634 and Lens#754 before changing priors; retain zero-centre gradient witness. | +| [Reconcile inference documentation with the current Cortex ledger contract](https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/reconcile_cortex_ledger_references.md) | slam-hst-base | ready | normal | Reconcile Now/Runs/Log documentation via the bounded organ implementation PR; do not alter scientific records. | +| [EP campaign — phase map for the 2026 Q3 graphical/EP push](https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/ep_campaign.md) | graphical-ep | needs-decision | high | Reconcile current Cortex records and human gates; this campaign map is never issued or bulk scheduled. | +| [Expectation Propagation Scale-Up — Scoping](https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/ep_scoping.md) | graphical-ep | needs-slicing | high | Reconcile completed wrapper work and Pulse profiling ownership before selecting a bounded remaining inference phase. | +| [Graphical Model Scale-Up — Scoping](https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/graphical_scoping.md) | graphical-ep | needs-slicing | high | Reconcile current Cortex scale results and library dependencies; select one bounded inference phase with a witness. | +| [NUTS/HMC trial on validated likelihoods](https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/gradient_sampler_validated_likelihoods.md) | point-source-admission | needs-decision | normal | An unscoped idea, not permission to submit: reconcile point-source admission with imaging and weak-lensing candidate targets. | + +Completed and superseded tasks remain in the [ledger](https://github.com/PyAutoLabs/PyAutoInsight/blob/main/campaigns.yaml). + +## Inference evidence + + + +## autolens_inference +Integrity: ok. Freshness: freshness policy unspecified · evidence time unknown. Qualification: 0/56 accepted by producer; remaining assessments shown below. +Capture source branch: feature/insight-producer; revision: 9c362a7a298238d766f61e812c694813e94830d1; fetched 2026-10-04T07:45:10Z; latest attempt 2026-10-04T07:45:10Z. + +Coverage: {"excluded": [], "expected": {"reason": "No complete expected-run manifest; unrecorded jobs cannot be enumerated", "runs": null}, "observed": {"archived_records": 50, "execution_complete": 55, "execution_stopped_early": 1, "records": 56, "runs": 15}} +No scientific acceptance or convergence inferred from process completion +Sample archives remain in project storage; current availability unknown +No evidence from the retired inference programme is imported +Archived rows belong to this project's September 2026 runs and are historical, not current baselines + +| Target | Stage | Seed | Execution | Convergence | Acceptance | Sampling s | Total s | Archive | Evidence | +|---|---|---|---|---|---|---:|---:|---|---| +| hst/slam5_delaunay_1250/seed0 | unknown | 0 | complete | not_assessed | not_assessed | unknown | unknown | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 8, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "dense", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "missing", + "values": {} + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json" + ], + "execution": { + "completed": null, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 37549 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": null, + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": null, + "target": "hst/slam5_delaunay_1250/seed0", + "timings": { + "compile_s": null, + "definitions": { + "compile_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "sampling_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "setup_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "total_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + }, + "sampling_s": null, + "setup_s": null, + "total_s": null + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "execution.completed": "No pipeline-wide completion marker is recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result", + "timings": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed0 | source_lp[1] | 0 | complete | not_assessed | not_assessed | 820.3699300289154 | 965.2785516558215 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-9b14006556621cdb + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 8, + "free_parameters": 17, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "dense", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 50, + "n_live": 200, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": false, + "likelihood_evals": 95550, + "log_evidence": 31968.383763833426, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 32068.410071242244, + "positions_info_present": false, + "posterior": { + "einstein_radius": { + "lower_1": 1.6002283246686797, + "median": 1.6004230089854896, + "sigma": 0.0001949065946738049, + "upper_1": 1.6006181378580273 + }, + "galaxies.lens.bulge.profile_list.0.centre_0": { + "lower_1": -8.031775937429898e-05, + "median": 0.0001389361594639322, + "sigma": 0.000216724895096841, + "upper_1": 0.000353132030819383 + }, + "galaxies.lens.bulge.profile_list.0.centre_1": { + "lower_1": -0.0002547771837667912, + "median": -2.7821663862796783e-05, + "sigma": 0.00022149755077947357, + "upper_1": 0.00018821791779215593 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_0": { + "lower_1": -0.04136838249203534, + "median": 0.057207819575440126, + "sigma": 0.08639504720137905, + "upper_1": 0.13142171191072277 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.1398513547720698, + "median": 0.06461580384668345, + "sigma": 0.17810747135467458, + "upper_1": 0.2163635879372794 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_0": { + "lower_1": 0.049412300834958935, + "median": 0.05190987972283432, + "sigma": 0.002304423069589799, + "upper_1": 0.05402114697413853 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_1": { + "lower_1": -0.004906295802892346, + "median": -0.00042521763287943433, + "sigma": 0.005736047900624068, + "upper_1": 0.006565799998355789 + }, + "galaxies.lens.mass.centre_0": { + "lower_1": -0.000349412945704988, + "median": -6.0885417467502025e-05, + "sigma": 0.00028788431997389837, + "upper_1": 0.00022635569424280866 + }, + "galaxies.lens.mass.centre_1": { + "lower_1": -0.0004850534559733189, + "median": -0.0002098508453829595, + "sigma": 0.000270473448362507, + "upper_1": 5.58934407516951e-05 + }, + "galaxies.lens.mass.ell_comps_0": { + "lower_1": 0.05214911753329512, + "median": 0.05273718451387662, + "sigma": 0.0005934193230080972, + "upper_1": 0.05333595617931131 + }, + "galaxies.lens.mass.ell_comps_1": { + "lower_1": -0.0005896562691280588, + "median": 0.0003613494208301405, + "sigma": 0.0009504087390928866, + "upper_1": 0.0013111612090577144 + }, + "galaxies.source.bulge.profile_list.0.centre_0": { + "lower_1": -0.0005294409654987636, + "median": -0.00030037628811868027, + "sigma": 0.00022929964615031568, + "upper_1": -7.084167319813222e-05 + }, + "galaxies.source.bulge.profile_list.0.centre_1": { + "lower_1": -0.00048664147381876125, + "median": -0.0002798699117450228, + "sigma": 0.0002066032623317734, + "upper_1": -7.343494915521446e-05 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.09559524951610894, + "median": 0.09754810902233561, + "sigma": 0.002008368902107406, + "upper_1": 0.09961198732032375 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.05788301611036884, + "median": -0.05584901861446844, + "sigma": 0.0019250978943196155, + "upper_1": -0.05403282032172961 + }, + "gamma_1": { + "lower_1": 0.049618681823851034, + "median": 0.050007664548500266, + "sigma": 0.00038176359228585255, + "upper_1": 0.05038220900842274 + }, + "gamma_2": { + "lower_1": 0.04978479191406146, + "median": 0.050104553018764326, + "sigma": 0.0003271490529083848, + "upper_1": 0.05043909001987823 + }, + "shear_magnitude": { + "lower_1": 0.07043406871177625, + "median": 0.07079428668589158, + "sigma": 0.0003526289539876881, + "upper_1": 0.07113932661975163 + } + }, + "truth_delta_sigma": { + "einstein_radius": 2.1703164338665526, + "gamma_1": 0.020076687916653336, + "gamma_2": 0.3195883278121635, + "shear_magnitude": 0.23710068697232145 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 37549 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-9b14006556621cdb", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "source_lp[1]", + "target": "hst/slam5_delaunay_1250/seed0", + "timings": { + "compile_s": 11.454642791766673, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 820.3699300289154, + "setup_s": null, + "total_s": 965.2785516558215 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed0 | source_pix[1] | 0 | complete | not_assessed | not_assessed | 2047.43035531044 | 2128.72460657591 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-1ad2b0be53ac3cb8 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 8, + "free_parameters": 10, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "dense", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 47760, + "log_evidence": 31559.6512100224, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31631.303764237837, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": 0.0006801302640231238, + "median": 0.0008591860109828434, + "sigma": 0.0001671329162394018, + "upper_1": 0.0010143960965019274 + }, + "centre_1": { + "lower_1": -0.0015785516108246965, + "median": -0.0014672992533057403, + "sigma": 0.00014054147674035563, + "upper_1": -0.0012974686573439852 + }, + "einstein_radius": { + "lower_1": 1.6002479252972448, + "median": 1.6004051693211514, + "sigma": 0.00013192588550658968, + "upper_1": 1.600511777068258 + }, + "ell_comps_0": { + "lower_1": 0.051445740526479385, + "median": 0.05172608472239314, + "sigma": 0.00039757549887187507, + "upper_1": 0.052240891524223135 + }, + "ell_comps_1": { + "lower_1": 0.0007754877045155659, + "median": 0.0009941591829107004, + "sigma": 0.0002777624901942398, + "upper_1": 0.0013310126849040455 + }, + "gamma_1": { + "lower_1": 0.04990885145700015, + "median": 0.050019656365527146, + "sigma": 0.00017379416219936647, + "upper_1": 0.05025643978139888 + }, + "gamma_2": { + "lower_1": 0.04956489823223322, + "median": 0.04973126723927235, + "sigma": 0.00026016320179940433, + "upper_1": 0.05008522463583203 + }, + "inner_coefficient": { + "lower_1": 8.553334174988577e-06, + "median": 0.00012847582074597793, + "sigma": 0.0007746233642857129, + "upper_1": 0.0015578000627464144 + }, + "outer_coefficient": { + "lower_1": 76.04556380041683, + "median": 88.81257309756506, + "sigma": 13.18789960039561, + "upper_1": 102.42136300120805 + }, + "shear_magnitude": { + "lower_1": 0.0703686921612, + "median": 0.07053423547142829, + "sigma": 0.00028448266375911274, + "upper_1": 0.07093765748871822 + }, + "signal_scale": { + "lower_1": 0.05950192751807972, + "median": 0.0680945152855752, + "sigma": 0.00994275097630944, + "upper_1": 0.0793874294706986 + } + }, + "truth_delta_sigma": { + "einstein_radius": 3.0711889451829575, + "gamma_1": 0.11310141421548145, + "gamma_2": -1.0329391661425595, + "shear_magnitude": -0.6202228455505584 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 37549 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-1ad2b0be53ac3cb8", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "source_pix[1]", + "target": "hst/slam5_delaunay_1250/seed0", + "timings": { + "compile_s": 21.742441596928984, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 2047.43035531044, + "setup_s": null, + "total_s": 2128.72460657591 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed0 | source_pix[2] | 0 | complete | not_assessed | not_assessed | 111.54320764541626 | 176.4244288089685 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-122202734335fe14 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 8, + "free_parameters": 3, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "dense", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 20, + "n_live": 75, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 2220, + "log_evidence": 31536.568792661637, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31549.54058766012, + "positions_info_present": true, + "posterior": { + "inner_coefficient": { + "lower_1": 3.843004952776652e-06, + "median": 6.63256429493189e-05, + "sigma": 0.0007650282970931766, + "upper_1": 0.0015338995991391298 + }, + "outer_coefficient": { + "lower_1": 72.4279672138732, + "median": 82.99985572032065, + "sigma": 8.376141505280088, + "upper_1": 89.18025022443338 + }, + "signal_scale": { + "lower_1": 0.04288033783359011, + "median": 0.04652672970284491, + "sigma": 0.005187199707235319, + "upper_1": 0.05325473724806075 + } + }, + "truth_delta_sigma": {} + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 37549 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-122202734335fe14", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "source_pix[2]", + "target": "hst/slam5_delaunay_1250/seed0", + "timings": { + "compile_s": 9.793207644950598, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 111.54320764541626, + "setup_s": null, + "total_s": 176.4244288089685 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed0 | light[1] | 0 | complete | not_assessed | not_assessed | 406.49094557762146 | 472.91616048291326 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-7f695c3cdb0cdedb + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 8, + "free_parameters": 6, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "dense", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 11720, + "log_evidence": 31525.56112336346, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31550.377783643526, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.0001010354719605698, + "median": 0.00012208147104635384, + "sigma": 0.00022335989655310342, + "upper_1": 0.000345684321145637 + }, + "centre_1": { + "lower_1": -0.00023167716280779596, + "median": -2.239756492462005e-05, + "sigma": 0.00021226235758127365, + "upper_1": 0.00019284755235475135 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.04033052577027705, + "median": 0.05192651164520427, + "sigma": 0.012078040909558618, + "upper_1": 0.06448660758939428 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.057713061475420435, + "median": -0.01426792487023704, + "sigma": 0.04710765887658856, + "upper_1": 0.03650225627775669 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_0": { + "lower_1": 0.03870228721333537, + "median": 0.051676229494409955, + "sigma": 0.013980696488286686, + "upper_1": 0.06666368018990874 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_1": { + "lower_1": -0.029177185986042846, + "median": 0.020862063058929427, + "sigma": 0.05080269826683603, + "upper_1": 0.07242821054762921 + } + }, + "truth_delta_sigma": {} + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 37549 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-7f695c3cdb0cdedb", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "light[1]", + "target": "hst/slam5_delaunay_1250/seed0", + "timings": { + "compile_s": 3.2584053240716457, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 406.49094557762146, + "setup_s": null, + "total_s": 472.91616048291326 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed0 | mass_total[1] | 0 | complete | not_assessed | not_assessed | 1383.843026638031 | 1475.3449251349084 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-03dd03936a025726 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 8, + "free_parameters": 8, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "dense", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 32000, + "log_evidence": 31512.923096191415, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31569.144087280234, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.0006288710092493257, + "median": -0.0001251878292926935, + "sigma": 0.0004747731060697553, + "upper_1": 0.0003206752028901849 + }, + "centre_1": { + "lower_1": -0.0009359182495793782, + "median": -0.0004848204460337668, + "sigma": 0.00041933715641742376, + "upper_1": -9.72439367445307e-05 + }, + "einstein_radius": { + "lower_1": 1.6098550257566364, + "median": 1.6118726441881506, + "sigma": 0.0021943309986449666, + "upper_1": 1.6142436877539263 + }, + "ell_comps_0": { + "lower_1": 0.0577322193854617, + "median": 0.060328200039030076, + "sigma": 0.002819755563409649, + "upper_1": 0.063371730512281 + }, + "ell_comps_1": { + "lower_1": -0.00013517197937906668, + "median": 0.001552495171977931, + "sigma": 0.002362793212106759, + "upper_1": 0.0045904144448344515 + }, + "gamma_1": { + "lower_1": 0.05551225990608463, + "median": 0.05681453166985495, + "sigma": 0.001466372848490969, + "upper_1": 0.05844500560306657 + }, + "gamma_2": { + "lower_1": 0.05187359038951638, + "median": 0.052833093385933186, + "sigma": 0.001300806646608265, + "upper_1": 0.05447520368273291 + }, + "shear_magnitude": { + "lower_1": 0.0762606424596888, + "median": 0.07781637561596229, + "sigma": 0.0015935132850569203, + "upper_1": 0.07944766902980265 + }, + "slope": { + "lower_1": 2.1082053325640215, + "median": 2.13399021805761, + "sigma": 0.02876877423554336, + "upper_1": 2.1657428810351083 + } + }, + "truth_delta_sigma": { + "einstein_radius": 5.4105985812906185, + "gamma_1": 4.647202569842807, + "gamma_2": 2.1779511915319745, + "shear_magnitude": 4.459139163721316, + "slope": 4.657487905482857 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 37549 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-03dd03936a025726", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "mass_total[1]", + "target": "hst/slam5_delaunay_1250/seed0", + "timings": { + "compile_s": 3.3110593240708113, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 1383.843026638031, + "setup_s": null, + "total_s": 1475.3449251349084 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed1 | unknown | 1 | complete | not_assessed | not_assessed | unknown | unknown | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 8, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "dense", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "missing", + "values": {} + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json" + ], + "execution": { + "completed": null, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 37549 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": null, + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": null, + "target": "hst/slam5_delaunay_1250/seed1", + "timings": { + "compile_s": null, + "definitions": { + "compile_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "sampling_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "setup_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "total_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + }, + "sampling_s": null, + "setup_s": null, + "total_s": null + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "execution.completed": "No pipeline-wide completion marker is recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result", + "timings": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed1 | source_lp[1] | 1 | complete | not_assessed | not_assessed | 882.9330096244812 | 1023.6260516527109 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-9b14006556621cdb + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 8, + "free_parameters": 17, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "dense", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 50, + "n_live": 200, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": false, + "likelihood_evals": 92700, + "log_evidence": 31968.288617476795, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 32068.57361229643, + "positions_info_present": false, + "posterior": { + "einstein_radius": { + "lower_1": 1.6002276155723718, + "median": 1.6004304841938644, + "sigma": 0.0001997230634146474, + "upper_1": 1.600627061699201 + }, + "galaxies.lens.bulge.profile_list.0.centre_0": { + "lower_1": -8.308618901208292e-05, + "median": 0.00014260490147366577, + "sigma": 0.0002266840977539146, + "upper_1": 0.0003702820064957463 + }, + "galaxies.lens.bulge.profile_list.0.centre_1": { + "lower_1": -0.00025950551857048645, + "median": -4.367066791094482e-05, + "sigma": 0.0002184626190544969, + "upper_1": 0.00017741971953850737 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_0": { + "lower_1": -0.04618982486279506, + "median": 0.04760696937209324, + "sigma": 0.08353462074374376, + "upper_1": 0.12087941662469245 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.14297181460836844, + "median": 0.07417549599662328, + "sigma": 0.1764847989190686, + "upper_1": 0.20999778322976873 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_0": { + "lower_1": 0.04982140468114926, + "median": 0.05228981940342104, + "sigma": 0.0022904287073468345, + "upper_1": 0.05440226209584293 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_1": { + "lower_1": -0.005536639002266688, + "median": -0.0010953838231013062, + "sigma": 0.005197646481569276, + "upper_1": 0.004858653960871863 + }, + "galaxies.lens.mass.centre_0": { + "lower_1": -0.0003495463433704143, + "median": -6.027831288266854e-05, + "sigma": 0.00028751393756083846, + "upper_1": 0.00022548153175126266 + }, + "galaxies.lens.mass.centre_1": { + "lower_1": -0.00046208337261394454, + "median": -0.00020255983123484595, + "sigma": 0.00026305845776553754, + "upper_1": 6.403354291713059e-05 + }, + "galaxies.lens.mass.ell_comps_0": { + "lower_1": 0.05214472772589139, + "median": 0.05275800112231576, + "sigma": 0.0006200930439337927, + "upper_1": 0.053384913813758976 + }, + "galaxies.lens.mass.ell_comps_1": { + "lower_1": -0.0005352959203160756, + "median": 0.000392194937727783, + "sigma": 0.0009294947407969931, + "upper_1": 0.0013236935612779106 + }, + "galaxies.source.bulge.profile_list.0.centre_0": { + "lower_1": -0.000511013491315985, + "median": -0.0002872030390811002, + "sigma": 0.00022230237068070595, + "upper_1": -6.640874995457313e-05 + }, + "galaxies.source.bulge.profile_list.0.centre_1": { + "lower_1": -0.00046067065988112977, + "median": -0.00026537322431123566, + "sigma": 0.00019536456210008908, + "upper_1": -6.99415356809516e-05 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.09567987828478586, + "median": 0.09770405129255826, + "sigma": 0.001972809199493303, + "upper_1": 0.09962549668377246 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.05791615491065392, + "median": -0.055889267942737346, + "sigma": 0.0019243623607940984, + "upper_1": -0.05406743018906572 + }, + "gamma_1": { + "lower_1": 0.04964245211627232, + "median": 0.05001047844938995, + "sigma": 0.00036722800152026724, + "upper_1": 0.050376908119312856 + }, + "gamma_2": { + "lower_1": 0.04977567439083208, + "median": 0.05011573922308979, + "sigma": 0.0003442813540290926, + "upper_1": 0.050464237098890266 + }, + "shear_magnitude": { + "lower_1": 0.07044752697621014, + "median": 0.07080260205352631, + "sigma": 0.0003573608641845166, + "upper_1": 0.07116224870457917 + } + }, + "truth_delta_sigma": { + "einstein_radius": 2.1554055225486572, + "gamma_1": 0.02853390631044192, + "gamma_2": 0.3361762748266817, + "shear_magnitude": 0.2572299993770043 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 37549 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-9b14006556621cdb", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "source_lp[1]", + "target": "hst/slam5_delaunay_1250/seed1", + "timings": { + "compile_s": 10.974624747410417, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 882.9330096244812, + "setup_s": null, + "total_s": 1023.6260516527109 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed1 | source_pix[1] | 1 | complete | not_assessed | not_assessed | 2589.473468542099 | 2668.5737252449617 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-1ad2b0be53ac3cb8 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 8, + "free_parameters": 10, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "dense", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 60600, + "log_evidence": 31553.65930637181, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31628.864924269645, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": 7.362246452831716e-05, + "median": 0.0001692041633130264, + "sigma": 0.00011687533428925861, + "upper_1": 0.00030737313310683436 + }, + "centre_1": { + "lower_1": -0.000886768799958333, + "median": -0.0007705929035354045, + "sigma": 0.0001707948067108975, + "upper_1": -0.000545179186536538 + }, + "einstein_radius": { + "lower_1": 1.600612194099884, + "median": 1.6006768943224843, + "sigma": 9.769217373223338e-05, + "upper_1": 1.6008075784473486 + }, + "ell_comps_0": { + "lower_1": 0.05426639659969243, + "median": 0.05449525513510206, + "sigma": 0.0003001631279877931, + "upper_1": 0.054866722855668015 + }, + "ell_comps_1": { + "lower_1": 0.007025513491640507, + "median": 0.007200397867006374, + "sigma": 0.00015976762421361038, + "upper_1": 0.0073450487400677274 + }, + "gamma_1": { + "lower_1": 0.05240454697774065, + "median": 0.05249003464511933, + "sigma": 7.617739401251031e-05, + "upper_1": 0.05255690176576567 + }, + "gamma_2": { + "lower_1": 0.050705555682214604, + "median": 0.05084166713912026, + "sigma": 0.00016859656499251113, + "upper_1": 0.051042748812199626 + }, + "inner_coefficient": { + "lower_1": 7.61344383049862e-06, + "median": 0.00010615660062285503, + "sigma": 0.0007158812509578383, + "upper_1": 0.0014393759457461753 + }, + "outer_coefficient": { + "lower_1": 68.18791477187187, + "median": 73.03114458257434, + "sigma": 4.9561398191912645, + "upper_1": 78.1001944102544 + }, + "shear_magnitude": { + "lower_1": 0.07299084154770966, + "median": 0.07308083254149689, + "sigma": 0.00010074473667844724, + "upper_1": 0.07319233102106655 + }, + "signal_scale": { + "lower_1": 0.07554752491871021, + "median": 0.08003183001624886, + "sigma": 0.004734870891832284, + "upper_1": 0.08501726670237478 + } + }, + "truth_delta_sigma": { + "einstein_radius": 6.928849022640278, + "gamma_1": 32.68731724677271, + "gamma_2": 4.992196247637906, + "shear_magnitude": 23.526334982710605 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 37549 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-1ad2b0be53ac3cb8", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "source_pix[1]", + "target": "hst/slam5_delaunay_1250/seed1", + "timings": { + "compile_s": 21.81813602289185, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 2589.473468542099, + "setup_s": null, + "total_s": 2668.5737252449617 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed1 | source_pix[2] | 1 | complete | not_assessed | not_assessed | 116.28890442848206 | 176.19057126017287 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-122202734335fe14 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 8, + "free_parameters": 3, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "dense", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 20, + "n_live": 75, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 2240, + "log_evidence": 31523.789020705255, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31536.84804793868, + "positions_info_present": true, + "posterior": { + "inner_coefficient": { + "lower_1": 5.48779502908254e-06, + "median": 0.00010491045113650727, + "sigma": 0.0008107902610350543, + "upper_1": 0.001627068317099191 + }, + "outer_coefficient": { + "lower_1": 64.88960751814159, + "median": 71.32483889605024, + "sigma": 6.02789438610268, + "upper_1": 76.94539629034695 + }, + "signal_scale": { + "lower_1": 0.04509695692377588, + "median": 0.04893607110555434, + "sigma": 0.004505534370594359, + "upper_1": 0.0541080256649646 + } + }, + "truth_delta_sigma": {} + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 37549 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-122202734335fe14", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "source_pix[2]", + "target": "hst/slam5_delaunay_1250/seed1", + "timings": { + "compile_s": 8.251005443744361, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 116.28890442848206, + "setup_s": null, + "total_s": 176.19057126017287 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed1 | light[1] | 1 | complete | not_assessed | not_assessed | 457.26988673210144 | 530.5928531419486 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-7f695c3cdb0cdedb + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 8, + "free_parameters": 6, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "dense", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 13580, + "log_evidence": 31514.026948751867, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31538.69002583083, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.00011262714163890226, + "median": 0.00013342639144256927, + "sigma": 0.00025397675547227463, + "upper_1": 0.00039532636930564703 + }, + "centre_1": { + "lower_1": -0.0002542248674751995, + "median": -3.266948044239967e-05, + "sigma": 0.0002309319805995855, + "upper_1": 0.00020763909372397145 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.019196134143757093, + "median": 0.05427828040518556, + "sigma": 0.029521047318985576, + "upper_1": 0.07823822878172824 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.05287090945447861, + "median": 0.009369931939461998, + "sigma": 0.047988770224109495, + "upper_1": 0.043106630993740375 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_0": { + "lower_1": 0.017147550860261793, + "median": 0.049902522493308094, + "sigma": 0.027583853174651805, + "upper_1": 0.0723152572095654 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_1": { + "lower_1": -0.05609806218309943, + "median": -0.006322863605877536, + "sigma": 0.04528868905307586, + "upper_1": 0.0344793159230523 + } + }, + "truth_delta_sigma": {} + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 37549 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-7f695c3cdb0cdedb", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "light[1]", + "target": "hst/slam5_delaunay_1250/seed1", + "timings": { + "compile_s": 3.2410381459631026, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 457.26988673210144, + "setup_s": null, + "total_s": 530.5928531419486 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed1 | mass_total[1] | 1 | complete | not_assessed | not_assessed | 1048.9292471408844 | 1138.162050106097 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-03dd03936a025726 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 8, + "free_parameters": 8, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "dense", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 23140, + "log_evidence": 31503.004625794427, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31555.84747931535, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.0010127103909564237, + "median": -0.0005628527132957931, + "sigma": 0.0005124450140973802, + "upper_1": 1.217963723833675e-05 + }, + "centre_1": { + "lower_1": -0.0003613656187329064, + "median": 2.8392630178950856e-07, + "sigma": 0.0003709809593391679, + "upper_1": 0.0003805962999454295 + }, + "einstein_radius": { + "lower_1": 1.5982737313845827, + "median": 1.5996046343149348, + "sigma": 0.001860016905959716, + "upper_1": 1.6019937651965022 + }, + "ell_comps_0": { + "lower_1": 0.052667368336424054, + "median": 0.05366832777829295, + "sigma": 0.0010803283221513908, + "upper_1": 0.054828024980726836 + }, + "ell_comps_1": { + "lower_1": -3.467574175924511e-05, + "median": 0.0010665503857261347, + "sigma": 0.0011679981993357395, + "upper_1": 0.0023013206569122337 + }, + "gamma_1": { + "lower_1": 0.048601010066459414, + "median": 0.04958846837707677, + "sigma": 0.0013871362102454085, + "upper_1": 0.05137528248695023 + }, + "gamma_2": { + "lower_1": 0.05013110873927545, + "median": 0.05063656887536409, + "sigma": 0.0005412393071556852, + "upper_1": 0.05121358735358682 + }, + "shear_magnitude": { + "lower_1": 0.07001960202451213, + "median": 0.07087351796231568, + "sigma": 0.0011697359649866165, + "upper_1": 0.07235907395448536 + }, + "slope": { + "lower_1": 1.970581841479581, + "median": 1.9869370334827916, + "sigma": 0.023083494287770123, + "upper_1": 2.0167488300551213 + } + }, + "truth_delta_sigma": { + "einstein_radius": -0.21256026426343994, + "gamma_1": -0.29667715389711186, + "gamma_2": 1.1761320121211698, + "shear_magnitude": 0.13921076938314023, + "slope": -0.5659007407786304 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 37549 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-03dd03936a025726", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "mass_total[1]", + "target": "hst/slam5_delaunay_1250/seed1", + "timings": { + "compile_s": 3.179320426657796, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 1048.9292471408844, + "setup_s": null, + "total_s": 1138.162050106097 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed0 | unknown | 0 | complete | not_assessed | not_assessed | unknown | unknown | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 8, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "sparse", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "missing", + "values": {} + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json" + ], + "execution": { + "completed": null, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 4813 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": null, + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": null, + "target": "hst/slam5_delaunay_1250/seed0", + "timings": { + "compile_s": null, + "definitions": { + "compile_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "sampling_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "setup_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "total_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + }, + "sampling_s": null, + "setup_s": null, + "total_s": null + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "execution.completed": "No pipeline-wide completion marker is recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result", + "timings": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed0 | source_lp[1] | 0 | complete | not_assessed | not_assessed | 749.7595109939575 | 888.2430416657589 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-9b14006556621cdb + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 8, + "free_parameters": 17, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "sparse", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 50, + "n_live": 200, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": false, + "likelihood_evals": 89800, + "log_evidence": 31968.45273593527, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 32068.5076835564, + "positions_info_present": false, + "posterior": { + "einstein_radius": { + "lower_1": 1.6002240173944375, + "median": 1.6004287202813214, + "sigma": 0.00020385706387748836, + "upper_1": 1.6006317315221925 + }, + "galaxies.lens.bulge.profile_list.0.centre_0": { + "lower_1": -7.692944415543618e-05, + "median": 0.00014028947296201678, + "sigma": 0.00022137573360369865, + "upper_1": 0.00036582202305196113 + }, + "galaxies.lens.bulge.profile_list.0.centre_1": { + "lower_1": -0.00025214362524379833, + "median": -3.4911806155984177e-05, + "sigma": 0.00021754225788628916, + "upper_1": 0.00018294089052877998 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.050334322497495074, + "median": 0.052732598225102775, + "sigma": 0.002183289097765568, + "upper_1": 0.05470090069302621 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.006982054898686185, + "median": -0.002840496185065656, + "sigma": 0.005157943065047762, + "upper_1": 0.003333831231409339 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_0": { + "lower_1": -0.042520445159745124, + "median": 0.03632431415304117, + "sigma": 0.07319454636447181, + "upper_1": 0.10386864756919852 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_1": { + "lower_1": -0.09405623933664098, + "median": 0.10222180294842424, + "sigma": 0.1545705455405051, + "upper_1": 0.21508485174436917 + }, + "galaxies.lens.mass.centre_0": { + "lower_1": -0.0003500977233288058, + "median": -6.93997989023038e-05, + "sigma": 0.00028074106866033706, + "upper_1": 0.00021138441399186833 + }, + "galaxies.lens.mass.centre_1": { + "lower_1": -0.00047158820523762475, + "median": -0.0002049267366680098, + "sigma": 0.0002649732354599183, + "upper_1": 5.8358265682211934e-05 + }, + "galaxies.lens.mass.ell_comps_0": { + "lower_1": 0.05212216412576254, + "median": 0.05272908001194239, + "sigma": 0.0006105595284171643, + "upper_1": 0.053343283182596866 + }, + "galaxies.lens.mass.ell_comps_1": { + "lower_1": -0.0005623984117655446, + "median": 0.00035042120982297574, + "sigma": 0.0009198813132368747, + "upper_1": 0.0012773642147082048 + }, + "galaxies.source.bulge.profile_list.0.centre_0": { + "lower_1": -0.0005211997962420691, + "median": -0.00029360793769185524, + "sigma": 0.00022484646367898557, + "upper_1": -7.150686888409796e-05 + }, + "galaxies.source.bulge.profile_list.0.centre_1": { + "lower_1": -0.00047177461546961364, + "median": -0.00026976907532285266, + "sigma": 0.00020148715089083222, + "upper_1": -6.880031368794921e-05 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.09554776273672833, + "median": 0.09755893083166312, + "sigma": 0.0020202082288522313, + "upper_1": 0.09958817919443279 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.0578338316588529, + "median": -0.0558037731559498, + "sigma": 0.0019278681119117777, + "upper_1": -0.05397809543502934 + }, + "gamma_1": { + "lower_1": 0.04962198129698579, + "median": 0.04999112224844983, + "sigma": 0.000371895191425331, + "upper_1": 0.050365771679836455 + }, + "gamma_2": { + "lower_1": 0.04977363665935129, + "median": 0.05010255702884514, + "sigma": 0.0003358072354974727, + "upper_1": 0.05044525113034624 + }, + "shear_magnitude": { + "lower_1": 0.07043876892842978, + "median": 0.07078178700539806, + "sigma": 0.0003443603678288734, + "upper_1": 0.07112748966408752 + } + }, + "truth_delta_sigma": { + "einstein_radius": 2.103043540247376, + "gamma_1": -0.023871649203499167, + "gamma_2": 0.3054044642403472, + "shear_magnitude": 0.20649555926432006 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 4813 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-9b14006556621cdb", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "source_lp[1]", + "target": "hst/slam5_delaunay_1250/seed0", + "timings": { + "compile_s": 10.573504409752786, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 749.7595109939575, + "setup_s": null, + "total_s": 888.2430416657589 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed0 | source_pix[1] | 0 | complete | not_assessed | not_assessed | 6124.670238494873 | 6221.422692308202 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-1ad2b0be53ac3cb8 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 8, + "free_parameters": 10, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "sparse", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 66580, + "log_evidence": 31537.516729375282, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31613.01421283507, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.0007226981015702346, + "median": -0.00022478908717301854, + "sigma": 0.0003371716730284488, + "upper_1": -4.835475551333688e-05 + }, + "centre_1": { + "lower_1": 0.00024367863009395696, + "median": 0.00036192715447267413, + "sigma": 0.00016054977415283262, + "upper_1": 0.0005647781783996222 + }, + "einstein_radius": { + "lower_1": 1.6005699713810975, + "median": 1.600678557698494, + "sigma": 0.0004621774299470305, + "upper_1": 1.6014943262409915 + }, + "ell_comps_0": { + "lower_1": 0.057066703741393256, + "median": 0.05735357597670084, + "sigma": 0.0005137105245230505, + "upper_1": 0.05809412479043936 + }, + "ell_comps_1": { + "lower_1": 0.00032828113046187853, + "median": 0.0006020466990516052, + "sigma": 0.0003184547002581831, + "upper_1": 0.0009651905309782448 + }, + "gamma_1": { + "lower_1": 0.049472389871745216, + "median": 0.049595331328059385, + "sigma": 0.00012834893946349346, + "upper_1": 0.0497290877506722 + }, + "gamma_2": { + "lower_1": 0.05241063978352845, + "median": 0.05257249137194684, + "sigma": 0.00035265474157092705, + "upper_1": 0.053115949266670306 + }, + "inner_coefficient": { + "lower_1": 7.672305179021023e-06, + "median": 9.053333910577159e-05, + "sigma": 0.0006087217575143897, + "upper_1": 0.0012251158202078003 + }, + "outer_coefficient": { + "lower_1": 65.96863217907004, + "median": 72.58724496110997, + "sigma": 6.293107797748, + "upper_1": 78.55484777456604 + }, + "shear_magnitude": { + "lower_1": 0.07211499585453617, + "median": 0.07226989881945396, + "sigma": 0.0002984562415880229, + "upper_1": 0.07271190833771221 + }, + "signal_scale": { + "lower_1": 0.07211749660444822, + "median": 0.07874913164997537, + "sigma": 0.0074986735373413355, + "upper_1": 0.08711484367913089 + } + }, + "truth_delta_sigma": { + "einstein_radius": 1.468175757894057, + "gamma_1": -3.1528789690990693, + "gamma_2": 7.2946456369407695, + "shear_magnitude": 5.22428578642856 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 4813 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-1ad2b0be53ac3cb8", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "source_pix[1]", + "target": "hst/slam5_delaunay_1250/seed0", + "timings": { + "compile_s": 23.97823385708034, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 6124.670238494873, + "setup_s": null, + "total_s": 6221.422692308202 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed0 | source_pix[2] | 0 | complete | not_assessed | not_assessed | 112.84573483467102 | 178.6054911534302 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-122202734335fe14 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 8, + "free_parameters": 3, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "sparse", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 20, + "n_live": 75, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 2180, + "log_evidence": 31545.43212868994, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31558.354626695716, + "positions_info_present": true, + "posterior": { + "inner_coefficient": { + "lower_1": 3.7599831641434094e-06, + "median": 5.2403616015989486e-05, + "sigma": 0.00043330228415386525, + "upper_1": 0.0008703645514718739 + }, + "outer_coefficient": { + "lower_1": 65.67539695611427, + "median": 73.7006472684364, + "sigma": 6.484120011784398, + "upper_1": 78.64363697968307 + }, + "signal_scale": { + "lower_1": 0.05582538058131591, + "median": 0.06014625668157923, + "sigma": 0.005988564780591978, + "upper_1": 0.06780251014249987 + } + }, + "truth_delta_sigma": {} + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 4813 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-122202734335fe14", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "source_pix[2]", + "target": "hst/slam5_delaunay_1250/seed0", + "timings": { + "compile_s": 9.265917625278234, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 112.84573483467102, + "setup_s": null, + "total_s": 178.6054911534302 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed0 | light[1] | 0 | complete | not_assessed | not_assessed | 353.50146985054016 | 426.6312530930154 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-7f695c3cdb0cdedb + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 8, + "free_parameters": 6, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "sparse", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 9580, + "log_evidence": 31535.294693353095, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31561.313996250938, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.0001137312872048545, + "median": 0.0001110622405605492, + "sigma": 0.00023116426822754706, + "upper_1": 0.0003485972492502396 + }, + "centre_1": { + "lower_1": -0.00026973867394926515, + "median": -3.9868231464514105e-05, + "sigma": 0.00023531674319823007, + "upper_1": 0.000200894812447195 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_0": { + "lower_1": -0.017761778998069244, + "median": 0.2068210677129733, + "sigma": 0.220383727394714, + "upper_1": 0.4230056757913588 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.18511970451007995, + "median": -0.060751524436059065, + "sigma": 0.1657049508649085, + "upper_1": 0.14629019721973707 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_0": { + "lower_1": 0.047365033992771595, + "median": 0.05055187653009896, + "sigma": 0.0031330814842944615, + "upper_1": 0.05363119696136052 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_1": { + "lower_1": -0.0015441017443226712, + "median": 0.001272476398055441, + "sigma": 0.0029823182376237795, + "upper_1": 0.004420534730924888 + } + }, + "truth_delta_sigma": {} + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 4813 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-7f695c3cdb0cdedb", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "light[1]", + "target": "hst/slam5_delaunay_1250/seed0", + "timings": { + "compile_s": 3.0349121638573706, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 353.50146985054016, + "setup_s": null, + "total_s": 426.6312530930154 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed0 | mass_total[1] | 0 | complete | not_assessed | not_assessed | 1771.1247885227203 | 1861.7668606671505 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-03dd03936a025726 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 8, + "free_parameters": 8, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "sparse", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 30640, + "log_evidence": 31515.484908394898, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31572.890362743987, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.0011159764726822365, + "median": -0.0008346340008088496, + "sigma": 0.0003101631768411548, + "upper_1": -0.0004956501189999269 + }, + "centre_1": { + "lower_1": -0.00088635612033098, + "median": -0.0005919647315031227, + "sigma": 0.00028991801089674847, + "upper_1": -0.0003065200985374831 + }, + "einstein_radius": { + "lower_1": 1.6069005484433925, + "median": 1.6106063544571758, + "sigma": 0.003258886407255357, + "upper_1": 1.6134183212579032 + }, + "ell_comps_0": { + "lower_1": 0.05813987555142578, + "median": 0.06026210805092361, + "sigma": 0.0020615917622184357, + "upper_1": 0.06226305907586265 + }, + "ell_comps_1": { + "lower_1": 0.007168814867998546, + "median": 0.007838064470547038, + "sigma": 0.0006845018094054152, + "upper_1": 0.008537818486809377 + }, + "gamma_1": { + "lower_1": 0.056493154613243175, + "median": 0.058588815371577375, + "sigma": 0.0018484452836856104, + "upper_1": 0.060190045180614396 + }, + "gamma_2": { + "lower_1": 0.05224116705129377, + "median": 0.05292826556555426, + "sigma": 0.000693899804529198, + "upper_1": 0.05362896666035217 + }, + "shear_magnitude": { + "lower_1": 0.07698107973765034, + "median": 0.07899995751433739, + "sigma": 0.0017801056434261706, + "upper_1": 0.08054129102450268 + }, + "slope": { + "lower_1": 2.0743228479983142, + "median": 2.1204192983975254, + "sigma": 0.0404506433921632, + "upper_1": 2.1552241347826406 + } + }, + "truth_delta_sigma": { + "einstein_radius": 3.2545947086595226, + "gamma_1": 4.6465077691951775, + "gamma_2": 4.2200120917184085, + "shear_magnitude": 4.656622165259945, + "slope": 2.976943956863122 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 4813 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-03dd03936a025726", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "mass_total[1]", + "target": "hst/slam5_delaunay_1250/seed0", + "timings": { + "compile_s": 3.2871260661631823, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 1771.1247885227203, + "setup_s": null, + "total_s": 1861.7668606671505 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed1 | unknown | 1 | complete | not_assessed | not_assessed | unknown | unknown | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 8, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "sparse", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "missing", + "values": {} + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json" + ], + "execution": { + "completed": null, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 4811 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": null, + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": null, + "target": "hst/slam5_delaunay_1250/seed1", + "timings": { + "compile_s": null, + "definitions": { + "compile_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "sampling_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "setup_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "total_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + }, + "sampling_s": null, + "setup_s": null, + "total_s": null + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "execution.completed": "No pipeline-wide completion marker is recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result", + "timings": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed1 | source_lp[1] | 1 | complete | not_assessed | not_assessed | 831.8062770366669 | 973.6843929146416 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-9b14006556621cdb + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 8, + "free_parameters": 17, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "sparse", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 50, + "n_live": 200, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": false, + "likelihood_evals": 94100, + "log_evidence": 31968.28939808002, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 32068.780614011714, + "positions_info_present": false, + "posterior": { + "einstein_radius": { + "lower_1": 1.6002248972064348, + "median": 1.600422687835802, + "sigma": 0.00020264745224607417, + "upper_1": 1.600630192110927 + }, + "galaxies.lens.bulge.profile_list.0.centre_0": { + "lower_1": -7.732047102839315e-05, + "median": 0.0001545566585732386, + "sigma": 0.0002278395060708388, + "upper_1": 0.0003783585411132844 + }, + "galaxies.lens.bulge.profile_list.0.centre_1": { + "lower_1": -0.0002606721919276488, + "median": -3.448536170181291e-05, + "sigma": 0.00021942862015382795, + "upper_1": 0.00017818504838000714 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.049755244497403046, + "median": 0.0521938261193833, + "sigma": 0.0023853951386957825, + "upper_1": 0.05452603477479461 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.00502544267138533, + "median": -0.0012375221636334558, + "sigma": 0.005139502416238445, + "upper_1": 0.005253562161091561 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_0": { + "lower_1": -0.03100459427394988, + "median": 0.04958772375193049, + "sigma": 0.07879764824099632, + "upper_1": 0.12659070220804275 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_1": { + "lower_1": -0.14939061554079391, + "median": 0.07283023663610734, + "sigma": 0.17627025680663724, + "upper_1": 0.20314989807248057 + }, + "galaxies.lens.mass.centre_0": { + "lower_1": -0.0003584617237860411, + "median": -7.53935310983437e-05, + "sigma": 0.00028629104210388596, + "upper_1": 0.0002141203604217308 + }, + "galaxies.lens.mass.centre_1": { + "lower_1": -0.000453880742516554, + "median": -0.00020171711435204803, + "sigma": 0.00025784437773363963, + "upper_1": 6.18080129507253e-05 + }, + "galaxies.lens.mass.ell_comps_0": { + "lower_1": 0.05212497392583492, + "median": 0.0527338372444188, + "sigma": 0.0006340613835216398, + "upper_1": 0.0533930966928782 + }, + "galaxies.lens.mass.ell_comps_1": { + "lower_1": -0.000570956670918316, + "median": 0.0003403447387821449, + "sigma": 0.0009368073591970871, + "upper_1": 0.0013026580474758583 + }, + "galaxies.source.bulge.profile_list.0.centre_0": { + "lower_1": -0.0005348496924524484, + "median": -0.0003038238733809278, + "sigma": 0.00022959869742103254, + "upper_1": -7.565229761038329e-05 + }, + "galaxies.source.bulge.profile_list.0.centre_1": { + "lower_1": -0.0004698020142082862, + "median": -0.00026931360850346466, + "sigma": 0.00020156493047671672, + "upper_1": -6.667215325485273e-05 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.09546858856600601, + "median": 0.0975234094441647, + "sigma": 0.002063685552684477, + "upper_1": 0.09959595967137497 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.057806531308544545, + "median": -0.05589796590206482, + "sigma": 0.0018572078790316075, + "upper_1": -0.05409211555048133 + }, + "gamma_1": { + "lower_1": 0.049631054398968574, + "median": 0.04999020663941062, + "sigma": 0.0003750990347839295, + "upper_1": 0.05038125246853643 + }, + "gamma_2": { + "lower_1": 0.04976161848899921, + "median": 0.0501050351191233, + "sigma": 0.000350155895664641, + "upper_1": 0.05046193028032849 + }, + "shear_magnitude": { + "lower_1": 0.07042281638220321, + "median": 0.07078505129278534, + "sigma": 0.00036700418129412965, + "upper_1": 0.07115682474479147 + } + }, + "truth_delta_sigma": { + "einstein_radius": 2.085828521982834, + "gamma_1": -0.026108733111049096, + "gamma_2": 0.29996673031572896, + "shear_magnitude": 0.2026493918088864 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 4811 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-9b14006556621cdb", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "source_lp[1]", + "target": "hst/slam5_delaunay_1250/seed1", + "timings": { + "compile_s": 10.928813580889255, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 831.8062770366669, + "setup_s": null, + "total_s": 973.6843929146416 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed1 | source_pix[1] | 1 | complete | not_assessed | not_assessed | 5258.141222238541 | 5348.343995480798 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-1ad2b0be53ac3cb8 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 8, + "free_parameters": 10, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "sparse", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 55100, + "log_evidence": 31527.810445148098, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31602.70335569526, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.0023434990433704255, + "median": -0.002210308079502524, + "sigma": 0.00011968780785933937, + "upper_1": -0.0021041234276517468 + }, + "centre_1": { + "lower_1": -9.319660257404176e-05, + "median": -6.09507495149444e-07, + "sigma": 8.900236367202239e-05, + "upper_1": 8.480812477000301e-05 + }, + "einstein_radius": { + "lower_1": 1.6001054687865077, + "median": 1.6001864067704987, + "sigma": 7.17682912561024e-05, + "upper_1": 1.60024900536902 + }, + "ell_comps_0": { + "lower_1": 0.0544820629999719, + "median": 0.0546045799250499, + "sigma": 0.0001364344666146891, + "upper_1": 0.05475493193320128 + }, + "ell_comps_1": { + "lower_1": -0.0025735905154637955, + "median": -0.002377761038827469, + "sigma": 0.0001903269617216294, + "upper_1": -0.0021929365920205367 + }, + "gamma_1": { + "lower_1": 0.04821031877750588, + "median": 0.048339373172820346, + "sigma": 0.000125323814138701, + "upper_1": 0.04846096640578328 + }, + "gamma_2": { + "lower_1": 0.05111257735202107, + "median": 0.0511918843403712, + "sigma": 9.111786511553296e-05, + "upper_1": 0.05129481308225214 + }, + "inner_coefficient": { + "lower_1": 6.884785214407731e-06, + "median": 0.00010341883828371076, + "sigma": 0.0007144551784801779, + "upper_1": 0.0014357951421747636 + }, + "outer_coefficient": { + "lower_1": 63.870241652403294, + "median": 71.02809532272727, + "sigma": 7.332972965991246, + "upper_1": 78.53618758438579 + }, + "shear_magnitude": { + "lower_1": 0.07028320353732255, + "median": 0.07041728863280634, + "sigma": 0.00012563346678510362, + "upper_1": 0.07053447047089276 + }, + "signal_scale": { + "lower_1": 0.07251556269712522, + "median": 0.07997078395407527, + "sigma": 0.008376866008184797, + "upper_1": 0.08926929471349482 + } + }, + "truth_delta_sigma": { + "einstein_radius": 2.5973416286790467, + "gamma_1": -13.250688535075806, + "gamma_2": 13.080687731872889, + "shear_magnitude": -2.3352813016794767 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 4811 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-1ad2b0be53ac3cb8", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "source_pix[1]", + "target": "hst/slam5_delaunay_1250/seed1", + "timings": { + "compile_s": 26.09571358980611, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 5258.141222238541, + "setup_s": null, + "total_s": 5348.343995480798 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed1 | source_pix[2] | 1 | complete | not_assessed | not_assessed | 115.64004683494568 | 182.61417358601466 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-122202734335fe14 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 8, + "free_parameters": 3, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "sparse", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 20, + "n_live": 75, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 2200, + "log_evidence": 31553.642508575576, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31566.565244506914, + "positions_info_present": true, + "posterior": { + "inner_coefficient": { + "lower_1": 5.896953038369445e-06, + "median": 0.00011159014949969803, + "sigma": 0.0009596030676794041, + "upper_1": 0.0019251030883971777 + }, + "outer_coefficient": { + "lower_1": 91.69580810377748, + "median": 108.50434713714664, + "sigma": 13.766780625523417, + "upper_1": 119.22936935482431 + }, + "signal_scale": { + "lower_1": 0.03643706030182262, + "median": 0.040203599160287956, + "sigma": 0.005768159799632119, + "upper_1": 0.04797337990108686 + } + }, + "truth_delta_sigma": {} + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 4811 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-122202734335fe14", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "source_pix[2]", + "target": "hst/slam5_delaunay_1250/seed1", + "timings": { + "compile_s": 9.218201531097293, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 115.64004683494568, + "setup_s": null, + "total_s": 182.61417358601466 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed1 | light[1] | 1 | complete | not_assessed | not_assessed | 379.8856680393219 | 453.2077537388541 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-7f695c3cdb0cdedb + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 8, + "free_parameters": 6, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "sparse", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 10420, + "log_evidence": 31543.169581456375, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31567.32162556375, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.0001716691330299478, + "median": 6.318784366977037e-05, + "sigma": 0.00023236808379187057, + "upper_1": 0.00029306703455379336 + }, + "centre_1": { + "lower_1": -0.0002913156863967421, + "median": -4.751428853652658e-05, + "sigma": 0.00024603512058587443, + "upper_1": 0.00020075455477500676 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.04882450752380965, + "median": 0.0517680519498064, + "sigma": 0.0030292516668652195, + "upper_1": 0.05488301085754009 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.004689586264956041, + "median": -5.063695648058507e-05, + "sigma": 0.005127044119568302, + "upper_1": 0.005564501974180564 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_0": { + "lower_1": -0.1316231052989348, + "median": 0.048300060441984094, + "sigma": 0.1618031936240376, + "upper_1": 0.19198328194914038 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_1": { + "lower_1": -0.15420322119451338, + "median": 0.06289292833579334, + "sigma": 0.2034875313140539, + "upper_1": 0.2527718414335944 + } + }, + "truth_delta_sigma": {} + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 4811 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-7f695c3cdb0cdedb", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "light[1]", + "target": "hst/slam5_delaunay_1250/seed1", + "timings": { + "compile_s": 3.221272853203118, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 379.8856680393219, + "setup_s": null, + "total_s": 453.2077537388541 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5_delaunay_1250/seed1 | mass_total[1] | 1 | complete | not_assessed | not_assessed | 1900.1771788597107 | 1995.9051318671554 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-03dd03936a025726 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 8, + "free_parameters": 8, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "sparse", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 34320, + "log_evidence": 31534.684096516205, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31591.89502805363, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.0008831957480234159, + "median": -0.0005053606747060731, + "sigma": 0.0003987277101635595, + "upper_1": -8.574032769629691e-05 + }, + "centre_1": { + "lower_1": -0.0001937220894514134, + "median": 0.00014681924663258514, + "sigma": 0.0003517218272096474, + "upper_1": 0.0005097215649678814 + }, + "einstein_radius": { + "lower_1": 1.5942230483452215, + "median": 1.5964635169296084, + "sigma": 0.0033773292197144533, + "upper_1": 1.6009777067846505 + }, + "ell_comps_0": { + "lower_1": 0.051175117542454046, + "median": 0.05237094649611395, + "sigma": 0.0015376247425419945, + "upper_1": 0.054250367027538035 + }, + "ell_comps_1": { + "lower_1": -0.002066743520308163, + "median": -0.0011871741400944558, + "sigma": 0.0009125439261035015, + "upper_1": -0.0002416556681011601 + }, + "gamma_1": { + "lower_1": 0.04534566274700057, + "median": 0.046775489775500945, + "sigma": 0.002172159164525605, + "upper_1": 0.04968998107605178 + }, + "gamma_2": { + "lower_1": 0.04957634691166957, + "median": 0.05020075497361776, + "sigma": 0.0007305055077156432, + "upper_1": 0.05103735792710085 + }, + "shear_magnitude": { + "lower_1": 0.06729579914992777, + "median": 0.06866887973469894, + "sigma": 0.0019082734615388508, + "upper_1": 0.07111234607300547 + }, + "slope": { + "lower_1": 1.9233309597008308, + "median": 1.9492868567850423, + "sigma": 0.0399913797271616, + "upper_1": 2.003313719155154 + } + }, + "truth_delta_sigma": { + "einstein_radius": -1.047124174258225, + "gamma_1": -1.4844723522842234, + "gamma_2": 0.27481650924924467, + "shear_magnitude": -1.0699715869387485, + "slope": -1.2681018649755162 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 4811 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-03dd03936a025726", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "mass_total[1]", + "target": "hst/slam5_delaunay_1250/seed1", + "timings": { + "compile_s": 3.3759442511945963, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 1900.1771788597107, + "setup_s": null, + "total_s": 1995.9051318671554 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed0 | unknown | 0 | complete | not_assessed | not_assessed | unknown | unknown | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 4, + "inversion": "dense", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "rect_mesh": "bilinear", + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "missing", + "values": {} + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json" + ], + "execution": { + "completed": null, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 21161 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": null, + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": null, + "target": "hst/slam5/seed0", + "timings": { + "compile_s": null, + "definitions": { + "compile_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "sampling_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "setup_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "total_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + }, + "sampling_s": null, + "setup_s": null, + "total_s": null + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "execution.completed": "No pipeline-wide completion marker is recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result", + "timings": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + } +} +``` + +
+ +| hst/slam5/seed0 | source_lp[1] | 0 | complete | not_assessed | not_assessed | 923.2988793849945 | 1110.2745820698328 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-9b14006556621cdb + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 4, + "free_parameters": 17, + "inversion": "dense", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 50, + "n_live": 200, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": false, + "likelihood_evals": 95950, + "log_evidence": 31968.386044728682, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 32068.389215185896, + "positions_info_present": false, + "posterior": { + "einstein_radius": { + "lower_1": 1.6002315264745468, + "median": 1.600420922014575, + "sigma": 0.00019204058641297816, + "upper_1": 1.6006156076473728 + }, + "galaxies.lens.bulge.profile_list.0.centre_0": { + "lower_1": -8.570949198414911e-05, + "median": 0.00012964954543330016, + "sigma": 0.0002138324822891158, + "upper_1": 0.0003419554725940825 + }, + "galaxies.lens.bulge.profile_list.0.centre_1": { + "lower_1": -0.00024442609130238737, + "median": -3.702744862484822e-05, + "sigma": 0.00021084040173402843, + "upper_1": 0.00017725471216566946 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.04981216227173771, + "median": 0.05224976010236734, + "sigma": 0.0022709141612055617, + "upper_1": 0.05435399059414883 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.005736288017538714, + "median": -0.0018405720511497719, + "sigma": 0.005747872519872762, + "upper_1": 0.005759457022206811 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_0": { + "lower_1": -0.03956054227628692, + "median": 0.04789349509934275, + "sigma": 0.07618103601187999, + "upper_1": 0.11280152974747307 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_1": { + "lower_1": -0.1094315935899517, + "median": 0.09640083506402285, + "sigma": 0.1708050936508713, + "upper_1": 0.2321785937117909 + }, + "galaxies.lens.mass.centre_0": { + "lower_1": -0.0003394661843456817, + "median": -6.871909755583681e-05, + "sigma": 0.0002787781033053865, + "upper_1": 0.00021809002226509132 + }, + "galaxies.lens.mass.centre_1": { + "lower_1": -0.0004731402129938208, + "median": -0.000209057253182255, + "sigma": 0.0002679759176636068, + "upper_1": 6.281162233339276e-05 + }, + "galaxies.lens.mass.ell_comps_0": { + "lower_1": 0.05218007984982602, + "median": 0.05275796220407587, + "sigma": 0.0005836643222685775, + "upper_1": 0.053347408494363174 + }, + "galaxies.lens.mass.ell_comps_1": { + "lower_1": -0.0005035849607098547, + "median": 0.0003989179914858002, + "sigma": 0.0008922912648012084, + "upper_1": 0.0012809975688925623 + }, + "galaxies.source.bulge.profile_list.0.centre_0": { + "lower_1": -0.0005055980619444168, + "median": -0.0002964474797543975, + "sigma": 0.00021170218499254627, + "upper_1": -8.219369195932428e-05 + }, + "galaxies.source.bulge.profile_list.0.centre_1": { + "lower_1": -0.000479247841508605, + "median": -0.00027231801785834313, + "sigma": 0.00019946011928292283, + "upper_1": -8.032760294275934e-05 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.09565167863322123, + "median": 0.09763517574235893, + "sigma": 0.001990148643407054, + "upper_1": 0.09963197592003534 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.05779021403510607, + "median": -0.055846456659172816, + "sigma": 0.0018647889988093122, + "upper_1": -0.054060636037487446 + }, + "gamma_1": { + "lower_1": 0.04964552516572051, + "median": 0.05000659076945474, + "sigma": 0.00035840882376065447, + "upper_1": 0.05036234281324182 + }, + "gamma_2": { + "lower_1": 0.04979766351035257, + "median": 0.050113807348712736, + "sigma": 0.0003209503946176237, + "upper_1": 0.05043956429958782 + }, + "shear_magnitude": { + "lower_1": 0.070454613260425, + "median": 0.07079852109494138, + "sigma": 0.0003406579945389246, + "upper_1": 0.07113592924950285 + } + }, + "truth_delta_sigma": { + "einstein_radius": 2.1918388317655757, + "gamma_1": 0.01838897096779659, + "gamma_2": 0.35459482406407833, + "shear_magnitude": 0.2578626590152563 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 21161 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-9b14006556621cdb", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "source_lp[1]", + "target": "hst/slam5/seed0", + "timings": { + "compile_s": 12.766160851810127, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 923.2988793849945, + "setup_s": null, + "total_s": 1110.2745820698328 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed0 | source_pix[1] | 0 | complete | not_assessed | not_assessed | 761.8690450191498 | 1096.6811571302824 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-1ad2b0be53ac3cb8 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 4, + "free_parameters": 10, + "inversion": "dense", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 32820, + "log_evidence": 31418.342975536092, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31485.45228258444, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.0003713942210326155, + "median": -4.3100361904935115e-05, + "sigma": 0.00031013944016111656, + "upper_1": 0.00024888465928961763 + }, + "centre_1": { + "lower_1": -0.0005816332836229778, + "median": -0.00022525057403800737, + "sigma": 0.0003488890831749694, + "upper_1": 0.00011614488272696113 + }, + "einstein_radius": { + "lower_1": 1.6003156275585906, + "median": 1.6005605530057556, + "sigma": 0.0002220686545053585, + "upper_1": 1.6007597648676013 + }, + "ell_comps_0": { + "lower_1": 0.050854997199662355, + "median": 0.05138163255840881, + "sigma": 0.0004668786185344706, + "upper_1": 0.051788754436731296 + }, + "ell_comps_1": { + "lower_1": -0.0005456150651527865, + "median": 0.0005945872733467307, + "sigma": 0.0009474334842610951, + "upper_1": 0.0013492519033694037 + }, + "gamma_1": { + "lower_1": 0.04995785608920304, + "median": 0.050413115820942475, + "sigma": 0.00040864403204366254, + "upper_1": 0.05077514415329037 + }, + "gamma_2": { + "lower_1": 0.049149391096537556, + "median": 0.04940638116398585, + "sigma": 0.0002520401286923851, + "upper_1": 0.04965347135392233 + }, + "inner_coefficient": { + "lower_1": 3.569784997381567e-06, + "median": 3.931765599797968e-05, + "sigma": 0.00023528859834182354, + "upper_1": 0.00047414698168102867 + }, + "outer_coefficient": { + "lower_1": 115.58390939363538, + "median": 147.9303553382513, + "sigma": 34.297264739242145, + "upper_1": 184.17843887211967 + }, + "shear_magnitude": { + "lower_1": 0.07023106946425157, + "median": 0.07059210594586793, + "sigma": 0.0003123029352482612, + "upper_1": 0.0708556753347481 + }, + "signal_scale": { + "lower_1": 0.017347581391335316, + "median": 0.021776128279219018, + "sigma": 0.005333443225361211, + "upper_1": 0.028014467842057737 + } + }, + "truth_delta_sigma": { + "einstein_radius": 2.5242329089808586, + "gamma_1": 1.0109429932854912, + "gamma_2": -2.35525524881263, + "shear_magnitude": -0.3796703757925948 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 21161 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-1ad2b0be53ac3cb8", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "source_pix[1]", + "target": "hst/slam5/seed0", + "timings": { + "compile_s": 20.18864746298641, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 761.8690450191498, + "setup_s": null, + "total_s": 1096.6811571302824 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed0 | source_pix[2] | 0 | complete | not_assessed | not_assessed | 131.4924976825714 | 457.5962234721519 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-122202734335fe14 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 4, + "free_parameters": 5, + "inversion": "dense", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 20, + "n_live": 75, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 3720, + "log_evidence": 31618.430168835894, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31635.592564226194, + "positions_info_present": true, + "posterior": { + "inner_coefficient": { + "lower_1": 6.281966410875598e-06, + "median": 0.00016023687783804248, + "sigma": 0.001628111202274457, + "upper_1": 0.0032625043709597896 + }, + "outer_coefficient": { + "lower_1": 54.14473429617261, + "median": 67.09708287526408, + "sigma": 11.135247987958852, + "upper_1": 76.41523027209031 + }, + "signal_scale": { + "lower_1": 0.04002635298307532, + "median": 0.04648908420667702, + "sigma": 0.00861617761117521, + "upper_1": 0.05725870820542574 + }, + "weight_floor": { + "lower_1": 5.851911985593018e-05, + "median": 0.0012303177425306202, + "sigma": 0.013270740806733846, + "upper_1": 0.026600000733323623 + }, + "weight_power": { + "lower_1": 4.097816350649761, + "median": 4.137351288460505, + "sigma": 0.046548413950431744, + "upper_1": 4.190913178550624 + } + }, + "truth_delta_sigma": {} + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 21161 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-122202734335fe14", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "source_pix[2]", + "target": "hst/slam5/seed0", + "timings": { + "compile_s": 8.859329219907522, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 131.4924976825714, + "setup_s": null, + "total_s": 457.5962234721519 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed0 | light[1] | 0 | complete | not_assessed | not_assessed | 237.3431966304779 | 585.0465070647188 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-7f695c3cdb0cdedb + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 4, + "free_parameters": 6, + "inversion": "dense", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 9620, + "log_evidence": 31611.07272571175, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31636.620639101693, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.00012215537211273652, + "median": 0.00010261141265597484, + "sigma": 0.00022948297939342957, + "upper_1": 0.0003368105866741226 + }, + "centre_1": { + "lower_1": -0.00024008402422401494, + "median": -3.096272887924275e-05, + "sigma": 0.00021677051863774492, + "upper_1": 0.00019345701305147487 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.04964855293509654, + "median": 0.05244308389421658, + "sigma": 0.0028839792585326103, + "upper_1": 0.05541651145216176 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.005349857163688041, + "median": -0.0009622496260501869, + "sigma": 0.00474486570139695, + "upper_1": 0.004139874239105859 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_0": { + "lower_1": -0.16332320065508527, + "median": 0.02824787382488417, + "sigma": 0.15655125481883375, + "upper_1": 0.14977930898258227 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_1": { + "lower_1": -0.13997514635091912, + "median": 0.08330755588674374, + "sigma": 0.17800108874284065, + "upper_1": 0.21602703113476215 + } + }, + "truth_delta_sigma": {} + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 21161 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-7f695c3cdb0cdedb", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "light[1]", + "target": "hst/slam5/seed0", + "timings": { + "compile_s": 2.803400441072881, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 237.3431966304779, + "setup_s": null, + "total_s": 585.0465070647188 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed0 | mass_total[1] | 0 | complete | not_assessed | not_assessed | 516.6248600482941 | 894.5935196429491 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-03dd03936a025726 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 4, + "free_parameters": 8, + "inversion": "dense", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 20080, + "log_evidence": 31592.704599557084, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31643.162863509424, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.0006686468833050859, + "median": -0.00010927108221072791, + "sigma": 0.0005804830693026889, + "upper_1": 0.0004923192553002919 + }, + "centre_1": { + "lower_1": -0.0009991086311915375, + "median": -0.0005401548624380931, + "sigma": 0.0004440872964930744, + "upper_1": -0.00011093403820538862 + }, + "einstein_radius": { + "lower_1": 1.6006368609077655, + "median": 1.6049087908041972, + "sigma": 0.004061952109819966, + "upper_1": 1.6087607651274054 + }, + "ell_comps_0": { + "lower_1": 0.05262391406386012, + "median": 0.05505869834243167, + "sigma": 0.0024228240870309246, + "upper_1": 0.05746956223792197 + }, + "ell_comps_1": { + "lower_1": -0.0016390775393614109, + "median": -0.0007526446254683475, + "sigma": 0.0009283115882590601, + "upper_1": 0.0002175456371567094 + }, + "gamma_1": { + "lower_1": 0.04925412700988009, + "median": 0.05204855223151093, + "sigma": 0.002696931131246711, + "upper_1": 0.054647989272373514 + }, + "gamma_2": { + "lower_1": 0.05007696923508563, + "median": 0.05106247176786417, + "sigma": 0.0009770997140994275, + "upper_1": 0.052031168663284486 + }, + "shear_magnitude": { + "lower_1": 0.0703033807320544, + "median": 0.07295200274522938, + "sigma": 0.002530600082597062, + "upper_1": 0.07536458089724853 + }, + "slope": { + "lower_1": 2.0012420378949107, + "median": 2.0542236286613327, + "sigma": 0.05113485098739323, + "upper_1": 2.103511739869697 + } + }, + "truth_delta_sigma": { + "einstein_radius": 1.2084807185022959, + "gamma_1": 0.7595864083351457, + "gamma_2": 1.0873729185801968, + "shear_magnitude": 0.885688988152733, + "slope": 1.0604045502098158 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 21161 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json#stage-03dd03936a025726", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "mass_total[1]", + "target": "hst/slam5/seed0", + "timings": { + "compile_s": 2.8257059240713716, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 516.6248600482941, + "setup_s": null, + "total_s": 894.5935196429491 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed1 | unknown | 1 | complete | not_assessed | not_assessed | unknown | unknown | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 4, + "inversion": "dense", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "rect_mesh": "bilinear", + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "missing", + "values": {} + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json" + ], + "execution": { + "completed": null, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 21161 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": null, + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": null, + "target": "hst/slam5/seed1", + "timings": { + "compile_s": null, + "definitions": { + "compile_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "sampling_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "setup_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "total_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + }, + "sampling_s": null, + "setup_s": null, + "total_s": null + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "execution.completed": "No pipeline-wide completion marker is recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result", + "timings": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + } +} +``` + +
+ +| hst/slam5/seed1 | source_lp[1] | 1 | complete | not_assessed | not_assessed | 834.8256509304047 | 1005.474559458904 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-9b14006556621cdb + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 4, + "free_parameters": 17, + "inversion": "dense", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 50, + "n_live": 200, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": false, + "likelihood_evals": 96150, + "log_evidence": 31968.39809871757, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 32068.982027746282, + "positions_info_present": false, + "posterior": { + "einstein_radius": { + "lower_1": 1.6002362709146263, + "median": 1.6004330404893516, + "sigma": 0.00019687787094069265, + "upper_1": 1.6006300266565077 + }, + "galaxies.lens.bulge.profile_list.0.centre_0": { + "lower_1": -7.421079072928782e-05, + "median": 0.00014493976271904479, + "sigma": 0.00021889751939202455, + "upper_1": 0.00036358424805476127 + }, + "galaxies.lens.bulge.profile_list.0.centre_1": { + "lower_1": -0.0002592968802040309, + "median": -4.2966535910439325e-05, + "sigma": 0.00021470232028180546, + "upper_1": 0.00017010776035957998 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.05034656059597934, + "median": 0.052576702786680835, + "sigma": 0.002089973939331191, + "upper_1": 0.05452650847464172 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.0069854792539558825, + "median": -0.002629706161272888, + "sigma": 0.005468850576939192, + "upper_1": 0.003952221899922502 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_0": { + "lower_1": -0.03752093037104172, + "median": 0.04073258900351603, + "sigma": 0.07079365610670536, + "upper_1": 0.10406638184236901 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_1": { + "lower_1": -0.10375870362521862, + "median": 0.09775059592875336, + "sigma": 0.16048420100547792, + "upper_1": 0.2172096983857372 + }, + "galaxies.lens.mass.centre_0": { + "lower_1": -0.0003365805982999102, + "median": -6.029552936326054e-05, + "sigma": 0.00027584041740488644, + "upper_1": 0.0002151002365098626 + }, + "galaxies.lens.mass.centre_1": { + "lower_1": -0.00047566808681698985, + "median": -0.00021473702931451628, + "sigma": 0.0002611020904992631, + "upper_1": 4.653609418153641e-05 + }, + "galaxies.lens.mass.ell_comps_0": { + "lower_1": 0.0522040659673803, + "median": 0.052786188882794405, + "sigma": 0.0006021567624035426, + "upper_1": 0.05340837949218739 + }, + "galaxies.lens.mass.ell_comps_1": { + "lower_1": -0.0004832277792389551, + "median": 0.00047047006461038737, + "sigma": 0.0009363822738062476, + "upper_1": 0.0013895367683735402 + }, + "galaxies.source.bulge.profile_list.0.centre_0": { + "lower_1": -0.0005195545320079, + "median": -0.00030253172613471733, + "sigma": 0.0002196695345341109, + "upper_1": -8.021546293967816e-05 + }, + "galaxies.source.bulge.profile_list.0.centre_1": { + "lower_1": -0.0004881751003846659, + "median": -0.00028258938111247125, + "sigma": 0.000205854600774334, + "upper_1": -7.64658988359979e-05 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.0957823792742703, + "median": 0.09778010250507774, + "sigma": 0.001978303270745309, + "upper_1": 0.09973898581576092 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.05774976146955322, + "median": -0.055851245468715546, + "sigma": 0.0018637676486647978, + "upper_1": -0.054022226172223624 + }, + "gamma_1": { + "lower_1": 0.04965969993347949, + "median": 0.050038345737619054, + "sigma": 0.00037469282845273635, + "upper_1": 0.05040908559038496 + }, + "gamma_2": { + "lower_1": 0.04980667595654918, + "median": 0.05012259522806425, + "sigma": 0.000333250368771177, + "upper_1": 0.050473176694091536 + }, + "shear_magnitude": { + "lower_1": 0.07048556851602046, + "median": 0.07082893568824743, + "sigma": 0.0003455595719765031, + "upper_1": 0.07117668765997347 + } + }, + "truth_delta_sigma": { + "einstein_radius": 2.199538664667924, + "gamma_1": 0.10233912876688063, + "gamma_2": 0.36787724651678627, + "shear_magnitude": 0.34222050026356216 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 21161 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-9b14006556621cdb", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "source_lp[1]", + "target": "hst/slam5/seed1", + "timings": { + "compile_s": 11.619808046612889, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 834.8256509304047, + "setup_s": null, + "total_s": 1005.474559458904 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed1 | source_pix[1] | 1 | complete | not_assessed | not_assessed | 748.7339024543762 | 1097.2968472116627 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-1ad2b0be53ac3cb8 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 4, + "free_parameters": 10, + "inversion": "dense", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 33000, + "log_evidence": 31437.88785216006, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31503.7936052073, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.00044100799256384187, + "median": -0.0001018589638662014, + "sigma": 0.00032596844107615086, + "upper_1": 0.00021092888958845987 + }, + "centre_1": { + "lower_1": -0.0006022223500377452, + "median": -0.00024378911944909946, + "sigma": 0.00035384770544807443, + "upper_1": 0.00010547306085840364 + }, + "einstein_radius": { + "lower_1": 1.6002292654071566, + "median": 1.600466872766326, + "sigma": 0.00024198437961842068, + "upper_1": 1.6007132341663934 + }, + "ell_comps_0": { + "lower_1": 0.05089293250656399, + "median": 0.05133818989401086, + "sigma": 0.0004631419359047463, + "upper_1": 0.05181921637837348 + }, + "ell_comps_1": { + "lower_1": -0.0008457526137596467, + "median": 0.0002547612143009781, + "sigma": 0.0010657989465323564, + "upper_1": 0.001285845279305066 + }, + "gamma_1": { + "lower_1": 0.04987237814443666, + "median": 0.05031356404127446, + "sigma": 0.0004381809810156054, + "upper_1": 0.05074874010646787 + }, + "gamma_2": { + "lower_1": 0.049205921787489955, + "median": 0.04943192287690121, + "sigma": 0.0002454474559456937, + "upper_1": 0.04969681669938134 + }, + "inner_coefficient": { + "lower_1": 6.277243928240445e-06, + "median": 8.278598139908959e-05, + "sigma": 0.0004372170003276448, + "upper_1": 0.00088071124458353 + }, + "outer_coefficient": { + "lower_1": 114.32274438532693, + "median": 145.1108471269094, + "sigma": 31.70789561750871, + "upper_1": 177.73853562034435 + }, + "shear_magnitude": { + "lower_1": 0.07020063812471372, + "median": 0.07053649465748915, + "sigma": 0.0003441491408217173, + "upper_1": 0.07088893640635716 + }, + "signal_scale": { + "lower_1": 0.017798260446834798, + "median": 0.022001555619210057, + "sigma": 0.0050675847905214556, + "upper_1": 0.02793343002787771 + } + }, + "truth_delta_sigma": { + "einstein_radius": 1.9293508410012026, + "gamma_1": 0.7156039510151425, + "gamma_2": -2.314455128125201, + "shear_magnitude": -0.5061278396619558 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 21161 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-1ad2b0be53ac3cb8", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "source_pix[1]", + "target": "hst/slam5/seed1", + "timings": { + "compile_s": 17.360391543246806, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 748.7339024543762, + "setup_s": null, + "total_s": 1097.2968472116627 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed1 | source_pix[2] | 1 | complete | not_assessed | not_assessed | 362.2842011451721 | 719.2182216290385 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-122202734335fe14 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 4, + "free_parameters": 5, + "inversion": "dense", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 20, + "n_live": 75, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 13800, + "log_evidence": 31620.542784113066, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31641.665228127415, + "positions_info_present": true, + "posterior": { + "inner_coefficient": { + "lower_1": 4.441640838870867e-06, + "median": 4.018084423264131e-05, + "sigma": 0.0005114447136146241, + "upper_1": 0.001027331068068119 + }, + "outer_coefficient": { + "lower_1": 57.060907749048866, + "median": 69.91860238797418, + "sigma": 9.76218791030103, + "upper_1": 76.58528356965093 + }, + "signal_scale": { + "lower_1": 0.039647125874696626, + "median": 0.04917942399048276, + "sigma": 0.008228250978201103, + "upper_1": 0.05610362783109883 + }, + "weight_floor": { + "lower_1": 8.314544617554278e-05, + "median": 0.0022583585489797313, + "sigma": 0.02199212332352126, + "upper_1": 0.04406739209321806 + }, + "weight_power": { + "lower_1": 1.1036774523871884, + "median": 3.9971832836259056, + "sigma": 1.4780810895584362, + "upper_1": 4.059839631504061 + } + }, + "truth_delta_sigma": {} + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 21161 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-122202734335fe14", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "source_pix[2]", + "target": "hst/slam5/seed1", + "timings": { + "compile_s": 7.912892768159509, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 362.2842011451721, + "setup_s": null, + "total_s": 719.2182216290385 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed1 | light[1] | 1 | complete | not_assessed | not_assessed | 267.35919713974 | 618.7239170190878 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-7f695c3cdb0cdedb + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 4, + "free_parameters": 6, + "inversion": "dense", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 12200, + "log_evidence": 31618.606880476535, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31643.073765432557, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.0001305451529587777, + "median": 0.00011219098953143217, + "sigma": 0.00023744131455621231, + "upper_1": 0.0003443374761536469 + }, + "centre_1": { + "lower_1": -0.00026851862986866247, + "median": -4.2409071231396995e-05, + "sigma": 0.00023817915714012154, + "upper_1": 0.00020783968441158058 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.018458110510601953, + "median": 0.05164870789347828, + "sigma": 0.03438648925423287, + "upper_1": 0.08723108901906769 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.06773296915893498, + "median": 0.012059116373161642, + "sigma": 0.0857913297806614, + "upper_1": 0.1038496904023878 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_0": { + "lower_1": 0.04189608282323192, + "median": 0.05205101070232318, + "sigma": 0.01123555781666893, + "upper_1": 0.06436719845656978 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_1": { + "lower_1": -0.025649931761376824, + "median": -0.0030098089681762853, + "sigma": 0.029127062824454777, + "upper_1": 0.03260419388753273 + } + }, + "truth_delta_sigma": {} + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 21161 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-7f695c3cdb0cdedb", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "light[1]", + "target": "hst/slam5/seed1", + "timings": { + "compile_s": 2.847682393155992, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 267.35919713974, + "setup_s": null, + "total_s": 618.7239170190878 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed1 | mass_total[1] | 1 | complete | not_assessed | not_assessed | 650.228832244873 | 1043.753492906224 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-03dd03936a025726 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 4, + "free_parameters": 8, + "inversion": "dense", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 25820, + "log_evidence": 31596.111410634843, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31652.104493221035, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.0011410035726914897, + "median": -0.0008512862629642314, + "sigma": 0.0003134643772881501, + "upper_1": -0.0005140748181151895 + }, + "centre_1": { + "lower_1": -0.0004340098669439938, + "median": -8.889159606349263e-05, + "sigma": 0.00032092548326223606, + "upper_1": 0.0002078410995804783 + }, + "einstein_radius": { + "lower_1": 1.598894454212682, + "median": 1.6013711655231428, + "sigma": 0.003929671211249874, + "upper_1": 1.6067537966351817 + }, + "ell_comps_0": { + "lower_1": 0.05231174867931312, + "median": 0.05365330394440513, + "sigma": 0.002198711380422904, + "upper_1": 0.056709171440158926 + }, + "ell_comps_1": { + "lower_1": 0.0004564194896633493, + "median": 0.001320838538475745, + "sigma": 0.0008148364760195597, + "upper_1": 0.0020860924417024688 + }, + "gamma_1": { + "lower_1": 0.04932093060536126, + "median": 0.05074616204725169, + "sigma": 0.0022240810271616576, + "upper_1": 0.053769092659684575 + }, + "gamma_2": { + "lower_1": 0.04990407027458321, + "median": 0.05052130870112935, + "sigma": 0.0009334983340669456, + "upper_1": 0.0517710669427171 + }, + "shear_magnitude": { + "lower_1": 0.07022152478323705, + "median": 0.07160577413427568, + "sigma": 0.0022206438346625626, + "upper_1": 0.07466281245256218 + }, + "slope": { + "lower_1": 1.9767129571916644, + "median": 2.0076537650934814, + "sigma": 0.04880067451273862, + "upper_1": 2.0743143062171416 + } + }, + "truth_delta_sigma": { + "einstein_radius": 0.348926271291446, + "gamma_1": 0.33549229463277797, + "gamma_2": 0.5584463111553465, + "shear_magnitude": 0.40307950408307297, + "slope": 0.15683728083478596 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 21161 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json#stage-03dd03936a025726", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "mass_total[1]", + "target": "hst/slam5/seed1", + "timings": { + "compile_s": 2.9197389110922813, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 650.228832244873, + "setup_s": null, + "total_s": 1043.753492906224 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed0 | unknown | 0 | stopped_early | not_assessed | not_assessed | unknown | unknown | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64__rate_probe_342695/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64__rate_probe_342695/stages_seed0.json + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 4, + "inversion": "dense", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "rect_mesh": "bilinear", + "stages_requested": "source_lp", + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "missing", + "values": {} + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64__rate_probe_342695/stages_seed0.json" + ], + "execution": { + "completed": null, + "reason": null, + "status": "stopped_early" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 2621 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64__rate_probe_342695/stages_seed0.json", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": null, + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": null, + "target": "hst/slam5/seed0", + "timings": { + "compile_s": null, + "definitions": { + "compile_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "sampling_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "setup_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "total_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + }, + "sampling_s": null, + "setup_s": null, + "total_s": null + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "execution.completed": "No pipeline-wide completion marker is recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result", + "timings": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + } +} +``` + +
+ +| hst/slam5/seed0 | source_lp[1] | 0 | complete | not_assessed | not_assessed | 855.3723487854004 | 1041.4455565372482 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64__rate_probe_342695/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64__rate_probe_342695/stages_seed0.json#stage-9b14006556621cdb + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 4, + "free_parameters": 17, + "inversion": "dense", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 50, + "n_live": 200, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": "source_lp", + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": false, + "likelihood_evals": 93550, + "log_evidence": 31968.373315680932, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 32068.952581889233, + "positions_info_present": false, + "posterior": { + "einstein_radius": { + "lower_1": 1.600228782817256, + "median": 1.600430536499548, + "sigma": 0.00019536429087796403, + "upper_1": 1.6006195113990118 + }, + "galaxies.lens.bulge.profile_list.0.centre_0": { + "lower_1": -7.795055350320109e-05, + "median": 0.00013882964952930326, + "sigma": 0.00022079567331118727, + "upper_1": 0.00036364079311917345 + }, + "galaxies.lens.bulge.profile_list.0.centre_1": { + "lower_1": -0.00024021133106382324, + "median": -2.8904558197248365e-05, + "sigma": 0.00021715332685679563, + "upper_1": 0.00019409532264976805 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_0": { + "lower_1": -0.04083790108690556, + "median": 0.04209752996451113, + "sigma": 0.0809871678115365, + "upper_1": 0.12113643453616744 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.12498015498691048, + "median": 0.0912251506727394, + "sigma": 0.16881322417070774, + "upper_1": 0.212646293354505 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_0": { + "lower_1": 0.05006853082082759, + "median": 0.05245959687559936, + "sigma": 0.0022046410067881085, + "upper_1": 0.05447781283440381 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_1": { + "lower_1": -0.006024296508881658, + "median": -0.0019378583407990606, + "sigma": 0.005096152405066287, + "upper_1": 0.004168008301250917 + }, + "galaxies.lens.mass.centre_0": { + "lower_1": -0.0003575645761629297, + "median": -7.3101993217768e-05, + "sigma": 0.00028727374257408816, + "upper_1": 0.00021698290898524667 + }, + "galaxies.lens.mass.centre_1": { + "lower_1": -0.0004613111056668821, + "median": -0.00020490622759399859, + "sigma": 0.00026219208347577763, + "upper_1": 6.307306128467312e-05 + }, + "galaxies.lens.mass.ell_comps_0": { + "lower_1": 0.05215143321275345, + "median": 0.05274464994664577, + "sigma": 0.0006080853076976048, + "upper_1": 0.05336760382814866 + }, + "galaxies.lens.mass.ell_comps_1": { + "lower_1": -0.000525595129881913, + "median": 0.0003963695500667964, + "sigma": 0.0009313837461711766, + "upper_1": 0.0013371723624604403 + }, + "galaxies.source.bulge.profile_list.0.centre_0": { + "lower_1": -0.0005317158653739457, + "median": -0.0003082459302901089, + "sigma": 0.00022716170435702009, + "upper_1": -7.73924566599055e-05 + }, + "galaxies.source.bulge.profile_list.0.centre_1": { + "lower_1": -0.00047262244198407105, + "median": -0.00026936950259433864, + "sigma": 0.00020341279065880672, + "upper_1": -6.579686066645762e-05 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.09551567263815622, + "median": 0.09754245804139765, + "sigma": 0.0020459794865402697, + "upper_1": 0.09960763161123676 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.057797296227140305, + "median": -0.05584462724230999, + "sigma": 0.0019018791293077333, + "upper_1": -0.05399353796852484 + }, + "gamma_1": { + "lower_1": 0.049644736982510825, + "median": 0.0500213276687916, + "sigma": 0.0003691627904977915, + "upper_1": 0.05038306256350641 + }, + "gamma_2": { + "lower_1": 0.049786861519693776, + "median": 0.05011281267247789, + "sigma": 0.0003326620505857171, + "upper_1": 0.05045218562086521 + }, + "shear_magnitude": { + "lower_1": 0.07045384371122906, + "median": 0.07080256784111373, + "sigma": 0.00035478171653022944, + "upper_1": 0.07116340714428952 + } + }, + "truth_delta_sigma": { + "einstein_radius": 2.203762507534026, + "gamma_1": 0.057773072857210464, + "gamma_2": 0.3391209555741652, + "shear_magnitude": 0.2590035454973506 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64__rate_probe_342695/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 2621 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64__rate_probe_342695/stages_seed0.json#stage-9b14006556621cdb", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_dense_fp64__rate_probe_342695/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "source_lp[1]", + "target": "hst/slam5/seed0", + "timings": { + "compile_s": 11.759456879924983, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 855.3723487854004, + "setup_s": null, + "total_s": 1041.4455565372482 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed0 | unknown | 0 | complete | not_assessed | not_assessed | unknown | unknown | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 4, + "inversion": "sparse", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "rect_mesh": "bilinear", + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "missing", + "values": {} + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json" + ], + "execution": { + "completed": null, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 8903 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": null, + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": null, + "target": "hst/slam5/seed0", + "timings": { + "compile_s": null, + "definitions": { + "compile_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "sampling_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "setup_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "total_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + }, + "sampling_s": null, + "setup_s": null, + "total_s": null + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "execution.completed": "No pipeline-wide completion marker is recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result", + "timings": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + } +} +``` + +
+ +| hst/slam5/seed0 | source_lp[1] | 0 | complete | not_assessed | not_assessed | 808.1857154369354 | 960.966922598891 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-9b14006556621cdb + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 4, + "free_parameters": 17, + "inversion": "sparse", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 50, + "n_live": 200, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": false, + "likelihood_evals": 92300, + "log_evidence": 31968.280867852878, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 32068.445549776923, + "positions_info_present": false, + "posterior": { + "einstein_radius": { + "lower_1": 1.600229863575926, + "median": 1.6004290733633844, + "sigma": 0.0001979087978316585, + "upper_1": 1.6006256811715893 + }, + "galaxies.lens.bulge.profile_list.0.centre_0": { + "lower_1": -8.614260976967795e-05, + "median": 0.00012857543803972436, + "sigma": 0.00021717139527496402, + "upper_1": 0.0003482001807802501 + }, + "galaxies.lens.bulge.profile_list.0.centre_1": { + "lower_1": -0.0002426472716903476, + "median": -2.9664492380187986e-05, + "sigma": 0.0002167737138912927, + "upper_1": 0.0001909001560922378 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_0": { + "lower_1": -0.04743287829371193, + "median": 0.052270784494966215, + "sigma": 0.08450181669090955, + "upper_1": 0.12157075508810716 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.13185252727270227, + "median": 0.07264934632155975, + "sigma": 0.17545355760262493, + "upper_1": 0.21905458793254756 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_0": { + "lower_1": 0.04941004303303117, + "median": 0.052033612649673086, + "sigma": 0.0024167562157134254, + "upper_1": 0.05424355546445802 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_1": { + "lower_1": -0.0048716833614554705, + "median": -0.0009039325387393178, + "sigma": 0.005478870228910195, + "upper_1": 0.006086057096364918 + }, + "galaxies.lens.mass.centre_0": { + "lower_1": -0.00033298690730244814, + "median": -5.53318768487472e-05, + "sigma": 0.000282886580995335, + "upper_1": 0.00023278625468822186 + }, + "galaxies.lens.mass.centre_1": { + "lower_1": -0.00046804418273239793, + "median": -0.00019940694834753186, + "sigma": 0.00026547434018271037, + "upper_1": 6.290449763302278e-05 + }, + "galaxies.lens.mass.ell_comps_0": { + "lower_1": 0.052120821569982614, + "median": 0.05275973875110844, + "sigma": 0.0006400817088837718, + "upper_1": 0.05340098498775016 + }, + "galaxies.lens.mass.ell_comps_1": { + "lower_1": -0.0005538883707651721, + "median": 0.0003081621893727426, + "sigma": 0.0008986642850942904, + "upper_1": 0.0012434401994234087 + }, + "galaxies.source.bulge.profile_list.0.centre_0": { + "lower_1": -0.0005144347342842435, + "median": -0.00028971485087687454, + "sigma": 0.00022808488227635132, + "upper_1": -5.826496973154081e-05 + }, + "galaxies.source.bulge.profile_list.0.centre_1": { + "lower_1": -0.0004706865054172802, + "median": -0.00027193490953312957, + "sigma": 0.0002013429246059668, + "upper_1": -6.800065620534657e-05 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.0955692588216893, + "median": 0.09759358318215168, + "sigma": 0.0020346336141676344, + "upper_1": 0.09963852605002457 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.057662150730918245, + "median": -0.05579997929728687, + "sigma": 0.0018180353781576744, + "upper_1": -0.054026079974602896 + }, + "gamma_1": { + "lower_1": 0.049629391416342955, + "median": 0.04998328672146354, + "sigma": 0.00036149604206179436, + "upper_1": 0.050352383500466544 + }, + "gamma_2": { + "lower_1": 0.04976947238534071, + "median": 0.050118848072839436, + "sigma": 0.00034803280196503417, + "upper_1": 0.05046553798927078 + }, + "shear_magnitude": { + "lower_1": 0.07042525494151729, + "median": 0.0707835517834953, + "sigma": 0.0003606362643555003, + "upper_1": 0.07114652747022829 + } + }, + "truth_delta_sigma": { + "einstein_radius": 2.168035822991758, + "gamma_1": -0.04623364184331053, + "gamma_2": 0.34148526279248237, + "shear_magnitude": 0.20206970857677 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 8903 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-9b14006556621cdb", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "source_lp[1]", + "target": "hst/slam5/seed0", + "timings": { + "compile_s": 11.69923901092261, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 808.1857154369354, + "setup_s": null, + "total_s": 960.966922598891 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed0 | source_pix[1] | 0 | complete | not_assessed | not_assessed | 1930.764386177063 | 2279.775213385001 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-1ad2b0be53ac3cb8 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 4, + "free_parameters": 10, + "inversion": "sparse", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 33140, + "log_evidence": 31422.23418822841, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31489.08888776055, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.00039791100162688947, + "median": -7.332668618440976e-05, + "sigma": 0.000313448699642722, + "upper_1": 0.0002289863976585545 + }, + "centre_1": { + "lower_1": -0.0005865277710890627, + "median": -0.0002615011590903586, + "sigma": 0.0003283936225472876, + "upper_1": 7.025947400551253e-05 + }, + "einstein_radius": { + "lower_1": 1.600237221913805, + "median": 1.600470539295235, + "sigma": 0.00022745745208097023, + "upper_1": 1.600692136817967 + }, + "ell_comps_0": { + "lower_1": 0.050854312603666754, + "median": 0.051356661364769414, + "sigma": 0.0004983624513545115, + "upper_1": 0.05185103750637578 + }, + "ell_comps_1": { + "lower_1": -0.0008430617028146567, + "median": 0.0002392329055113905, + "sigma": 0.001053392282751811, + "upper_1": 0.001263722862688965 + }, + "gamma_1": { + "lower_1": 0.04988413152322257, + "median": 0.05032404847272736, + "sigma": 0.0004235405741904383, + "upper_1": 0.050731212671603444 + }, + "gamma_2": { + "lower_1": 0.04915888887562886, + "median": 0.04942846696495698, + "sigma": 0.00027885835908657286, + "upper_1": 0.049716605593802005 + }, + "inner_coefficient": { + "lower_1": 4.016685182615919e-06, + "median": 5.041023333984501e-05, + "sigma": 0.0003617845878049112, + "upper_1": 0.0007275858607924384 + }, + "outer_coefficient": { + "lower_1": 107.94686058431748, + "median": 135.06236537342357, + "sigma": 26.03297461871452, + "upper_1": 160.01280982174652 + }, + "shear_magnitude": { + "lower_1": 0.07017809454358624, + "median": 0.07054982304184312, + "sigma": 0.0003487097765991834, + "upper_1": 0.07087551409678461 + }, + "signal_scale": { + "lower_1": 0.019884848134068253, + "median": 0.02382772665413446, + "sigma": 0.0049764747220108554, + "upper_1": 0.029837797578089964 + } + }, + "truth_delta_sigma": { + "einstein_radius": 2.068691489023546, + "gamma_1": 0.7650942848787243, + "gamma_2": -2.049546002189549, + "shear_magnitude": -0.46128639804823435 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 8903 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-1ad2b0be53ac3cb8", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "source_pix[1]", + "target": "hst/slam5/seed0", + "timings": { + "compile_s": 20.21704276604578, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 1930.764386177063, + "setup_s": null, + "total_s": 2279.775213385001 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed0 | source_pix[2] | 0 | complete | not_assessed | not_assessed | 178.61591696739197 | 535.9517216398381 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-122202734335fe14 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 4, + "free_parameters": 5, + "inversion": "sparse", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 20, + "n_live": 75, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 3260, + "log_evidence": 31615.926126596125, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31633.55600511511, + "positions_info_present": true, + "posterior": { + "inner_coefficient": { + "lower_1": 5.026005634838336e-06, + "median": 0.00016106495551258316, + "sigma": 0.00197283679409024, + "upper_1": 0.003950699593815318 + }, + "outer_coefficient": { + "lower_1": 34.136668837510854, + "median": 38.10496820415385, + "sigma": 3.1530491777730703, + "upper_1": 40.442767193056994 + }, + "signal_scale": { + "lower_1": 0.07495627913991262, + "median": 0.0807987228191902, + "sigma": 0.008216000056908576, + "upper_1": 0.09138827925372978 + }, + "weight_floor": { + "lower_1": 6.398493879092095e-05, + "median": 0.0012804808023735756, + "sigma": 0.012466578435110561, + "upper_1": 0.024997141809012042 + }, + "weight_power": { + "lower_1": 4.1082101473022785, + "median": 4.191938733518657, + "sigma": 0.06508183735499928, + "upper_1": 4.238373822012277 + } + }, + "truth_delta_sigma": {} + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 8903 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-122202734335fe14", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "source_pix[2]", + "target": "hst/slam5/seed0", + "timings": { + "compile_s": 9.113326458726078, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 178.61591696739197, + "setup_s": null, + "total_s": 535.9517216398381 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed0 | light[1] | 0 | complete | not_assessed | not_assessed | 267.51912546157837 | 621.3747416636907 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-7f695c3cdb0cdedb + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 4, + "free_parameters": 6, + "inversion": "sparse", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 11320, + "log_evidence": 31608.94089884179, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31634.26514523622, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.00011563944795123819, + "median": 0.00012396700120934176, + "sigma": 0.00023273824106364847, + "upper_1": 0.00034983703417605877 + }, + "centre_1": { + "lower_1": -0.0002659617612300783, + "median": -5.023608410604068e-05, + "sigma": 0.00022967040388825044, + "upper_1": 0.00019337904654642257 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.04887110577592829, + "median": 0.05246100226581214, + "sigma": 0.003856888671661004, + "upper_1": 0.0565848831192503 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.00854416018292672, + "median": -0.0017662915936733998, + "sigma": 0.0068333635309879865, + "upper_1": 0.005122566879049255 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_0": { + "lower_1": -0.09647005672837743, + "median": 0.04246240903637811, + "sigma": 0.1086211887880496, + "upper_1": 0.1207723208477218 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_1": { + "lower_1": -0.09029748925515409, + "median": 0.04647722589085967, + "sigma": 0.13055809477954863, + "upper_1": 0.1708187003039432 + } + }, + "truth_delta_sigma": {} + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 8903 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-7f695c3cdb0cdedb", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "light[1]", + "target": "hst/slam5/seed0", + "timings": { + "compile_s": 3.147912166081369, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 267.51912546157837, + "setup_s": null, + "total_s": 621.3747416636907 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed0 | mass_total[1] | 0 | complete | not_assessed | not_assessed | 800.279230594635 | 1168.8711764230393 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-03dd03936a025726 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 4, + "free_parameters": 8, + "inversion": "sparse", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 20780, + "log_evidence": 31589.517760652383, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31640.27897356204, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.0007973979011487347, + "median": -0.00029065335383412924, + "sigma": 0.0005291617336463017, + "upper_1": 0.0002609255661438686 + }, + "centre_1": { + "lower_1": -0.0010004817465142663, + "median": -0.0005405525107195992, + "sigma": 0.00045006861419747367, + "upper_1": -0.00010034451811931893 + }, + "einstein_radius": { + "lower_1": 1.6019444175809487, + "median": 1.6047858513192814, + "sigma": 0.00362851020374233, + "upper_1": 1.6092014379884334 + }, + "ell_comps_0": { + "lower_1": 0.0532874587761125, + "median": 0.055201788027649225, + "sigma": 0.0023546125678615554, + "upper_1": 0.05799668391183561 + }, + "ell_comps_1": { + "lower_1": -0.0017838579139172516, + "median": -0.0006635172405130986, + "sigma": 0.0011377140954758336, + "upper_1": 0.0004915702770344155 + }, + "gamma_1": { + "lower_1": 0.04998443399592357, + "median": 0.052021164753911046, + "sigma": 0.0024079324334008445, + "upper_1": 0.05480029886272526 + }, + "gamma_2": { + "lower_1": 0.050381992061317535, + "median": 0.051267110917400556, + "sigma": 0.0009516483614926165, + "upper_1": 0.05228528878430277 + }, + "shear_magnitude": { + "lower_1": 0.07113276213878587, + "median": 0.07295421084660728, + "sigma": 0.0022626299865816032, + "upper_1": 0.07565802211194908 + }, + "slope": { + "lower_1": 2.0160319925229717, + "median": 2.0522222960629666, + "sigma": 0.045337910370845336, + "upper_1": 2.1067078132646624 + } + }, + "truth_delta_sigma": { + "einstein_radius": 1.3189576577035271, + "gamma_1": 0.839377685966234, + "gamma_2": 1.3314906731023508, + "shear_magnitude": 0.9915597076223915, + "slope": 1.1518461180899124 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 8903 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json#stage-03dd03936a025726", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed0.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": "mass_total[1]", + "target": "hst/slam5/seed0", + "timings": { + "compile_s": 3.259830947034061, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 800.279230594635, + "setup_s": null, + "total_s": 1168.8711764230393 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed1 | unknown | 1 | complete | not_assessed | not_assessed | unknown | unknown | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 4, + "inversion": "sparse", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "rect_mesh": "bilinear", + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "missing", + "values": {} + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json" + ], + "execution": { + "completed": null, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 8903 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": null, + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": null, + "target": "hst/slam5/seed1", + "timings": { + "compile_s": null, + "definitions": { + "compile_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "sampling_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "setup_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed", + "total_s": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + }, + "sampling_s": null, + "setup_s": null, + "total_s": null + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "execution.completed": "No pipeline-wide completion marker is recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result", + "timings": "Pipeline aggregate not measured; stage clocks may overlap and are never summed" + } +} +``` + +
+ +| hst/slam5/seed1 | source_lp[1] | 1 | complete | not_assessed | not_assessed | 798.179591178894 | 944.8778721541166 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-9b14006556621cdb + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 4, + "free_parameters": 17, + "inversion": "sparse", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 50, + "n_live": 200, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": false, + "likelihood_evals": 96150, + "log_evidence": 31968.52941020597, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 32068.70891729047, + "positions_info_present": false, + "posterior": { + "einstein_radius": { + "lower_1": 1.6002418430321865, + "median": 1.6004466431748656, + "sigma": 0.00020159165771416188, + "upper_1": 1.6006450263476149 + }, + "galaxies.lens.bulge.profile_list.0.centre_0": { + "lower_1": -7.750488385683926e-05, + "median": 0.00013847889437458108, + "sigma": 0.0002162299291853899, + "upper_1": 0.0003549549745139405 + }, + "galaxies.lens.bulge.profile_list.0.centre_1": { + "lower_1": -0.00025826743784520146, + "median": -3.9419768538432916e-05, + "sigma": 0.00021847966484632398, + "upper_1": 0.0001786918918474465 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_0": { + "lower_1": -0.015757444378201757, + "median": 0.04356674639326028, + "sigma": 0.05712723729907899, + "upper_1": 0.09849703021995622 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.10628712510673623, + "median": 0.08993038028173284, + "sigma": 0.15074030880305253, + "upper_1": 0.19519349249936885 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_0": { + "lower_1": 0.050143899752556634, + "median": 0.05243086160710461, + "sigma": 0.0021997336861041203, + "upper_1": 0.054543367124764874 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_1": { + "lower_1": -0.007837489740746302, + "median": -0.0030678586683284357, + "sigma": 0.006260495287802669, + "upper_1": 0.004683500834859036 + }, + "galaxies.lens.mass.centre_0": { + "lower_1": -0.000345207978736966, + "median": -6.426899222157285e-05, + "sigma": 0.00027832077639389875, + "upper_1": 0.0002114335740508315 + }, + "galaxies.lens.mass.centre_1": { + "lower_1": -0.00048461869666152613, + "median": -0.00022120579195460268, + "sigma": 0.00026749865833432094, + "upper_1": 5.037862000711575e-05 + }, + "galaxies.lens.mass.ell_comps_0": { + "lower_1": 0.05215882176929355, + "median": 0.05275202415535034, + "sigma": 0.0006124830614972071, + "upper_1": 0.05338378789228796 + }, + "galaxies.lens.mass.ell_comps_1": { + "lower_1": -0.0005335667636561853, + "median": 0.0003455948775807905, + "sigma": 0.0008928922239277532, + "upper_1": 0.0012522176841993212 + }, + "galaxies.source.bulge.profile_list.0.centre_0": { + "lower_1": -0.0005087136574358788, + "median": -0.0002939683972266, + "sigma": 0.00021370007708250514, + "upper_1": -8.13135032708685e-05 + }, + "galaxies.source.bulge.profile_list.0.centre_1": { + "lower_1": -0.0004828782366314912, + "median": -0.0002810826691120134, + "sigma": 0.00020679659806377884, + "upper_1": -6.928504050393354e-05 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_0": { + "lower_1": 0.09559309905930148, + "median": 0.0976682168048574, + "sigma": 0.00208662903021517, + "upper_1": 0.09976635711973182 + }, + "galaxies.source.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.05751491054456686, + "median": -0.05560945370702354, + "sigma": 0.0018635682504467808, + "upper_1": -0.0537877740436733 + }, + "gamma_1": { + "lower_1": 0.04964190116111291, + "median": 0.04998270660058873, + "sigma": 0.00034578590763384084, + "upper_1": 0.05033347297638059 + }, + "gamma_2": { + "lower_1": 0.04978134628211007, + "median": 0.050125269749287035, + "sigma": 0.0003484251285704072, + "upper_1": 0.05047819653925088 + }, + "shear_magnitude": { + "lower_1": 0.07045347480331579, + "median": 0.07078951109216314, + "sigma": 0.00034249733403977217, + "upper_1": 0.07113846947139534 + } + }, + "truth_delta_sigma": { + "einstein_radius": 2.2155836205227972, + "gamma_1": -0.05001186870108602, + "gamma_2": 0.35953132829717505, + "shear_magnitude": 0.230171057329216 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 8903 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-9b14006556621cdb", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "source_lp[1]", + "target": "hst/slam5/seed1", + "timings": { + "compile_s": 11.664846658241004, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 798.179591178894, + "setup_s": null, + "total_s": 944.8778721541166 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed1 | source_pix[1] | 1 | complete | not_assessed | not_assessed | 1904.31116771698 | 2245.7180267861113 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-1ad2b0be53ac3cb8 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 4, + "free_parameters": 10, + "inversion": "sparse", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 33020, + "log_evidence": 31432.37150489249, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31498.611629283845, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.0004051104753894064, + "median": -7.526370741944349e-05, + "sigma": 0.0003182550935723185, + "upper_1": 0.00023139971175523057 + }, + "centre_1": { + "lower_1": -0.0005862149855325486, + "median": -0.00022339059010757808, + "sigma": 0.0003683780953943969, + "upper_1": 0.00015054120525624518 + }, + "einstein_radius": { + "lower_1": 1.6003338997552268, + "median": 1.6005539118878076, + "sigma": 0.00022222253826587046, + "upper_1": 1.6007783448317585 + }, + "ell_comps_0": { + "lower_1": 0.050850092124795514, + "median": 0.05133050512098238, + "sigma": 0.0004349006305760343, + "upper_1": 0.05171989338594758 + }, + "ell_comps_1": { + "lower_1": -0.0006984395485381928, + "median": 0.0005188863791714676, + "sigma": 0.001039496303558125, + "upper_1": 0.0013805530585780572 + }, + "gamma_1": { + "lower_1": 0.04988857706956348, + "median": 0.05037332251104496, + "sigma": 0.0004470303240305812, + "upper_1": 0.050782637717624646 + }, + "gamma_2": { + "lower_1": 0.049162371528577754, + "median": 0.049416569615704765, + "sigma": 0.0002336854082701685, + "upper_1": 0.04962974234511809 + }, + "inner_coefficient": { + "lower_1": 3.5609643240707968e-06, + "median": 3.6862036021450234e-05, + "sigma": 0.0002580796791264336, + "upper_1": 0.000519720322576938 + }, + "outer_coefficient": { + "lower_1": 114.82682095645933, + "median": 145.81913435805797, + "sigma": 30.12137251459437, + "upper_1": 175.06956598564807 + }, + "shear_magnitude": { + "lower_1": 0.07018365817400594, + "median": 0.07055861427885095, + "sigma": 0.00034326793515274123, + "upper_1": 0.07087019404431143 + }, + "signal_scale": { + "lower_1": 0.018080769302226685, + "median": 0.021941152075661084, + "sigma": 0.00493701718904997, + "upper_1": 0.027954803680326625 + } + }, + "truth_delta_sigma": { + "einstein_radius": 2.4925999501670866, + "gamma_1": 0.8351167493045054, + "gamma_2": -2.4966487578921552, + "shear_magnitude": -0.44298876833967904 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 8903 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-1ad2b0be53ac3cb8", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "source_pix[1]", + "target": "hst/slam5/seed1", + "timings": { + "compile_s": 19.77601555129513, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 1904.31116771698, + "setup_s": null, + "total_s": 2245.7180267861113 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed1 | source_pix[2] | 1 | complete | not_assessed | not_assessed | 187.7734875679016 | 523.5104860081337 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-122202734335fe14 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 4, + "free_parameters": 5, + "inversion": "sparse", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 20, + "n_live": 75, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 3580, + "log_evidence": 31618.6122970467, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31636.541000790596, + "positions_info_present": true, + "posterior": { + "inner_coefficient": { + "lower_1": 5.495053695429887e-06, + "median": 0.0001294606399508073, + "sigma": 0.0017393764313756478, + "upper_1": 0.0034842479164467256 + }, + "outer_coefficient": { + "lower_1": 40.859302058544, + "median": 46.35831803297557, + "sigma": 4.910076647233058, + "upper_1": 50.67945535301012 + }, + "signal_scale": { + "lower_1": 0.060044118090618395, + "median": 0.06694290465959576, + "sigma": 0.00817336132755847, + "upper_1": 0.07639084074573534 + }, + "weight_floor": { + "lower_1": 7.756400814296158e-05, + "median": 0.001211499672841594, + "sigma": 0.012641010273363305, + "upper_1": 0.02535958455486957 + }, + "weight_power": { + "lower_1": 3.992034112319395, + "median": 4.032484148804173, + "sigma": 0.04179493679485846, + "upper_1": 4.075623985909112 + } + }, + "truth_delta_sigma": {} + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 8903 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-122202734335fe14", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "source_pix[2]", + "target": "hst/slam5/seed1", + "timings": { + "compile_s": 8.781606091186404, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 187.7734875679016, + "setup_s": null, + "total_s": 523.5104860081337 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed1 | light[1] | 1 | complete | not_assessed | not_assessed | 253.89728569984436 | 606.7501009167172 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-7f695c3cdb0cdedb + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 4, + "free_parameters": 6, + "inversion": "sparse", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 9980, + "log_evidence": 31611.348496324066, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31636.97780090187, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -9.256108694536274e-05, + "median": 0.00013058564806291397, + "sigma": 0.00022297136001897893, + "upper_1": 0.0003533816330925951 + }, + "centre_1": { + "lower_1": -0.000252978358942859, + "median": -3.1042794756174566e-05, + "sigma": 0.00022859878882430595, + "upper_1": 0.0002042192187057529 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_0": { + "lower_1": -0.12299672635308904, + "median": 0.03884950456667832, + "sigma": 0.11276230141824903, + "upper_1": 0.102527876483409 + }, + "galaxies.lens.bulge.profile_list.0.ell_comps_1": { + "lower_1": -0.12520395353884461, + "median": 0.05763209180733065, + "sigma": 0.15278697026130977, + "upper_1": 0.18036998698377496 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_0": { + "lower_1": 0.04950554513154913, + "median": 0.05241465902110188, + "sigma": 0.003108736615922779, + "upper_1": 0.05572301836339469 + }, + "galaxies.lens.bulge.profile_list.20.ell_comps_1": { + "lower_1": -0.00701153280159096, + "median": -0.0011040753118636939, + "sigma": 0.006978365064416689, + "upper_1": 0.006945197327242417 + } + }, + "truth_delta_sigma": {} + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 8903 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-7f695c3cdb0cdedb", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "light[1]", + "target": "hst/slam5/seed1", + "timings": { + "compile_s": 3.146366816945374, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 253.89728569984436, + "setup_s": null, + "total_s": 606.7501009167172 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| hst/slam5/seed1 | mass_total[1] | 1 | complete | not_assessed | not_assessed | 868.1189360618591 | 1255.2648063716479 | True | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json) | + +
Provenance: results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-03dd03936a025726 + +```json +{ + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_sparse_fp64", + "cores": 4, + "free_parameters": 8, + "inversion": "sparse", + "memo": "off", + "mesh_shape": [ + 28, + 28 + ], + "n_batch": 20, + "n_live": 150, + "rect_mesh": "bilinear", + "resumed": false, + "stages_requested": null, + "test_mode": false, + "use_jax": true + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": true, + "likelihood_evals": 21640, + "log_evidence": 31592.444531101104, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "max_log_likelihood": 31643.201491647094, + "positions_info_present": true, + "posterior": { + "centre_0": { + "lower_1": -0.0006063313167605052, + "median": -0.00015480353147833862, + "sigma": 0.0004699276084807665, + "upper_1": 0.0003335239002010278 + }, + "centre_1": { + "lower_1": -0.0010893145054597904, + "median": -0.0005659168432272032, + "sigma": 0.0004987433875846499, + "upper_1": -9.182773029049073e-05 + }, + "einstein_radius": { + "lower_1": 1.5997927522466993, + "median": 1.6039533926931169, + "sigma": 0.004330972228549279, + "upper_1": 1.6084546967037978 + }, + "ell_comps_0": { + "lower_1": 0.0522636774701747, + "median": 0.05484859715851913, + "sigma": 0.0027448502174055277, + "upper_1": 0.057753377904985755 + }, + "ell_comps_1": { + "lower_1": -0.0019499451010100224, + "median": -0.0007833753874749372, + "sigma": 0.0012747126298375852, + "upper_1": 0.0005994801586651482 + }, + "gamma_1": { + "lower_1": 0.04846752808326362, + "median": 0.051278956697827525, + "sigma": 0.0030606455843170018, + "upper_1": 0.054588819251897625 + }, + "gamma_2": { + "lower_1": 0.05001641273561682, + "median": 0.05112911904641106, + "sigma": 0.0011305240731175892, + "upper_1": 0.052277460881852 + }, + "shear_magnitude": { + "lower_1": 0.06975476312372066, + "median": 0.07240238442356879, + "sigma": 0.0028678264340456366, + "upper_1": 0.07549041599181193 + }, + "slope": { + "lower_1": 1.986048971801755, + "median": 2.040321863557936, + "sigma": 0.05596954394103826, + "upper_1": 2.0979880596838316 + } + }, + "truth_delta_sigma": { + "einstein_radius": 0.9128187585818419, + "gamma_1": 0.4178715446116994, + "gamma_2": 0.9987571899263903, + "shear_magnitude": 0.5898914539704341, + "slope": 0.7204250869082192 + } + } + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 4, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 8903 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json#stage-03dd03936a025726", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/slam_base/hpc_a100_jax_gpu_sparse_fp64/stages_seed1.json", + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": "mass_total[1]", + "target": "hst/slam5/seed1", + "timings": { + "compile_s": 3.209078643936664, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 868.1189360618591, + "setup_s": null, + "total_s": 1255.2648063716479 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "dependency_revisions": "Dependency revisions not recorded", + "measured_at": "Not recorded in source result", + "model": "Not recorded in source result", + "sampler": "Not recorded in source result" + } +} +``` + +
+ +| simple/source_plane_solved/seed0 | unknown | 0 | complete | not_assessed | not_assessed | 56.56000781059265 | 65.21035292901797 | False | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed0.json) | + +
Provenance: results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed0.json + +```json +{ + "archived": false, + "backend": "jax_cpu", + "comparison": { + "declared_target": "simple/source_plane_solved/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_cpu_dense_fp64", + "cores": 8, + "fit_positions_cls": "FitPositionsSourceSolved", + "free_parameters": 5, + "inversion": "dense", + "n_batch": 50, + "n_live": 100, + "priors": { + "einstein_radius": "U(0.5, 4.0)", + "others": "library-default Isothermal priors" + }, + "resumed": false, + "test_mode": false, + "use_jax": true, + "variant": "source_plane_solved" + }, + "dataset": { + "class": "point_source", + "id": null, + "instrument": "simple" + }, + "dependency_revisions": { + "autoarray": "9428eca2", + "autofit": "404b3e5f7", + "autogalaxy": "c9609825", + "autolens": "21b520be4", + "autonerves": "bf10410" + }, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": false, + "likelihood_evals": 4850, + "likelihood_share": 0.00040501777436034486, + "log_evidence": -7.153937371523832, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "log_likelihood_at_truth": 15.902398430038737, + "max_log_likelihood": 16.48568552789147, + "per_call_s": 4.723259480670094e-06, + "positions_info_present": false, + "posterior": { + "centre_0": { + "lower_1": -0.04216816245874099, + "median": -0.017453424264998282, + "sigma": 0.025797484788372172, + "upper_1": 0.00942680711800335 + }, + "centre_1": { + "lower_1": -0.04336377661022216, + "median": -0.018067299498348146, + "sigma": 0.025791414665263836, + "upper_1": 0.00821905272030551 + }, + "einstein_radius": { + "lower_1": 1.5945063508229387, + "median": 1.6056260743312727, + "sigma": 0.01115243788494824, + "upper_1": 1.6168112265928352 + }, + "ell_comps_0": { + "lower_1": 0.047688746799381704, + "median": 0.06559594956905983, + "sigma": 0.01762818058418965, + "upper_1": 0.082945107967761 + }, + "ell_comps_1": { + "lower_1": -0.00019838121507798336, + "median": 2.4804878302780597e-05, + "sigma": 0.00022114345613303182, + "upper_1": 0.0002439056971880803 + } + }, + "timing": { + "batched": { + "median_s": 0.00023616297403350472, + "min_s": 0.00019111903384327888, + "n": 200, + "p16_s": 0.00021696899784728885, + "p84_s": 0.00025908800307661295, + "warmup": 10 + }, + "compile_s": 1.9011598539655097, + "compile_single_s": 1.3180420650169253, + "single": { + "median_s": 0.0002068985195364803, + "min_s": 0.00016070902347564697, + "n": 200, + "p16_s": 0.00019189901649951935, + "p84_s": 0.00022341799922287464, + "warmup": 10 + }, + "vector": [ + 0.0, + 0.0, + 0.0, + 0.0, + 2.25 + ] + }, + "truth_delta_sigma": { + "centre_0": -0.6765552691735727, + "centre_1": -0.7005160334489673, + "einstein_radius": 0.5044703578995735, + "ell_comps_0": 0.7354344119505386, + "ell_comps_1": 0.112166458535656 + }, + "truths": { + "centre_0": 0.0, + "centre_1": 0.0, + "einstein_radius": 1.6, + "ell_comps_0": 0.05263157894736841, + "ell_comps_1": 3.2227547345982974e-18 + } + } + }, + "evidence_paths": [ + "results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "cpu", + "cpu_count": 124, + "device": "cpu:0", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + }, + "host": "euclid-ral-gpu-2", + "slurm_array_job_id": null, + "slurm_array_task_id": null, + "slurm_job_id": "366937" + }, + "id": "results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed0.json", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": "L5: Isothermal (5 free) + PointSolved source", + "parent_run_id": null, + "pipeline": null, + "precision": "fp64", + "sampler": "nautilus", + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": "output/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/seed_0/nautilus/b39f17cc6d57dba03e090e4d9682ce2b" + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": null, + "target": "simple/source_plane_solved/seed0", + "timings": { + "compile_s": 1.9011598539655097, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 56.56000781059265, + "setup_s": null, + "total_s": 65.21035292901797 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "measured_at": "Not recorded in source result" + } +} +``` + +
+ +| simple/source_plane_solved/seed1 | unknown | 1 | complete | not_assessed | not_assessed | 54.18134164810181 | 62.80253079999238 | False | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed1.json) | + +
Provenance: results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed1.json + +```json +{ + "archived": false, + "backend": "jax_cpu", + "comparison": { + "declared_target": "simple/source_plane_solved/seed1", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_cpu_dense_fp64", + "cores": 8, + "fit_positions_cls": "FitPositionsSourceSolved", + "free_parameters": 5, + "inversion": "dense", + "n_batch": 50, + "n_live": 100, + "priors": { + "einstein_radius": "U(0.5, 4.0)", + "others": "library-default Isothermal priors" + }, + "resumed": false, + "test_mode": false, + "use_jax": true, + "variant": "source_plane_solved" + }, + "dataset": { + "class": "point_source", + "id": null, + "instrument": "simple" + }, + "dependency_revisions": { + "autoarray": "9428eca2", + "autofit": "404b3e5f7", + "autogalaxy": "c9609825", + "autolens": "21b520be4", + "autonerves": "bf10410" + }, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": false, + "likelihood_evals": 4850, + "likelihood_share": 0.00044716559745482814, + "log_evidence": -7.158348941897645, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "log_likelihood_at_truth": 15.902398430038737, + "max_log_likelihood": 16.471893783907685, + "per_call_s": 4.9954705173149704e-06, + "positions_info_present": false, + "posterior": { + "centre_0": { + "lower_1": -0.04856937188459597, + "median": -0.017285637201381637, + "sigma": 0.02993531121787803, + "upper_1": 0.011301250551160087 + }, + "centre_1": { + "lower_1": -0.04927473815156828, + "median": -0.018977019644291444, + "sigma": 0.029723460521518596, + "upper_1": 0.010172182891468916 + }, + "einstein_radius": { + "lower_1": 1.594870045945336, + "median": 1.6057568326663125, + "sigma": 0.012117262851364718, + "upper_1": 1.6191045716480654 + }, + "ell_comps_0": { + "lower_1": 0.04638198418932514, + "median": 0.06675091014973905, + "sigma": 0.019889650208944093, + "upper_1": 0.08616128460721333 + }, + "ell_comps_1": { + "lower_1": -0.00018779622671053593, + "median": 2.46203758156711e-05, + "sigma": 0.00021289849021188594, + "upper_1": 0.00023800075371323593 + } + }, + "timing": { + "batched": { + "median_s": 0.0002497735258657485, + "min_s": 0.00018407899187877774, + "n": 200, + "p16_s": 0.00022290903143584728, + "p84_s": 0.00027851801132783294, + "warmup": 10 + }, + "compile_s": 1.9216195569606498, + "compile_single_s": 1.3229017739649862, + "single": { + "median_s": 0.00021802401170134544, + "min_s": 0.00017516798106953502, + "n": 200, + "p16_s": 0.0001976100029423833, + "p84_s": 0.0002334390301257372, + "warmup": 10 + }, + "vector": [ + 0.0, + 0.0, + 0.0, + 0.0, + 2.25 + ] + }, + "truth_delta_sigma": { + "centre_0": -0.5774330213429775, + "centre_1": -0.6384525661321582, + "einstein_radius": 0.4750934874425102, + "ell_comps_0": 0.7098833339975671, + "ell_comps_1": 0.11564373139125879 + }, + "truths": { + "centre_0": 0.0, + "centre_1": 0.0, + "einstein_radius": 1.6, + "ell_comps_0": 0.05263157894736841, + "ell_comps_1": 3.2227547345982974e-18 + } + } + }, + "evidence_paths": [ + "results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed1.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "cpu", + "cpu_count": 124, + "device": "cpu:0", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + }, + "host": "euclid-ral-gpu-2", + "slurm_array_job_id": "367140", + "slurm_array_task_id": "1", + "slurm_job_id": "367142" + }, + "id": "results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed1.json", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": "L5: Isothermal (5 free) + PointSolved source", + "parent_run_id": null, + "pipeline": null, + "precision": "fp64", + "sampler": "nautilus", + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": "output/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/seed_1/nautilus/08bb8d044c7b8ef96f88b433289c1945" + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 1, + "stage": null, + "target": "simple/source_plane_solved/seed1", + "timings": { + "compile_s": 1.9216195569606498, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 54.18134164810181, + "setup_s": null, + "total_s": 62.80253079999238 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "measured_at": "Not recorded in source result" + } +} +``` + +
+ +| simple/source_plane_solved/seed2 | unknown | 2 | complete | not_assessed | not_assessed | 53.764665603637695 | 62.157571617048234 | False | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed2.json) | + +
Provenance: results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed2.json + +```json +{ + "archived": false, + "backend": "jax_cpu", + "comparison": { + "declared_target": "simple/source_plane_solved/seed2", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_cpu_dense_fp64", + "cores": 8, + "fit_positions_cls": "FitPositionsSourceSolved", + "free_parameters": 5, + "inversion": "dense", + "n_batch": 50, + "n_live": 100, + "priors": { + "einstein_radius": "U(0.5, 4.0)", + "others": "library-default Isothermal priors" + }, + "resumed": false, + "test_mode": false, + "use_jax": true, + "variant": "source_plane_solved" + }, + "dataset": { + "class": "point_source", + "id": null, + "instrument": "simple" + }, + "dependency_revisions": { + "autoarray": "9428eca2", + "autofit": "404b3e5f7", + "autogalaxy": "c9609825", + "autolens": "21b520be4", + "autonerves": "bf10410" + }, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": false, + "likelihood_evals": 4800, + "likelihood_share": 0.0004274334182432175, + "log_evidence": -7.127663664789889, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "log_likelihood_at_truth": 15.902398430038737, + "max_log_likelihood": 16.455195490792534, + "per_call_s": 4.787669749930501e-06, + "positions_info_present": false, + "posterior": { + "centre_0": { + "lower_1": -0.044552537407693396, + "median": -0.01645754209509627, + "sigma": 0.026327229074754138, + "upper_1": 0.008101920741814879 + }, + "centre_1": { + "lower_1": -0.04601578011653029, + "median": -0.017599982212707296, + "sigma": 0.02647169499234084, + "upper_1": 0.006927609868151387 + }, + "einstein_radius": { + "lower_1": 1.594529490192973, + "median": 1.6052381856951654, + "sigma": 0.011551766199159097, + "upper_1": 1.6176330225912912 + }, + "ell_comps_0": { + "lower_1": 0.04839427588028559, + "median": 0.06520642607572252, + "sigma": 0.01780926818936322, + "upper_1": 0.08401281225901203 + }, + "ell_comps_1": { + "lower_1": -0.00019188612196046327, + "median": 3.450858016227866e-05, + "sigma": 0.00023448606435910046, + "upper_1": 0.00027708600675773766 + } + }, + "timing": { + "batched": { + "median_s": 0.00023938348749652505, + "min_s": 0.00018467899644747376, + "n": 200, + "p16_s": 0.00021848897449672222, + "p84_s": 0.0002651280374266207, + "warmup": 10 + }, + "compile_s": 1.9320949129760265, + "compile_single_s": 1.3133752529975027, + "single": { + "median_s": 0.0002106740139424801, + "min_s": 0.00015419896226376295, + "n": 200, + "p16_s": 0.00018793897470459342, + "p84_s": 0.00023178901756182313, + "warmup": 10 + }, + "vector": [ + 0.0, + 0.0, + 0.0, + 0.0, + 2.25 + ] + }, + "truth_delta_sigma": { + "centre_0": -0.6251148591584154, + "centre_1": -0.6648604185640387, + "einstein_radius": 0.4534532299958279, + "ell_comps_0": 0.7060844384310287, + "ell_comps_1": 0.14716687005086895 + }, + "truths": { + "centre_0": 0.0, + "centre_1": 0.0, + "einstein_radius": 1.6, + "ell_comps_0": 0.05263157894736841, + "ell_comps_1": 3.2227547345982974e-18 + } + } + }, + "evidence_paths": [ + "results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed2.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "cpu", + "cpu_count": 124, + "device": "cpu:0", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + }, + "host": "euclid-ral-gpu-2", + "slurm_array_job_id": "367140", + "slurm_array_task_id": "2", + "slurm_job_id": "367445" + }, + "id": "results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed2.json", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": "L5: Isothermal (5 free) + PointSolved source", + "parent_run_id": null, + "pipeline": null, + "precision": "fp64", + "sampler": "nautilus", + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": "output/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/seed_2/nautilus/50fc5826cef9396f305df994ee3fc8c6" + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 2, + "stage": null, + "target": "simple/source_plane_solved/seed2", + "timings": { + "compile_s": 1.9320949129760265, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 53.764665603637695, + "setup_s": null, + "total_s": 62.157571617048234 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "measured_at": "Not recorded in source result" + } +} +``` + +
+ +| simple/source_plane_solved/seed3 | unknown | 3 | complete | not_assessed | not_assessed | 50.38304305076599 | 58.97957983799279 | False | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed3.json) | + +
Provenance: results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed3.json + +```json +{ + "archived": false, + "backend": "jax_cpu", + "comparison": { + "declared_target": "simple/source_plane_solved/seed3", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_cpu_dense_fp64", + "cores": 8, + "fit_positions_cls": "FitPositionsSourceSolved", + "free_parameters": 5, + "inversion": "dense", + "n_batch": 50, + "n_live": 100, + "priors": { + "einstein_radius": "U(0.5, 4.0)", + "others": "library-default Isothermal priors" + }, + "resumed": false, + "test_mode": false, + "use_jax": true, + "variant": "source_plane_solved" + }, + "dataset": { + "class": "point_source", + "id": null, + "instrument": "simple" + }, + "dependency_revisions": { + "autoarray": "9428eca2", + "autofit": "404b3e5f7", + "autogalaxy": "c9609825", + "autolens": "21b520be4", + "autonerves": "bf10410" + }, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": false, + "likelihood_evals": 4700, + "likelihood_share": 0.0004284008831123949, + "log_evidence": -7.1422108149915955, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "log_likelihood_at_truth": 15.902398430038737, + "max_log_likelihood": 16.442962159403347, + "per_call_s": 4.592370241880417e-06, + "positions_info_present": false, + "posterior": { + "centre_0": { + "lower_1": -0.04391072851853559, + "median": -0.014527938015443267, + "sigma": 0.02547875767253761, + "upper_1": 0.0070467868265396295 + }, + "centre_1": { + "lower_1": -0.043816453308936715, + "median": -0.016769753132973745, + "sigma": 0.02505543764174138, + "upper_1": 0.006294421974546041 + }, + "einstein_radius": { + "lower_1": 1.5951467730554156, + "median": 1.6048231816592944, + "sigma": 0.011000225562784238, + "upper_1": 1.617147224180984 + }, + "ell_comps_0": { + "lower_1": 0.04876829937658942, + "median": 0.06453071192811381, + "sigma": 0.01728396333033107, + "upper_1": 0.08333622603725156 + }, + "ell_comps_1": { + "lower_1": -0.00020965181380705589, + "median": 2.039972265406487e-05, + "sigma": 0.0002217830213759653, + "upper_1": 0.0002339142289448747 + } + }, + "timing": { + "batched": { + "median_s": 0.00022961851209402084, + "min_s": 0.00017524900613352656, + "n": 200, + "p16_s": 0.00020891905296593904, + "p84_s": 0.00025858799926936626, + "warmup": 10 + }, + "compile_s": 1.9380113480146974, + "compile_single_s": 1.3268661400070414, + "single": { + "median_s": 0.0002172240347135812, + "min_s": 0.00016806903295218945, + "n": 200, + "p16_s": 0.00020077801309525967, + "p84_s": 0.00023928796872496605, + "warmup": 10 + }, + "vector": [ + 0.0, + 0.0, + 0.0, + 0.0, + 2.25 + ] + }, + "truth_delta_sigma": { + "centre_0": -0.5701980529098665, + "centre_1": -0.6693059356119964, + "einstein_radius": 0.4384620689608398, + "ell_comps_0": 0.6884493303607047, + "ell_comps_1": 0.09198054263802348 + }, + "truths": { + "centre_0": 0.0, + "centre_1": 0.0, + "einstein_radius": 1.6, + "ell_comps_0": 0.05263157894736841, + "ell_comps_1": 3.2227547345982974e-18 + } + } + }, + "evidence_paths": [ + "results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed3.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "cpu", + "cpu_count": 124, + "device": "cpu:0", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + }, + "host": "euclid-ral-gpu-2", + "slurm_array_job_id": "367140", + "slurm_array_task_id": "3", + "slurm_job_id": "367446" + }, + "id": "results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed3.json", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": "L5: Isothermal (5 free) + PointSolved source", + "parent_run_id": null, + "pipeline": null, + "precision": "fp64", + "sampler": "nautilus", + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": "output/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/seed_3/nautilus/bfcfe9793f8b2cfa915170d57974952f" + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 3, + "stage": null, + "target": "simple/source_plane_solved/seed3", + "timings": { + "compile_s": 1.9380113480146974, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 50.38304305076599, + "setup_s": null, + "total_s": 58.97957983799279 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "measured_at": "Not recorded in source result" + } +} +``` + +
+ +| simple/source_plane_solved/seed4 | unknown | 4 | complete | not_assessed | not_assessed | 58.50750803947449 | 67.16032414801884 | False | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed4.json) | + +
Provenance: results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed4.json + +```json +{ + "archived": false, + "backend": "jax_cpu", + "comparison": { + "declared_target": "simple/source_plane_solved/seed4", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_cpu_dense_fp64", + "cores": 8, + "fit_positions_cls": "FitPositionsSourceSolved", + "free_parameters": 5, + "inversion": "dense", + "n_batch": 50, + "n_live": 100, + "priors": { + "einstein_radius": "U(0.5, 4.0)", + "others": "library-default Isothermal priors" + }, + "resumed": false, + "test_mode": false, + "use_jax": true, + "variant": "source_plane_solved" + }, + "dataset": { + "class": "point_source", + "id": null, + "instrument": "simple" + }, + "dependency_revisions": { + "autoarray": "9428eca2", + "autofit": "404b3e5f7", + "autogalaxy": "c9609825", + "autolens": "21b520be4", + "autonerves": "bf10410" + }, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": false, + "likelihood_evals": 4750, + "likelihood_share": 0.0004068701458214967, + "log_evidence": -7.171363991071475, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "log_likelihood_at_truth": 15.902398430038737, + "max_log_likelihood": 16.46678997877629, + "per_call_s": 5.011570174247026e-06, + "positions_info_present": false, + "posterior": { + "centre_0": { + "lower_1": -0.04204305304460868, + "median": -0.015202703809942616, + "sigma": 0.025313908068376127, + "upper_1": 0.008584763092143573 + }, + "centre_1": { + "lower_1": -0.04385515716199128, + "median": -0.015818560715543373, + "sigma": 0.02594592273742021, + "upper_1": 0.008036688312849143 + }, + "einstein_radius": { + "lower_1": 1.5948994050053367, + "median": 1.6047485261323444, + "sigma": 0.010872921797065671, + "upper_1": 1.616645248599468 + }, + "ell_comps_0": { + "lower_1": 0.04857850833216588, + "median": 0.06434333621231575, + "sigma": 0.017004965660044506, + "upper_1": 0.08258843965225489 + }, + "ell_comps_1": { + "lower_1": -0.0001812031602880165, + "median": 2.3331858448681895e-05, + "sigma": 0.00022295095800914236, + "upper_1": 0.00026469875573026825 + } + }, + "timing": { + "batched": { + "median_s": 0.0002505785087123513, + "min_s": 0.00020097801461815834, + "n": 200, + "p16_s": 0.00023126800078898668, + "p84_s": 0.0002849379670806229, + "warmup": 10 + }, + "compile_s": 1.940513722016476, + "compile_single_s": 1.3289996769744903, + "single": { + "median_s": 0.0002128235064446926, + "min_s": 0.00017350801499560475, + "n": 200, + "p16_s": 0.0001968390424735844, + "p84_s": 0.00023722898913547397, + "warmup": 10 + }, + "vector": [ + 0.0, + 0.0, + 0.0, + 0.0, + 2.25 + ] + }, + "truth_delta_sigma": { + "centre_0": -0.6005672363539504, + "centre_1": -0.60967423959562, + "einstein_radius": 0.4367295397660122, + "ell_comps_0": 0.6887257227731847, + "ell_comps_1": 0.10465018252005862 + }, + "truths": { + "centre_0": 0.0, + "centre_1": 0.0, + "einstein_radius": 1.6, + "ell_comps_0": 0.05263157894736841, + "ell_comps_1": 3.2227547345982974e-18 + } + } + }, + "evidence_paths": [ + "results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed4.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "cpu", + "cpu_count": 124, + "device": "cpu:0", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + }, + "host": "euclid-ral-gpu-2", + "slurm_array_job_id": "367140", + "slurm_array_task_id": "4", + "slurm_job_id": "367140" + }, + "id": "results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed4.json", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": "L5: Isothermal (5 free) + PointSolved source", + "parent_run_id": null, + "pipeline": null, + "precision": "fp64", + "sampler": "nautilus", + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": "output/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/seed_4/nautilus/a7d8d84b055356df0f0dbb3151d46885" + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 4, + "stage": null, + "target": "simple/source_plane_solved/seed4", + "timings": { + "compile_s": 1.940513722016476, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 58.50750803947449, + "setup_s": null, + "total_s": 67.16032414801884 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "measured_at": "Not recorded in source result" + } +} +``` + +
+ +| simple/source_plane_solved/seed0 | unknown | 0 | complete | not_assessed | not_assessed | 60.43147277832031 | 69.44111120000161 | False | [result](https://github.com/PyAutoLabs/autolens_inference/blob/9c362a7a298238d766f61e812c694813e94830d1/results/searches/point_source/nautilus/simple/source_plane_solved/local_jax_cpu_dense_fp64/search_seed0.json) | + +
Provenance: results/searches/point_source/nautilus/simple/source_plane_solved/local_jax_cpu_dense_fp64/search_seed0.json + +```json +{ + "archived": false, + "backend": "jax_cpu", + "comparison": { + "declared_target": "simple/source_plane_solved/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "local_jax_cpu_dense_fp64", + "cores": 8, + "fit_positions_cls": "FitPositionsSourceSolved", + "free_parameters": 5, + "inversion": "dense", + "n_batch": 50, + "n_live": 100, + "priors": { + "einstein_radius": "U(0.5, 4.0)", + "others": "library-default Isothermal priors" + }, + "resumed": false, + "test_mode": false, + "use_jax": true, + "variant": "source_plane_solved" + }, + "dataset": { + "class": "point_source", + "id": null, + "instrument": "simple" + }, + "dependency_revisions": { + "autoarray": "9428eca2", + "autofit": "404b3e5f7", + "autogalaxy": "c9609825", + "autolens": "21b520be4", + "autonerves": "0747524" + }, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": false, + "likelihood_evals": 4850, + "likelihood_share": 0.0005765604992410397, + "log_evidence": -7.15393737152383, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "log_likelihood_at_truth": 15.902398430038737, + "max_log_likelihood": 16.48568552789147, + "per_call_s": 7.1840000236988995e-06, + "positions_info_present": false, + "posterior": { + "centre_0": { + "lower_1": -0.042168162458740985, + "median": -0.01745342426499831, + "sigma": 0.025797484788372165, + "upper_1": 0.009426807118003344 + }, + "centre_1": { + "lower_1": -0.04336377661022215, + "median": -0.018067299498348146, + "sigma": 0.025791414665263825, + "upper_1": 0.008219052720305502 + }, + "einstein_radius": { + "lower_1": 1.5945063508229387, + "median": 1.6056260743312727, + "sigma": 0.01115243788494824, + "upper_1": 1.6168112265928352 + }, + "ell_comps_0": { + "lower_1": 0.047688746799381704, + "median": 0.06559594956905983, + "sigma": 0.01762818058418965, + "upper_1": 0.082945107967761 + }, + "ell_comps_1": { + "lower_1": -0.00019838121507798325, + "median": 2.4804878302780604e-05, + "sigma": 0.00022114345613303174, + "upper_1": 0.00024390569718808023 + } + }, + "timing": { + "batched": { + "median_s": 0.000359200001184945, + "min_s": 0.0003099999994446989, + "n": 200, + "p16_s": 0.00033690000054775737, + "p84_s": 0.000389000000723172, + "warmup": 10 + }, + "compile_s": 2.3335995999987063, + "compile_single_s": 1.8172457000000577, + "single": { + "median_s": 0.0002923499996541068, + "min_s": 0.0002471000007062685, + "n": 200, + "p16_s": 0.0002716999988479074, + "p84_s": 0.0003206000001227949, + "warmup": 10 + }, + "vector": [ + 0.0, + 0.0, + 0.0, + 0.0, + 2.25 + ] + }, + "truth_delta_sigma": { + "centre_0": -0.676555269173574, + "centre_1": -0.7005160334489675, + "einstein_radius": 0.5044703578995735, + "ell_comps_0": 0.7354344119505386, + "ell_comps_1": 0.11216645853565607 + }, + "truths": { + "centre_0": 0.0, + "centre_1": 0.0, + "einstein_radius": 1.6, + "ell_comps_0": 0.05263157894736841, + "ell_comps_1": 3.2227547345982974e-18 + } + } + }, + "evidence_paths": [ + "results/searches/point_source/nautilus/simple/source_plane_solved/local_jax_cpu_dense_fp64/search_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "cpu", + "cpu_count": 8, + "device": "cpu:0", + "omp_num_threads": "8", + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + }, + "host": "DESKTOP-H143S82", + "slurm_array_job_id": null, + "slurm_array_task_id": null, + "slurm_job_id": null + }, + "id": "results/searches/point_source/nautilus/simple/source_plane_solved/local_jax_cpu_dense_fp64/search_seed0.json", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": "L5: Isothermal (5 free) + PointSolved source", + "parent_run_id": null, + "pipeline": null, + "precision": "fp64", + "sampler": "nautilus", + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": "output/searches/point_source/nautilus/simple/source_plane_solved/local_jax_cpu_dense_fp64/seed_0/nautilus/b39f17cc6d57dba03e090e4d9682ce2b" + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": null, + "target": "simple/source_plane_solved/seed0", + "timings": { + "compile_s": 2.3335995999987063, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 60.43147277832031, + "setup_s": null, + "total_s": 69.44111120000161 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "measured_at": "Not recorded in source result" + } +} +``` + +
+ diff --git a/insight/__init__.py b/insight/__init__.py new file mode 100644 index 0000000..6a3b23d --- /dev/null +++ b/insight/__init__.py @@ -0,0 +1,5 @@ +"""PyAutoInsight: inference campaign coordination and evidence reader.""" + +from pathlib import Path + +ORGAN_ROOT = Path(__file__).resolve().parents[1] diff --git a/insight/board.py b/insight/board.py new file mode 100644 index 0000000..8c234af --- /dev/null +++ b/insight/board.py @@ -0,0 +1,410 @@ +"""Reproducible inference evidence view, separate from scientific conclusions.""" + +from __future__ import annotations + +import hashlib +import html +import json +import re +from datetime import UTC, datetime +from pathlib import Path + +from insight import ORGAN_ROOT, campaigns, summary +from insight.theme import CSS, JS + +PAGES_URL = "https://pyautolabs.github.io/PyAutoInsight/" +REPO_URL = "https://github.com/PyAutoLabs/PyAutoInsight" +MARKER = re.compile(r"") + + +def _now(): + return datetime.now(UTC).strftime("%Y-%m-%dT%H:%M:%SZ") + + +def records(s): + return (s.doc or {}).get("records", []) + + +def comparisons(s): + return (s.doc or {}).get("comparisons", []) + + +def integrity(s): + if s.source == "local": + return f"local checkout {s.commit or 'unknown'} ({'dirty' if s.dirty else 'clean' if s.dirty is False else 'unknown state'})" + return f"cached · latest fetch {s.outcome}" if s.cached else s.outcome + + +def freshness(s, now=None): + d = s.doc or {} + ev = d.get("evidence_updated_at") + vu = d.get("valid_until") + if vu: + expired = now is not None and summary.parse_utc(now) > summary.parse_utc(vu) + return f"{'stale' if expired else 'valid'} · deadline {vu}" + return f"freshness policy unspecified · evidence {ev or 'time unknown'}" + + +def qualification(s): + if not records(s): + return "no measurements" + return f"{summary.qualified_records(s.doc)}/{len(records(s))} accepted by producer; remaining assessments shown below" + + +def counts(views): + return [ + ("Projects", len(views)), + ("Records", sum(len(records(s)) for s in views)), + ("Comparisons", sum(len(comparisons(s)) for s in views)), + ("Cached", sum(s.cached for s in views)), + ("Failed", sum(s.failed and not s.cached for s in views)), + ] + + +def marker(s): + return f"" + + +def markers(text): + return {n: {"receipt": r, "outcome": o, "shown": s} for n, r, o, s in MARKER.findall(text)} + + +def input_marker(views, data): + body = [ + { + "instance": vars(s.instance), + "outcome": s.outcome, + "source": s.source, + "doc": s.doc, + "commit": s.commit, + "fetched_at": s.fetched_at, + "errors": s.errors, + "cached": s.cached, + "attempt_commit": s.attempt_commit, + "attempt_at": s.attempt_at, + "dirty": s.dirty, + "source_branch": s.source_branch, + } + for s in views + ] + return ( + "" + ) + + +def e(v): + return html.escape("unknown" if v is None else str(v), quote=True) + + +def md(v): + return e(v).replace("|", "\\|").replace("\n", " ") + + +def _dump(v): + return json.dumps(v, sort_keys=True, ensure_ascii=False) + + +def table(headers, rows): + return ( + '
' + + "".join("" for x in headers) + + "" + + "".join("" + "".join("" for x in row) + "" for row in rows) + + "
" + e(x) + "
" + x + "
" + ) + + +def _detail(s, now=None): + d = s.doc or {} + inst = s.instance + parts = [ + marker(s), + f'

{e(inst.repo)}

', + f"

Integrity: {e(integrity(s))} · Freshness: {e(freshness(s, now))} · Scientific qualification: {e(qualification(s))}

", + f'

Captured source branch: {e(s.source_branch)}. Revision: {e(s.commit)}. Capture time: {e(s.fetched_at)}. Latest attempt: {e(s.attempt_at)}.

', + ] + if s.errors: + parts.append('

' + e("; ".join(s.errors)) + "

") + if s.cached: + parts.append( + '

Cached evidence: original capture and evidence times retained. This refresh has not made the evidence newer.

' + ) + if s.source == "local": + parts.append( + '

Local preview, not a published capture. No receipt written.

' + ) + if not d: + return "".join(parts) + parts += [ + f"

Scope: {e(d['scope'])}. Producer revision: {e(d.get('producer_revision'))}. Generated: {e(d['generated_at'])}. {e(d.get('generation_basis', 'Producer-declared generation time'))}

", + f'

Project dashboard · Ingest receipt

', + "

Coverage and limitations

"
+        + e(_dump(d["coverage"]))
+        + "
    " + + "".join("
  • " + e(x) + "
    • " for x in d["limitations"]) + + "
", + ] + if not records(s): + parts.append("

No measurements. An empty feed does not mean scientific success.

") + for archived, label in [(False, "Current results"), (True, "Historical project results")]: + rows = [] + for r in records(s): + if r["archived"] != archived: + continue + paths = " · ".join( + f'result' for p in r["evidence_paths"] + ) + rows.append( + [ + e(r["target"]), + e(r["stage"] or ("pipeline parent" if r["pipeline"] else "single search")), + e(r["sampler"]), + e(r["configuration"].get("config_name")), + e(r["seed"]), + e(r["execution"]["status"]), + e(r["scientific"]["convergence"]) + " / " + e(r["scientific"]["acceptance"]), + e(r["timings"]["sampling_s"]), + e(r["timings"]["total_s"]), + e(r["diagnostics"]["status"]), + e(r["samples"]["availability"]), + paths, + ] + ) + parts += [ + f"

{label}

", + table( + [ + "Target", + "Stage", + "Sampler", + "Configuration", + "Seed", + "Execution", + "Convergence / acceptance", + "Sampler clock (s)", + "Fit call (s)", + "Diagnostics", + "Samples", + "Evidence", + ], + rows, + ), + ] + parts.append( + "

Comparisons and parity groups

Targets, seeds and stages are retained. No best-seed selection, cross-target ranking or automatic convergence threshold. Declared groups do not themselves certify parity.

" + ) + if not comparisons(s): + parts.append("

No certified comparison pairs supplied by this producer.

") + for c in comparisons(s): + reasons = summary.comparison_refusals(d, c) + parts.append( + "

" + + e(c["id"]) + + ": " + + e( + "refused: " + "; ".join(reasons) + if reasons + else "producer-declared protocol " + c["protocol"] + ) + + "

" + ) + parts.append("

Diagnostics, timing definitions and provenance

") + for r in records(s): + parts.append("
" + e(r["id"]) + "
") + for k in ( + "parent_run_id", + "stage", + "target", + "dataset", + "model", + "pipeline", + "sampler", + "configuration", + "backend", + "hardware", + "precision", + "seed", + "dependency_revisions", + "library_version", + "execution", + "scientific", + "timings", + "diagnostics", + "comparison", + "samples", + "measured_at", + "unknown_reasons", + ): + parts.append("
" + e(k) + "
" + e(_dump(r.get(k))) + "
") + parts.append("
") + return "".join(parts) + + +def render_html(views, now=None, campaign_data=None): + data = campaign_data if campaign_data is not None else campaigns.load() + return ( + '' + "PyAutoInsight dashboard" + '

PyAutoInsight

Question. Infer. Understand.

' + + input_marker(views, data) + + campaigns.render_html(data) + + "

Execution completion is separate from convergence and scientific acceptance. Cortex retains scientific conclusions. Evidence remains in project storage.

" + + '
' + + "".join(f"
{n}{label}
" for label, n in counts(views)) + + "
" + + "".join(_detail(s, now) for s in views) + + "
\n" + ) + + +def render_markdown(views, now=None, campaign_data=None): + data = campaign_data if campaign_data is not None else campaigns.load() + out = [ + "# PyAutoInsight — inference control room", + "", + input_marker(views, data), + "", + "| Where | Count |", + "|---|---:|", + ] + out += [f"| [{label}](#evidence) | {n} |" for label, n in counts(views)] + out += ["", campaigns.markdown(data)] + for s in views: + out += [ + "", + marker(s), + f"## {s.instance.repo}", + f"Integrity: {integrity(s)}. Freshness: {freshness(s, now)}. Qualification: {qualification(s)}.", + f"Capture source branch: {s.source_branch}; revision: {s.commit}; fetched {s.fetched_at}; latest attempt {s.attempt_at}.", + *s.errors, + ] + if s.cached: + out.append("Cached: original evidence and capture times retained.") + if not s.doc: + continue + out += [ + "", + "Coverage: " + _dump(s.doc["coverage"]), + *s.doc["limitations"], + "", + "| Target | Stage | Seed | Execution | Convergence | Acceptance | Sampling s | Total s | Archive | Evidence |", + "|---|---|---|---|---|---|---:|---:|---|---|", + ] + for r in records(s): + path = r["evidence_paths"][0] + vals = [ + r["target"], + r["stage"], + r["seed"], + r["execution"]["status"], + r["scientific"]["convergence"], + r["scientific"]["acceptance"], + r["timings"]["sampling_s"], + r["timings"]["total_s"], + r["archived"], + ] + out.append( + "| " + + " | ".join(md(v) for v in vals) + + f" | [result]({s.instance.blob_url(s.commit, path)}) |" + ) + out += [ + "", + f"
Provenance: {md(r['id'])}", + "", + "```json", + json.dumps(r, indent=2, ensure_ascii=False), + "```", + "", + "
", + "", + ] + for c in comparisons(s): + out.append( + f"Comparison {md(c['id'])}: " + + md(summary.comparison_refusals(s.doc, c) or c["protocol"]) + ) + return "\n".join(out) + "\n" + + +def render_state(views, updated=None, now=None): + items = [] + for s in views: + severity = ( + "red" + if s.failed and not s.cached + else "yellow" + if s.cached or not records(s) or summary.qualified_records(s.doc) < len(records(s)) + else "info" + ) + if ( + s.doc + and s.doc.get("valid_until") + and summary.parse_utc(now or _now()) > summary.parse_utc(s.doc["valid_until"]) + ): + severity = "yellow" if severity != "red" else severity + items.append( + { + "id": f"insight:{s.instance.instance}", + "severity": severity, + "text": f"{s.instance.instance}: {integrity(s)} · {len(records(s))} records · {qualification(s)} · {freshness(s, now)}", + "url": PAGES_URL + "#" + s.instance.instance, + "prompt": None, + } + ) + status = ( + "red" + if any(i["severity"] == "red" for i in items) + else "yellow" + if not items or any(i["severity"] == "yellow" for i in items) + else "green" + ) + return { + "schema_version": 1, + "organ": "insight", + "repo": "PyAutoInsight", + "status": status, + "headline": f"{len(views)} projects · {sum(len(records(s)) for s in views)} records · {sum(s.cached for s in views)} cached", + "updated": updated or _now(), + "pages_url": PAGES_URL, + "items": items, + } + + +def render_badge(views, state=None): + state = state or render_state(views) + return { + "schemaVersion": 1, + "label": "insight", + "message": state["headline"], + "color": {"red": "red", "yellow": "yellow", "green": "brightgreen"}[state["status"]], + } + + +def write(views, out_dir=ORGAN_ROOT, updated=None, now=None): + root = Path(out_dir) + root.mkdir(parents=True, exist_ok=True) + state = render_state(views, updated, now) + try: + prior = json.loads((root / "state.json").read_text()) + except (OSError, ValueError): + prior = {} + if updated is None and {k: v for k, v in prior.items() if k != "updated"} == { + k: v for k, v in state.items() if k != "updated" + }: + state["updated"] = prior["updated"] + files = { + "dashboard.md": render_markdown(views, now), + "dashboard.html": render_html(views, now), + "state.json": json.dumps(state, indent=2) + "\n", + "badge.json": json.dumps(render_badge(views, state), indent=2) + "\n", + } + for name, body in files.items(): + (root / name).write_text(body) + return [root / name for name in files] diff --git a/insight/campaigns.py b/insight/campaigns.py new file mode 100644 index 0000000..7d86008 --- /dev/null +++ b/insight/campaigns.py @@ -0,0 +1,256 @@ +"""Human-maintained inference intent; never infer scientific outcomes from timings.""" + +from __future__ import annotations + +import hashlib +import html +import json +import re +from datetime import date +from pathlib import Path +from urllib.parse import urlparse + +import yaml + +from insight import ORGAN_ROOT + +URL = "https://github.com/PyAutoLabs/PyAutoInsight/blob/main/" +STATUSES = { + "active", + "ready", + "running", + "blocked", + "parked", + "needs-decision", + "needs-slicing", + "complete", + "superseded", +} +CLOSED = {"complete", "superseded"} +PROMPT = """Use PyAutoInsight as the home for all inference work in this chat. Read PyAutoInsight/AGENTS.md and CHECKIN.md, then campaigns.yaml, the linked tasks and current project campaign ledgers. Check every open campaign and task for updates since the last check-in: results, jobs, PRs, releases, blockers and the next bounded step. Distinguish verified updates from stale or unavailable evidence; do not infer success from missing data. Give me one concise campaign-by-campaign summary and a proposed priority order. Apply any campaign direction or ideas I supply before or after this prompt; otherwise cover everything. Keep the overall check-in even when I focus one campaign. Update the Insight ledger with verified facts and dated source links, regenerate the board, and use the repository workflow to persist changes. Use Cortex for scientific observations and human conclusions, and the existing Brain samplers faculty for optional advice and project drivers for execution. Do not launch new compute, change defaults or baselines, or bypass a human/release gate merely to check in. Continue managing subsequent inference requests in this chat. Optional direction: [leave blank, name a campaign, or suggest an idea].""" + + +class CampaignError(ValueError): + """The control-room ledger is invalid or incomplete.""" + + +def load(root: Path = ORGAN_ROOT) -> dict: + root = Path(root) + path = root / "campaigns.yaml" + if not path.is_file(): + raise CampaignError(f"{path}: missing campaign ledger") + try: + data = yaml.safe_load(path.read_text()) + except yaml.YAMLError as exc: + raise CampaignError(str(exc)) from exc + if not isinstance(data, dict) or data.get("version") != 1: + raise CampaignError("campaign ledger must have version 1") + for kind in ("campaigns", "tasks"): + rows = data.get(kind) + if not isinstance(rows, list): + raise CampaignError(f"{kind} must be a list") + seen = set() + for row in rows: + if not isinstance(row, dict): + raise CampaignError(f"{kind}: expected a mapping") + for field in ("id", "title", "status", "updated", "next"): + if not isinstance(row.get(field), str) or not row[field].strip(): + raise CampaignError(f"{kind}: missing text {field}") + if not re.fullmatch(r"[a-z0-9][a-z0-9_-]*", row["id"]) or row["id"] in seen: + raise CampaignError(f"{kind}: invalid/duplicate id {row['id']}") + seen.add(row["id"]) + try: + date.fromisoformat(row["updated"]) + except ValueError as exc: + raise CampaignError(f"{row['id']}: invalid review date") from exc + if row["status"] not in STATUSES: + raise CampaignError(f"{row['id']}: unknown status") + for field in ("evidence", "issue"): + if field in row and ( + not isinstance(row[field], str) + or urlparse(row[field]).scheme != "https" + or not urlparse(row[field]).netloc + ): + raise CampaignError(f"{row['id']}: {field} must be an HTTPS URL") + ids = {c["id"] for c in data["campaigns"]} + for task in data["tasks"]: + if task.get("campaign") not in ids: + raise CampaignError(f"{task['id']}: unknown campaign") + relative = task.get("path", "") + if not isinstance(relative, str): + raise CampaignError(f"{task['id']}: task path must be text") + path = root / relative + if ( + not isinstance(relative, str) + or not relative.startswith("tasks/") + or not path.resolve().is_relative_to((root / "tasks").resolve()) + or not path.is_file() + ): + raise CampaignError(f"{task['id']}: missing or unsafe task path") + stamp = data.get("last_checkin") + if stamp is not None: + from insight.summary import parse_utc + + if parse_utc(stamp) is None: + raise CampaignError("last_checkin must be UTC or null") + report = data.get("last_checkin_report", "") + if not isinstance(report, str): + raise CampaignError("last_checkin_report must be text") + target = root / report + if ( + not report.startswith("checkins/") + or not target.resolve().is_relative_to((root / "checkins").resolve()) + or not target.is_file() + ): + raise CampaignError("last_checkin requires durable report") + return data + + +def marker(data: dict) -> str: + digest = hashlib.sha256(json.dumps(data, sort_keys=True).encode()).hexdigest() + return f"" + + +def _md(value) -> str: + return html.escape(str(value)).replace("|", "\\|").replace("\n", " ") + + +def markdown(data: dict) -> str: + rows = [ + marker(data), + "## Check in on all inference work", + "", + "Copy this into one chat. Add a campaign focus or idea before or after it, or leave it unchanged.", + "", + "```text", + PROMPT, + "```", + "", + f"Last check-in: {data.get('last_checkin') or 'not recorded yet'}. Ledger dates are review dates, not measurement freshness.", + "", + "## Active campaigns", + "", + "| Campaign | Status | Open tasks | Recent progress | Blockers | Job status | Next step | Reviewed |", + "|---|---|---:|---|---|---|---|---|", + ] + for c in data["campaigns"]: + if c["status"] in CLOSED: + continue + n = sum(t["campaign"] == c["id"] and t["status"] not in CLOSED for t in data["tasks"]) + title = f"[{_md(c['title'])}]({c['evidence']})" if c.get("evidence") else _md(c["title"]) + rows.append( + f"| {title} | {c['status']} | {n} | {_md(c.get('recent_progress', 'unknown'))} | {_md(c.get('blockers', 'unknown'))} | {_md(c.get('job_status', 'unknown'))} | {_md(c['next'])} | {c['updated']} |" + ) + rows += [ + "", + "## Active tasks", + "", + "Open means tracked, not necessarily running. Blockers and decisions still apply.", + "", + "| Task | Campaign | Status | Priority | Next step |", + "|---|---|---|---|---|", + ] + for t in data["tasks"]: + if t["status"] not in CLOSED: + rows.append( + f"| [{_md(t['title'])}]({URL}{t['path']}) | {t['campaign']} | {t['status']} | {_md(t.get('priority', 'normal'))} | {_md(t['next'])} |" + ) + rows += [ + "", + "Completed and superseded tasks remain in the [ledger](" + URL + "campaigns.yaml).", + "", + "## Inference evidence", + "", + ] + return "\n".join(rows) + + +def render_html(data: dict) -> str: + def e(value): + return html.escape(str(value), quote=True) + + campaigns = [] + for c in data["campaigns"]: + if c["status"] in CLOSED: + continue + n = sum(t["campaign"] == c["id"] and t["status"] not in CLOSED for t in data["tasks"]) + title = ( + f'{e(c["title"])}' + if c.get("evidence") + else e(c["title"]) + ) + campaigns.append( + f"{title}{e(c['status'])}{n}{e(c.get('recent_progress', 'unknown'))}{e(c.get('blockers', 'unknown'))}{e(c.get('job_status', 'unknown'))}{e(c['next'])}{e(c['updated'])}" + ) + tasks = [] + for t in data["tasks"]: + if t["status"] in CLOSED: + continue + issue = f' · issue' if t.get("issue") else "" + tasks.append( + f'{e(t["title"])}{issue}{e(t["campaign"])}{e(t["status"])}{e(t.get("priority", "normal"))}{e(t["next"])}' + ) + + def table(headers, rows): + return ( + '
' + + "".join(f"" for h in headers) + + "" + + "".join(rows) + + "
{h}
" + ) + + return ( + marker(data) + '

Check in on all inference work

' + "

One prompt, one ongoing chat. Add a campaign focus or idea here, or tell the chat before or afterwards.

" + f'' + '' + f'

Last check-in: {e(data.get("last_checkin") or "not recorded yet")}. Ledger dates are review dates, not measurement freshness.

' + '

Active campaigns

' + + table( + [ + "Campaign", + "Status", + "Open tasks", + "Recent progress", + "Blockers", + "Job status", + "Next step", + "Reviewed", + ], + campaigns, + ) + + '

Active tasks

Open means tracked, not necessarily running. Blockers and decisions still apply.

' + + table(["Task", "Campaign", "Status", "Priority", "Next step"], tasks) + + f'

Completed and superseded tasks remain in the ledger.

Inference evidence

' + ) + + +def validate_migration(root=ORGAN_ROOT): + root = Path(root) + try: + data = yaml.safe_load((root / "migration.yaml").read_text()) + except (OSError, yaml.YAMLError) as exc: + raise CampaignError(f"migration cannot be read: {exc}") from exc + if ( + not isinstance(data, dict) + or data.get("version") != 1 + or not isinstance(data.get("tasks"), list) + ): + raise CampaignError("migration requires version 1 and tasks list") + seen = set() + for row in data.get("tasks", []): + if not isinstance(row, dict) or not isinstance(row.get("destination"), str): + raise CampaignError("migration destination must be text") + dest = row["destination"] + path = root / dest + if ( + dest in seen + or not dest.startswith("tasks/") + or not path.resolve().is_relative_to((root / "tasks").resolve()) + ): + raise CampaignError("unsafe or duplicate migration destination") + seen.add(dest) + if not path.is_file() or hashlib.sha256(path.read_bytes()).hexdigest() != row.get("sha256"): + raise CampaignError(f"migration original changed: {dest}") + return data diff --git a/insight/cli.py b/insight/cli.py new file mode 100644 index 0000000..3f0a167 --- /dev/null +++ b/insight/cli.py @@ -0,0 +1,309 @@ +"""``pyauto-insight``: the organ's command line (check, board, census, fetch).""" + +from __future__ import annotations + +import argparse +import importlib.util +import json +import os +import sys +from pathlib import Path + +from insight import ORGAN_ROOT, board, campaigns, ingest, registry +from insight import summary as summary_mod + + +def _sources(values, instances) -> dict[str, Path]: + """Parse ``--from`` values: ``=``, or a bare ````. + + A bare path is matched to the instance whose repo name is the path's + basename, or to the only instance when the registry has just one. + """ + names = {i.instance for i in instances} + out = {} + for value in values or []: + name, sep, path = value.partition("=") + if sep and name in names: + out[name] = Path(path).expanduser() + continue + path = Path(value).expanduser() + base = path.resolve().name if path.is_dir() else None + match = [i.instance for i in instances if i.repo == base] + if not match and len(instances) == 1: + match = [instances[0].instance] + if len(match) != 1: + raise SystemExit(f"pyauto-insight: cannot tell which instance --from {value} is for") + out[match[0]] = path + return out + + +def _load(args): + instances = registry.load(args.registry, mind=getattr(args, "mind", None)) + return instances, _sources(getattr(args, "sources", None), instances) + + +def _ingest_all(args, instances, sources) -> list[ingest.Snapshot]: + return [ + ingest.ingest( + inst, + args.out, + offline=getattr(args, "offline", False), + local_path=sources.get(inst.instance), + ) + for inst in instances + ] + + +def _say(s: ingest.Snapshot) -> str: + key = s.instance.instance + if s.doc: + line = ( + f"{key}: {board.integrity(s)}; {len(board.records(s))} records at " + f"{(s.commit or 'unknown')[:8]}; {board.freshness(s)}; {board.qualification(s)}" + ) + else: + line = f"{key}: {board.integrity(s)}" + if s.failed: + line += f" — {'; '.join(s.errors)}" + return line + + +def cmd_board(args) -> int: + instances, sources = _load(args) + views = _ingest_all(args, instances, sources) + for path in board.write(views, args.out): + print(f"wrote {path}") + for s in views: + print(_say(s), file=sys.stderr if s.failed else sys.stdout) + return 0 + + +def cmd_fetch(args) -> int: + instances = registry.load(args.registry, mind=args.mind) + chosen = [registry.get(instances, args.instance)] if args.instance else instances + rc = 0 + for inst in chosen: + s = ingest.ingest(inst, args.out) + receipt = ingest.read_receipt(args.out, inst.instance) or {} + print( + f"{inst.instance}: {receipt.get('outcome')} at " + f"{(receipt.get('resolved_commit') or 'unresolved')[:12]} " + f"(receipt {ingest.receipt_path(args.out, inst.instance)})" + ) + if s.failed: + print(f" {'; '.join(s.errors)}", file=sys.stderr) + rc = 1 + return rc + + +def cmd_census(args) -> int: + instances, sources = _load(args) + for s in _ingest_all(argparse.Namespace(out=args.out, offline=True), instances, sources): + key = s.instance.instance + if not s.doc: + print(f"{key}: no capture ({board.integrity(s)})") + continue + doc = s.doc + print( + f"{key}: {len(board.records(s))} records · {len(board.comparisons(s))} comparisons · " + f"{len(doc.get('limitations') or [])} limitations · " + f"{summary_mod.qualified_records(doc)} qualified · {board.integrity(s)} · " + f"{(s.commit or 'unknown')[:8]}" + ) + return 0 + + +def _state_validator(): + """The Brain's cockpit-feed validator (board/_state.py), or None. + + Looked for at $PYAUTO_BRAIN, then beside this organ (flat or grouped + ``organs/`` layouts both put PyAutoBrain next to PyAutoInsight). + """ + candidates = [] + if os.environ.get("PYAUTO_BRAIN"): + candidates.append(Path(os.environ["PYAUTO_BRAIN"]).expanduser()) + candidates.append(ORGAN_ROOT.parent / "PyAutoBrain") + for root in candidates: + path = root / "board" / "_state.py" + if path.is_file(): + spec = importlib.util.spec_from_file_location("_brain_state", path) + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + return mod + return None + + +def _check_state(path: Path) -> list[str]: + """state.json exists, parses, and (when the Brain is here) meets contract v1.""" + if not path.is_file(): + print("FAIL state: state.json missing — run `pyauto-insight board`") + return ["state.json missing"] + try: + state = json.loads(path.read_text()) + except ValueError as exc: + print(f"FAIL state: state.json unreadable ({exc})") + return [f"state.json unreadable: {exc}"] + validator = _state_validator() + if validator is None: + print("skip state: no PyAutoBrain here to validate state.json (set PYAUTO_BRAIN)") + return [] + errors = validator.validate_state(state) + if errors: + print(f"FAIL state: {'; '.join(errors)}") + return [f"state.json: {e}" for e in errors] + print(f"ok state: valid against the Brain contract ({state['status']}, {state['headline']})") + return [] + + +def cmd_check(args) -> int: + problems = [] + try: + campaign_data = campaigns.load() + campaigns.validate_migration() + except campaigns.CampaignError as exc: + print(f"FAIL campaigns: {exc}") + return 1 + print( + f"ok campaigns: {len(campaign_data['campaigns'])} campaigns, {len(campaign_data['tasks'])} tasks" + ) + try: + body_map = registry.load_body_map(args.mind) + print(f"ok body map: {registry.body_map_path(args.mind)}") + instances = registry.load(args.registry, body_map=body_map) + except registry.RegistryError as exc: + print(f"FAIL registry: {exc}") + return 1 + print( + f"ok registry: {len(instances)} instance(s) " + f"({', '.join(f'{i.instance}→{i.github}' for i in instances)})" + ) + sources = _sources(args.sources, instances) + views = _ingest_all(args, instances, sources) + mode = "committed receipts + snapshots" if args.offline else "fetched at one resolved commit" + for s in views: + key = s.instance.instance + if s.failed: + problems.append(f"{key}: {s.outcome}: {'; '.join(s.errors)}") + print(f"FAIL {key}: {s.outcome}: {'; '.join(s.errors)}") + continue + if s.source == "remote": + receipt = ingest.read_receipt(args.out, key) + if not receipt or receipt.get("resolved_commit") != s.commit: + problems.append(f"{key}: receipt does not record the snapshot commit") + print(f"FAIL {key}: receipt does not record the snapshot commit") + continue + # The committed snapshot must still validate under this reader. + outcome, errors = summary_mod.classify( + s.doc, (s.instance.schema_name, s.instance.schema_version) + ) + if outcome != "ok": + problems.append(f"{key}: snapshot {outcome}: {'; '.join(errors)}") + print(f"FAIL {key}: snapshot {outcome}: {'; '.join(errors[:5])}") + continue + print( + f"ok {key}: {s.doc['schema']}@{s.doc['version']} valid, " + f"{len(board.records(s))} records, {len(board.comparisons(s))} comparisons at " + f"{(s.commit or 'unknown')[:12]} ({mode if s.source == 'remote' else 'local read'})" + ) + out = Path(args.out) + md, page = out / "dashboard.md", out / "dashboard.html" + if not md.is_file() or not page.is_file(): + problems.append("dashboard.md / dashboard.html missing") + print("FAIL dashboard: dashboard.md / dashboard.html missing — run `pyauto-insight board`") + else: + expected = campaigns.marker(campaign_data) + digest = board.input_marker(views, campaign_data) + if digest not in md.read_text() or digest not in page.read_text(): + problems.append("dashboard evidence inputs changed — regenerate board") + if expected not in md.read_text() or expected not in page.read_text(): + problems.append("campaign dashboard stale — run pyauto-insight board") + print("FAIL campaigns: dashboard does not match the campaign ledger") + recorded = board.markers(md.read_text()) + names = {i.instance for i in instances} + stale = sorted( + s.instance.instance + for s in views + if s.source == "remote" + and recorded.get(s.instance.instance) + != { + "receipt": s.attempt_commit or "none", + "outcome": s.outcome, + "shown": s.commit or "none", + } + ) + extra = sorted(set(recorded) - names) + if stale or extra: + problems.append(f"dashboard stale: {stale + extra}") + print( + f"FAIL dashboard: stale for {', '.join(stale + extra)} — run `pyauto-insight board`" + ) + else: + print("ok dashboard: current with every receipt") + problems += _check_state(out / "state.json") + print("check: " + ("FAIL" if problems else "OK")) + return 1 if problems else 0 + + +def main(argv=None) -> int: + parser = argparse.ArgumentParser( + prog="pyauto-insight", + description="PyAutoInsight: the cross-project inference dashboard organ.", + ) + parser.add_argument("--registry", default=str(registry.REGISTRY_PATH), help=argparse.SUPPRESS) + sub = parser.add_subparsers(dest="cmd", required=True) + + def common(p, reading=True): + p.add_argument( + "--mind", + metavar="PATH", + help="the PyAutoMind checkout whose repos.yaml resolves each instance's repo " + "(default: $PYAUTO_MIND, else $PYAUTO_ROOT or beside this organ)", + ) + p.add_argument("--out", default=str(ORGAN_ROOT), help=argparse.SUPPRESS) + if reading: + p.add_argument( + "--offline", + action="store_true", + help="use the committed receipts + snapshots, never the network", + ) + p.add_argument( + "--from", + dest="sources", + action="append", + metavar="[INSTANCE=]PATH", + help="read an instance's summary from this local checkout or file " + "(labelled as a local read; writes no receipt)", + ) + + p = sub.add_parser( + "board", help="ingest, then render dashboard.md + dashboard.html + badge.json + state.json" + ) + common(p) + p.set_defaults(func=cmd_board) + p = sub.add_parser( + "check", + help="registry + body map valid, every summary ingests and validates, receipt written, " + "dashboard current, state.json valid", + ) + common(p) + p.set_defaults(func=cmd_check) + p = sub.add_parser( + "census", help="one line per instance from the committed snapshots (no network)" + ) + common(p, reading=False) + p.set_defaults(func=cmd_census, sources=None) + p = sub.add_parser("fetch", help="ingest and write receipts only (no render)") + common(p, reading=False) + p.add_argument("--instance", metavar="K", help="only this instance") + p.set_defaults(func=cmd_fetch) + + args = parser.parse_args(argv) + try: + return args.func(args) + except registry.RegistryError as exc: + print(f"pyauto-insight: {exc}", file=sys.stderr) + return 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/insight/ingest.py b/insight/ingest.py new file mode 100644 index 0000000..7c35e1a --- /dev/null +++ b/insight/ingest.py @@ -0,0 +1,340 @@ +"""Ingest one instance's summary at one resolved commit, with a receipt. + +An online ingest resolves the instance's branch to **one commit** through the +GitHub API and reads ``summary_path`` at that SHA from +``raw.githubusercontent.com`` — never at a moving branch name, so every value +the board shows traces to a captured commit. Each attempt writes a receipt +(``receipts/.json``: resolved commit, fetch time, outcome, errors). +A good read also writes the last-good snapshot (``snapshots/.json``: +the summary plus the commit and time it was captured). A failed read keeps +that snapshot and the board shows it labelled **cached**, with its original +evidence and fetch times and the new error: nothing re-ages it. + +Receipts and snapshots are written only when their content changes (the +``fetched_at`` of an unchanged outcome at the same commit is carried), so a +nightly refresh against an unchanged project commits nothing. A local +``--from`` read reports the checkout's revision and dirty state, is labelled +"local checkout", and writes no receipt or snapshot: it cannot masquerade as +a published capture. +""" + +from __future__ import annotations + +import json +import os +import re +import subprocess +import urllib.error +import urllib.request +from dataclasses import dataclass, field +from datetime import UTC, datetime +from pathlib import Path + +from insight import ORGAN_ROOT +from insight import summary as summary_mod +from insight.registry import Instance + +API_COMMIT = "https://api.github.com/repos/{github}/commits/{branch}" +RAW_FILE = "https://raw.githubusercontent.com/{github}/{sha}/{path}" +TIMEOUT = 20 +USER_AGENT = "PyAutoInsight-ingest (+https://github.com/PyAutoLabs/PyAutoInsight)" +OUTCOMES = ("ok", "invalid", "unavailable", "unsupported") +_SHA = re.compile(r"^[0-9a-f]{40}$") + + +class FetchError(Exception): + """A transport failure: the API or the raw file could not be read.""" + + +def utc_now() -> str: + return datetime.now(UTC).strftime("%Y-%m-%dT%H:%M:%SZ") + + +# --------------------------------------------------------------- network --- + + +def _get(url: str, accept: str | None = None) -> bytes: + headers = {"User-Agent": USER_AGENT} + if accept: + headers["Accept"] = accept + token = os.environ.get("GH_TOKEN") + if token and url.startswith("https://api.github.com/"): + headers["Authorization"] = f"Bearer {token}" + request = urllib.request.Request(url, headers=headers) + try: + with urllib.request.urlopen(request, timeout=TIMEOUT) as response: + return response.read() + except (urllib.error.URLError, OSError, ValueError) as exc: + raise FetchError(f"GET {url}: {exc}") from exc + + +def resolve_commit(github: str, branch: str = "main") -> str: + """The one commit ``branch`` points at now (40-hex SHA).""" + body = _get(API_COMMIT.format(github=github, branch=branch), "application/vnd.github+json") + try: + sha = json.loads(body)["sha"] + except (ValueError, KeyError, TypeError) as exc: + raise FetchError(f"{github}@{branch}: unreadable commit response ({exc})") from exc + if not (isinstance(sha, str) and _SHA.match(sha)): + raise FetchError(f"{github}@{branch}: commit response has no 40-hex sha") + return sha + + +def fetch_at(github: str, sha: str, path: str) -> bytes: + """``path`` exactly as it is at commit ``sha``.""" + return _get(RAW_FILE.format(github=github, sha=sha, path=path)) + + +# -------------------------------------------------------------- snapshot --- + + +@dataclass +class Snapshot: + """What the board shows for one instance. + + ``outcome``/``errors``/``attempt_*`` describe the latest attempt; + ``doc``/``commit``/``fetched_at`` describe the summary shown, which is the + last good capture when the latest attempt failed (``cached``). + """ + + instance: Instance + outcome: str + source: str # remote | local | none + doc: dict | None = None + commit: str | None = None + fetched_at: str | None = None + errors: list[str] = field(default_factory=list) + cached: bool = False + attempt_commit: str | None = None + attempt_at: str | None = None + dirty: bool | None = None + checkout: str | None = None + source_branch: str | None = None + + @property + def failed(self) -> bool: + return self.outcome != "ok" + + +def receipt_path(out_dir: Path, name: str) -> Path: + return Path(out_dir) / "receipts" / f"{name}.json" + + +def snapshot_path(out_dir: Path, name: str) -> Path: + return Path(out_dir) / "snapshots" / f"{name}.json" + + +def _read_json(path: Path) -> dict | None: + try: + doc = json.loads(path.read_text()) + except (OSError, ValueError): + return None + return doc if isinstance(doc, dict) else None + + +def _write_json(path: Path, doc: dict, indent: int = 2) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + tmp = path.with_name(path.name + ".tmp") + tmp.write_text(json.dumps(doc, indent=indent, ensure_ascii=False) + "\n") + tmp.replace(path) + + +def read_receipt(out_dir: Path, name: str) -> dict | None: + return _read_json(receipt_path(out_dir, name)) + + +def read_snapshot(out_dir: Path, name: str) -> dict | None: + snap = _read_json(snapshot_path(out_dir, name)) + return snap if snap and isinstance(snap.get("summary"), dict) else None + + +def _carry(path: Path, doc: dict, keys=("fetched_at",)) -> bool: + """Write ``doc`` unless the file already holds it apart from ``keys``. + + Returns True when written. Carrying keeps re-ingests of an unchanged + outcome at an unchanged commit from rewriting (and re-aging) the file. + """ + prior = _read_json(path) + if prior is not None: + strip = lambda d: {k: v for k, v in d.items() if k not in keys} # noqa: E731 + if strip(prior) == strip(doc): + return False + _write_json(path, doc) + return True + + +# ---------------------------------------------------------------- ingest --- + + +def _from_files(inst: Instance, out_dir: Path) -> Snapshot: + """The committed receipt + last-good snapshot, read with no network.""" + receipt = read_receipt(out_dir, inst.instance) + snap = read_snapshot(out_dir, inst.instance) + if receipt is None: + view = Snapshot(inst, "unavailable", "none", errors=["no receipt yet (never fetched)"]) + else: + view = Snapshot( + inst, + receipt.get("outcome") if receipt.get("outcome") in OUTCOMES else "invalid", + "remote", + errors=[str(e) for e in receipt.get("errors") or []], + attempt_commit=receipt.get("resolved_commit"), + attempt_at=receipt.get("fetched_at"), + source_branch=receipt.get("branch"), + ) + if receipt is not None: + problems = [] + for key, expected in ( + ("instance", inst.instance), + ("repo", inst.repo), + ("github", inst.github), + ("summary_path", inst.summary_path), + ): + if receipt.get(key) != expected: + problems.append(f"receipt {key} differs from registry") + if summary_mod.parse_utc(receipt.get("fetched_at")) is None: + problems.append("receipt capture time is invalid") + resolved = receipt.get("resolved_commit") + if resolved is not None and (not isinstance(resolved, str) or not _SHA.fullmatch(resolved)): + problems.append("receipt commit is invalid") + if receipt.get("outcome") == "ok" and (snap is None or resolved != snap.get("commit")): + problems.append("receipt does not match snapshot commit") + if problems: + view.outcome = "invalid" + view.errors += problems + if snap is not None: + outcome, errors = summary_mod.classify( + snap["summary"], (inst.schema_name, inst.schema_version) + ) + if snap.get("repo") != inst.repo or snap["summary"].get("project") != inst.repo: + errors.append("cached project identity differs from registry") + if snap.get("github") != inst.github or snap.get("summary_path") != inst.summary_path: + errors.append("cached source differs from registry") + if not isinstance(snap.get("commit"), str) or not _SHA.fullmatch(snap["commit"]): + errors.append("cached commit is invalid") + if summary_mod.parse_utc(snap.get("fetched_at")) is None: + errors.append("cached capture time is invalid") + if outcome != "ok" or errors: + view.outcome, view.errors = "invalid", errors + return view + view.doc = snap["summary"] + view.commit = snap.get("commit") + view.fetched_at = snap.get("fetched_at") + view.source = "remote" + view.cached = view.failed + return view + + +def ingest( + inst: Instance, + out_dir: Path = ORGAN_ROOT, + *, + offline: bool = False, + local_path: Path | str | None = None, + now: str | None = None, +) -> Snapshot: + """Read one instance and return what the board shows for it. + + ``offline``: the committed receipt + snapshot only (no network, no + write). ``local_path``: a local checkout (or the summary file itself), + labelled as such, writing nothing. Otherwise: resolve, fetch, validate, + write the receipt (and the snapshot on success). + """ + out_dir = Path(out_dir) + wanted = (inst.schema_name, inst.schema_version) + if local_path is not None: + return _ingest_local(inst, Path(local_path).expanduser(), wanted) + if offline: + return _from_files(inst, out_dir) + + now = now or utc_now() + commit, doc, errors, outcome = None, None, [], "ok" + try: + commit = resolve_commit(inst.github, inst.branch) + raw = fetch_at(inst.github, commit, inst.summary_path) + try: + doc = json.loads(raw) + except ValueError as exc: + outcome, errors = "invalid", [f"{inst.summary_path} is not JSON ({exc})"] + else: + outcome, errors = summary_mod.classify(doc, wanted) + if outcome == "ok" and doc.get("project") != inst.repo: + outcome, errors = "invalid", ["project identity differs from registry"] + except FetchError as exc: + outcome, errors = "unavailable", [str(exc)] + + snap_file = snapshot_path(out_dir, inst.instance) + if outcome == "ok": + _carry( + snap_file, + { + "instance": inst.instance, + "repo": inst.repo, + "github": inst.github, + "summary_path": inst.summary_path, + "commit": commit, + "fetched_at": now, + "summary": doc, + }, + ) + last_good = read_snapshot(out_dir, inst.instance) + receipt = { + "instance": inst.instance, + "repo": inst.repo, + "github": inst.github, + "branch": inst.branch, + "resolved_commit": commit, + "summary_path": inst.summary_path, + "source": "remote", + "fetched_at": now, + "outcome": outcome, + "schema": doc.get("schema") if isinstance(doc, dict) else None, + "version": doc.get("version") if isinstance(doc, dict) else None, + "record_count": len(doc["records"]) + if isinstance(doc, dict) and isinstance(doc.get("records"), list) + else None, + "errors": errors, + "cached_from": last_good.get("fetched_at") if last_good and outcome != "ok" else None, + } + _carry(receipt_path(out_dir, inst.instance), receipt) + return _from_files(inst, out_dir) + + +def _git(checkout: Path, *args: str) -> str | None: + try: + proc = subprocess.run( + ["git", "-C", str(checkout), *args], capture_output=True, text=True, timeout=20 + ) + except (OSError, subprocess.SubprocessError): + return None + return proc.stdout if proc.returncode == 0 else None + + +def _ingest_local(inst: Instance, path: Path, wanted) -> Snapshot: + file = path / inst.summary_path if path.is_dir() else path + view = Snapshot(inst, "ok", "local", checkout=str(file.parent), fetched_at=utc_now()) + view.attempt_at = view.fetched_at + top = _git(file.parent, "rev-parse", "--show-toplevel") + if top: + checkout = Path(top.strip()) + view.checkout = str(checkout) + head = _git(checkout, "rev-parse", "HEAD") + view.commit = head.strip() if head else None + status = _git(checkout, "status", "--porcelain") + view.dirty = bool(status.strip()) if status is not None else None + view.attempt_commit = view.commit + try: + view.doc = json.loads(file.read_text()) + except OSError as exc: + view.outcome, view.errors = "unavailable", [f"{file}: {exc}"] + return view + except ValueError as exc: + view.outcome, view.errors = "invalid", [f"{file} is not JSON ({exc})"] + view.doc = None + return view + view.outcome, view.errors = summary_mod.classify(view.doc, wanted) + if view.outcome == "ok" and view.doc.get("project") != inst.repo: + view.outcome, view.errors = "invalid", ["project identity differs from registry"] + if view.failed: + view.doc = None + return view diff --git a/insight/registry.py b/insight/registry.py new file mode 100644 index 0000000..6bdbb1a --- /dev/null +++ b/insight/registry.py @@ -0,0 +1,281 @@ +"""Read and validate ``registry.yaml``, the one-row-per-instance registry. + +A row names its project by a PyAutoMind body-map identity (``repo``). The +project's location (``path``) and GitHub home (``github``) are read from +``PyAutoMind/repos.yaml`` when the registry is loaded, so the organ never keeps +a second catalogue of repositories (the spec's "Proposed project registry"). +""" + +from __future__ import annotations + +import os +import re +from dataclasses import dataclass +from pathlib import Path +from urllib.parse import quote, urlparse + +import yaml + +from insight import ORGAN_ROOT +from insight.summary import safe_relative_path + +REGISTRY_PATH = ORGAN_ROOT / "registry.yaml" +REGISTRY_SCHEMA = 1 +MIND_REPO = "PyAutoMind" # an organ name, not an instance fact +BODY_MAP = "repos.yaml" +BRANCH = "main" # the branch every ingest resolves to one commit + +REQUIRED = ("instance", "repo", "summary_path", "supported_schema", "dashboard_url") +FIELDS = (*REQUIRED, "library_refs", "cortex_project") + +_KEY = re.compile(r"^[a-z][a-z0-9_-]*$") +_GITHUB = re.compile(r"^[A-Za-z0-9_.-]+/[A-Za-z0-9_.-]+$") +_SCHEMA = re.compile(r"^([a-z][a-z0-9-]*)@([1-9][0-9]*)$") + + +class RegistryError(ValueError): + """``registry.yaml`` (or the body map it resolves through) is malformed; + the message lists every problem.""" + + +@dataclass(frozen=True) +class Instance: + instance: str + repo: str + summary_path: str + supported_schema: str + dashboard_url: str + library_refs: tuple[str, ...] = () + cortex_project: str | None = None + # Resolved from the body map, never written in registry.yaml. + path: str = "" + github: str = "" + branch: str = BRANCH + + @property + def name(self) -> str: + return self.instance + + @property + def github_url(self) -> str: + return f"https://github.com/{self.github}" + + @property + def schema_name(self) -> str: + return _SCHEMA.match(self.supported_schema)[1] + + @property + def schema_version(self) -> int: + return int(_SCHEMA.match(self.supported_schema)[2]) + + def blob_url(self, commit: str | None, path: str) -> str: + """The GitHub page of ``path`` at ``commit`` (the branch when unknown).""" + return f"{self.github_url}/blob/{quote(commit or self.branch, safe='')}/{quote(path, safe='/')}" + + def tree_url(self, commit: str | None) -> str: + return f"{self.github_url}/tree/{quote(commit or self.branch, safe='')}" + + +# ------------------------------------------------------------- body map --- + + +def workspace_roots(root: Path | None = None) -> list[Path]: + """Candidate workspace roots, most specific first.""" + if root is not None: + return [Path(root)] + roots = [] + env = os.environ.get("PYAUTO_ROOT") + if env: + roots.append(Path(env).expanduser()) + # organs/PyAutoInsight (grouped canonical) or /PyAutoInsight (flat bundle). + roots += [ORGAN_ROOT.parent.parent, ORGAN_ROOT.parent] + return roots + + +def mind_candidates(mind: Path | str | None = None) -> list[Path]: + """Where the PyAutoMind checkout is looked for, in order. + + An explicit ``--mind PATH`` first, then ``$PYAUTO_MIND``, then + ``$PYAUTO_ROOT/organs/PyAutoMind`` and ``$PYAUTO_ROOT/PyAutoMind``, then + beside this organ (grouped ``organs/`` or flat bundle layout). + """ + out: list[Path] = [] + if mind is not None: + out.append(Path(mind).expanduser()) + env = os.environ.get("PYAUTO_MIND") + if env: + out.append(Path(env).expanduser()) + for base in workspace_roots(): + out += [base / "organs" / MIND_REPO, base / MIND_REPO] + return out + + +def body_map_path(mind: Path | str | None = None) -> Path: + """``PyAutoMind/repos.yaml``; raise RegistryError when none is found. + + An explicit ``mind`` that holds no body map is an error, never a silent + fall-through to another checkout. + """ + if mind is not None: + path = Path(mind).expanduser() / BODY_MAP + if path.is_file(): + return path + raise RegistryError(f"--mind {mind}: no {BODY_MAP} there") + for candidate in mind_candidates(): + path = candidate / BODY_MAP + if path.is_file(): + return path + raise RegistryError( + f"cannot find {MIND_REPO}/{BODY_MAP} (the body map that resolves each instance's " + "repo); pass --mind PATH or set PYAUTO_MIND" + ) + + +def load_body_map(mind: Path | str | None = None) -> dict[str, dict]: + """``{repo name: {path, github, …}}`` from the Mind body map.""" + path = body_map_path(mind) + try: + doc = yaml.safe_load(path.read_text()) + except (OSError, yaml.YAMLError) as exc: + raise RegistryError(f"{path}: {exc}") from exc + repos = doc.get("repos") if isinstance(doc, dict) else None + if not isinstance(repos, dict): + raise RegistryError(f"{path}: no `repos:` mapping") + return {str(k): v for k, v in repos.items() if isinstance(v, dict)} + + +# ------------------------------------------------------------- registry --- + + +def _safe_relative(value: str) -> bool: + return safe_relative_path(value) + + +def _https_url(value): + try: + parsed = urlparse(value) + return parsed.scheme == "https" and bool(parsed.netloc) + except ValueError: + return False + + +def validate(data, body_map: dict[str, dict] | None = None) -> list[str]: + """Return every problem with a parsed registry document (empty = valid). + + With ``body_map`` (``load_body_map()``), each ``repo`` and every + ``library_refs`` entry must also be a body-map identity, and each repo must + carry a ``github`` home there. + """ + if not isinstance(data, dict): + return ["registry is not a mapping"] + problems = [] + if type(data.get("schema")) is not int or data.get("schema") != REGISTRY_SCHEMA: + problems.append(f"schema must be {REGISTRY_SCHEMA}, got {data.get('schema')!r}") + rows = data.get("instances") + if not isinstance(rows, list) or not rows: + return problems + ["instances must be a non-empty list"] + seen: set[str] = set() + scopes: dict[tuple[str, str], str] = {} + for i, row in enumerate(rows): + where = f"instances[{i}]" + if not isinstance(row, dict): + problems.append(f"{where} is not a mapping") + continue + where = f"instance {row.get('instance', i)!r}" + for key in REQUIRED: + value = row.get(key) + if not isinstance(value, str) or not value.strip(): + problems.append(f"{where}: {key} is missing or empty") + unknown = sorted(str(k) for k in set(row) - set(FIELDS)) + if unknown: + problems.append(f"{where}: unknown field(s) {', '.join(unknown)}") + key = row.get("instance") + if isinstance(key, str): + if not _KEY.match(key): + problems.append(f"{where}: instance must match {_KEY.pattern}") + if key in seen: + problems.append(f"{where}: duplicate instance") + seen.add(key) + path = row.get("summary_path") + if isinstance(path, str) and not _safe_relative(path): + problems.append(f"{where}: summary_path must be a relative path inside the repo") + schema = row.get("supported_schema") + if isinstance(schema, str) and not _SCHEMA.match(schema): + problems.append(f"{where}: supported_schema must be @") + url = row.get("dashboard_url") + if isinstance(url, str) and not _https_url(url): + problems.append(f"{where}: dashboard_url must be an https:// URL") + refs = row.get("library_refs", []) + if refs is None: + refs = [] + if not isinstance(refs, list) or not all(isinstance(r, str) and r for r in refs): + problems.append(f"{where}: library_refs must be a list of body-map repo names") + refs = [] + cortex = row.get("cortex_project") + if cortex is not None and not (isinstance(cortex, str) and cortex.strip()): + problems.append(f"{where}: cortex_project must be a Cortex key or null") + repo = row.get("repo") + if isinstance(repo, str) and isinstance(path, str): + # Disjoint scopes: one summary is registered once, never twice under + # two names to inflate coverage. + other = scopes.get((repo, path)) + if other is not None: + problems.append(f"{where}: {repo}:{path} is already registered as {other!r}") + scopes[(repo, path)] = str(key) + if body_map is not None: + if isinstance(repo, str) and repo: + entry = body_map.get(repo) + if not isinstance(entry, dict): + problems.append(f"{where}: repo {repo!r} is not in the body map") + else: + gh = entry.get("github") + if not (isinstance(gh, str) and _GITHUB.match(gh)): + problems.append(f"{where}: body map has no github owner/repo for {repo!r}") + missing = [r for r in refs if r not in body_map] + if missing: + problems.append(f"{where}: library_refs not in the body map: {', '.join(missing)}") + return problems + + +def load( + path: Path | str = REGISTRY_PATH, + mind: Path | str | None = None, + body_map: dict[str, dict] | None = None, +) -> list[Instance]: + """Parse and validate the registry, resolving each repo through the body + map; raise RegistryError on any problem.""" + path = Path(path) + try: + data = yaml.safe_load(path.read_text()) + except (OSError, yaml.YAMLError) as exc: + raise RegistryError(f"{path}: {exc}") from exc + if body_map is None: + body_map = load_body_map(mind) + problems = validate(data, body_map) + if problems: + raise RegistryError(f"{path}:\n " + "\n ".join(problems)) + out = [] + for row in data["instances"]: + entry = body_map[row["repo"]] + out.append( + Instance( + instance=row["instance"], + repo=row["repo"], + summary_path=row["summary_path"], + supported_schema=row["supported_schema"], + dashboard_url=row["dashboard_url"], + library_refs=tuple(row.get("library_refs") or ()), + cortex_project=row.get("cortex_project"), + path=str(entry.get("path") or row["repo"]), + github=str(entry["github"]), + ) + ) + return out + + +def get(instances: list[Instance], name: str) -> Instance: + for instance in instances: + if instance.instance == name: + return instance + known = ", ".join(i.instance for i in instances) + raise RegistryError(f"no instance {name!r} in the registry (known: {known})") diff --git a/insight/summary.py b/insight/summary.py new file mode 100644 index 0000000..9541e93 --- /dev/null +++ b/insight/summary.py @@ -0,0 +1,347 @@ +"""Validate inference-summary v1 without interpreting scientific quality.""" + +from __future__ import annotations + +import math +import re +from datetime import datetime +from urllib.parse import unquote + +SHA = re.compile(r"^[0-9a-f]{40}$") + + +def parse_utc(value): + if not isinstance(value, str): + return None + try: + dt = datetime.fromisoformat(value.replace("Z", "+00:00")) + return dt if dt.utcoffset() is not None and dt.utcoffset().total_seconds() == 0 else None + except ValueError: + return None + + +def safe_relative_path(value): + if not isinstance(value, str) or not value: + return False + decoded = unquote(value) + return ( + not decoded.startswith("/") + and not any(c in decoded for c in ("\\", ":", "?", "#")) + and all(p not in ("", ".", "..") for p in decoded.split("/")) + and not any(ord(c) < 32 for c in decoded) + ) + + +def _text(value): + return isinstance(value, str) and bool(value.strip()) + + +def _finite(value): + if isinstance(value, float): + return math.isfinite(value) + if isinstance(value, dict): + return all(_finite(v) for v in value.values()) + if isinstance(value, list): + return all(_finite(v) for v in value) + return True + + +def supported(doc, wanted=None): + if not isinstance(doc, dict): + return "summary is not an object" + got = doc.get("schema"), doc.get("version") + if got[0] != "inference-summary" or type(got[1]) is not int or got[1] != 1: + return f"unsupported schema {got!r}" + if wanted is not None and got != tuple(wanted): + return "summary schema differs from registry" + return None + + +def validate(doc): + if not isinstance(doc, dict): + return ["summary is not an object"] + required = ( + "schema", + "version", + "project", + "scope", + "generated_at", + "evidence_updated_at", + "producer_revision", + "coverage", + "records", + "comparisons", + "comparison_policy", + "limitations", + ) + errors = [f"missing required field {k}" for k in required if k not in doc] + if not _finite(doc): + errors.append("non-finite metric in summary") + for k in ("project", "scope"): + if not _text(doc.get(k)): + errors.append(f"{k} must be non-empty text") + if parse_utc(doc.get("generated_at")) is None: + errors.append("generated_at must be UTC") + for k in ("evidence_updated_at", "valid_until"): + v = doc.get(k) + if v is not None and parse_utc(v) is None: + errors.append(f"{k} must be UTC or null") + if doc.get("evidence_updated_at") is None and not _text(doc.get("evidence_updated_at_reason")): + errors.append("unknown evidence_updated_at needs reason") + ev, vu = parse_utc(doc.get("evidence_updated_at")), parse_utc(doc.get("valid_until")) + if ev and vu and vu < ev: + errors.append("valid_until precedes evidence_updated_at") + rev = doc.get("producer_revision") + if rev is not None and (not isinstance(rev, str) or not SHA.fullmatch(rev)): + errors.append("producer_revision must be full SHA or null") + if rev is None and not _text(doc.get("producer_revision_reason")): + errors.append("unknown producer_revision needs reason") + if "source_commit" in doc and ( + not isinstance(doc["source_commit"], str) or not SHA.fullmatch(doc["source_commit"]) + ): + errors.append("source_commit must be full SHA when supplied") + if not isinstance(doc.get("comparison_policy"), dict): + errors.append("comparison_policy must be object") + lim = doc.get("limitations") + if not isinstance(lim, list) or not all(_text(x) for x in lim): + errors.append("limitations must be text list") + cov = doc.get("coverage") + if ( + not isinstance(cov, dict) + or not isinstance(cov.get("expected"), dict) + or not isinstance(cov.get("observed"), dict) + ): + errors.append("coverage requires expected and observed objects") + else: + if any(type(v) is not int or v < 0 for v in cov["observed"].values()): + errors.append("coverage observed counts must be nonnegative integers") + excluded = cov.get("excluded", []) + if not isinstance(excluded, list): + errors.append("coverage exclusions must be a list") + else: + for x in excluded: + if not isinstance(x, dict) or not _text(x.get("reason")): + errors.append("coverage exclusion requires path or id and reason") + elif "path" in x: + if not safe_relative_path(x["path"]): + errors.append("coverage exclusion path is unsafe") + elif not _text(x.get("id")): + errors.append("coverage exclusion requires path or id and reason") + recs = doc.get("records") + if not isinstance(recs, list): + return errors + ["records must be a list"] + ids = set() + needed = ( + "id", + "parent_run_id", + "stage", + "target", + "dataset", + "model", + "pipeline", + "sampler", + "configuration", + "backend", + "hardware", + "precision", + "seed", + "dependency_revisions", + "execution", + "scientific", + "timings", + "diagnostics", + "comparison", + "samples", + "evidence_paths", + "measured_at", + "archived", + ) + for i, r in enumerate(recs): + if not isinstance(r, dict): + errors.append(f"record {i} is not an object") + continue + rid = r.get("id") + if not _text(rid): + errors.append(f"record {i} needs id") + elif rid in ids: + errors.append(f"duplicate record id {rid}") + else: + ids.add(rid) + errors += [f"{rid}: missing {k}" for k in needed if k not in r] + for k in ( + "dataset", + "configuration", + "hardware", + "dependency_revisions", + "execution", + "scientific", + "timings", + "diagnostics", + "comparison", + "samples", + ): + if not isinstance(r.get(k), dict): + errors.append(f"{rid}: {k} must be object") + reasons = r.get("unknown_reasons", {}) + if not isinstance(reasons, dict) or not all(_text(v) for v in reasons.values()): + errors.append(f"{rid}: unknown_reasons must map fields to explanations") + reasons = {} + for key in ("target", "model", "pipeline", "stage", "sampler", "backend", "precision"): + if r.get(key) is not None and not _text(r[key]): + errors.append(f"{rid}: {key} must be nonempty text or null") + if ( + r.get(key) is None + and key not in ("pipeline", "stage") + and not _text(reasons.get(key)) + ): + errors.append(f"{rid}: unknown {key} needs reason") + for key in ("seed", "measured_at"): + if r.get(key) is None and not _text(reasons.get(key)): + errors.append(f"{rid}: unknown {key} needs reason") + dataset = r.get("dataset") + if isinstance(dataset, dict): + for key in ("class", "instrument", "id"): + if dataset.get(key) is not None and not _text(dataset[key]): + errors.append(f"{rid}: dataset.{key} must be text or null") + if dataset.get("id") is None and not _text(reasons.get("dataset.id")): + errors.append(f"{rid}: unknown dataset.id needs reason") + revisions = r.get("dependency_revisions") + if isinstance(revisions, dict): + if any(not _text(v) for v in revisions.values()): + errors.append(f"{rid}: dependency revisions must be text") + if not revisions and not _text(reasons.get("dependency_revisions")): + errors.append(f"{rid}: unknown dependency_revisions needs reason") + paths = r.get("evidence_paths") + if ( + not isinstance(paths, list) + or not paths + or not all(safe_relative_path(p) for p in paths) + ): + errors.append(f"{rid}: unsafe or missing evidence paths") + measured = r.get("measured_at") + if measured is not None and parse_utc(measured) is None: + errors.append(f"{rid}: measured_at must be UTC or null") + if type(r.get("archived")) is not bool: + errors.append(f"{rid}: archived must be boolean") + if r.get("seed") is not None and type(r["seed"]) is not int: + errors.append(f"{rid}: seed must be integer or null") + execution = r.get("execution") or {} + if isinstance(execution, dict) and not _text(execution.get("status")): + errors.append(f"{rid}: execution status required") + if isinstance(execution, dict): + if execution.get("completed") is not None and type(execution["completed"]) is not bool: + errors.append(f"{rid}: execution.completed must be boolean or null") + if execution.get("completed") is None and not _text(reasons.get("execution.completed")): + errors.append(f"{rid}: unknown execution.completed needs reason") + sci = r.get("scientific") or {} + if isinstance(sci, dict): + states = { + "convergence": ("not_assessed", "unknown", "converged", "not_converged"), + "acceptance": ("not_assessed", "unknown", "accepted", "rejected"), + } + for k, allowed in states.items(): + if sci.get(k) not in allowed: + errors.append(f"{rid}: invalid scientific {k}") + timing = r.get("timings") or {} + if isinstance(timing, dict): + defs = timing.get("definitions") + for k in ("setup_s", "sampling_s", "total_s", "compile_s"): + v = timing.get(k) + if k not in timing or (v is not None and (type(v) not in (float, int) or v < 0)): + errors.append(f"{rid}: {k} must be nonnegative seconds or null") + if not isinstance(defs, dict) or not _text(defs.get(k)): + errors.append(f"{rid}: {k} needs timing definition") + samples = r.get("samples") or {} + if isinstance(samples, dict): + if samples.get("availability") not in ( + "unknown", + "available", + "missing", + "unavailable", + "restricted", + ): + errors.append(f"{rid}: invalid sample availability") + if samples.get("path") is not None and not safe_relative_path(samples["path"]): + errors.append(f"{rid}: unsafe sample path") + diagnostics = r.get("diagnostics") or {} + if isinstance(diagnostics, dict) and ( + diagnostics.get("status") not in ("available", "missing", "not_assessed") + or not isinstance(diagnostics.get("values"), dict) + ): + errors.append(f"{rid}: diagnostics require status and values") + by_id = {r["id"]: r for r in recs if isinstance(r, dict) and _text(r.get("id"))} + for r in by_id.values(): + parent = r.get("parent_run_id") + if parent is not None and ( + not isinstance(parent, str) or parent not in by_id or parent == r["id"] + ): + errors.append(f"{r['id']}: parent_run_id unresolved or self-referential") + comps = doc.get("comparisons") + if not isinstance(comps, list): + errors.append("comparisons must be a list") + else: + seen = set() + for c in comps: + if not isinstance(c, dict) or not _text(c.get("id")): + errors.append("comparison needs id") + continue + if c["id"] in seen: + errors.append("duplicate comparison id") + seen.add(c["id"]) + if ( + not isinstance(c.get("records"), list) + or len(c["records"]) < 2 + or any(not isinstance(x, str) or x not in by_id for x in c["records"]) + ): + errors.append("comparison must name at least two existing records") + if ( + isinstance(c.get("records"), list) + and all(isinstance(x, str) for x in c["records"]) + and len(c["records"]) != len(set(c["records"])) + ): + errors.append("comparison endpoints must be distinct") + if not _text(c.get("protocol")): + errors.append("comparison requires declared protocol") + return errors + + +def classify(doc, wanted=None): + why = supported(doc, wanted) + if why: + return "unsupported", [why] + errors = validate(doc) + return ("invalid", errors) if errors else ("ok", []) + + +def qualified_records(doc): + return sum( + r.get("scientific", {}).get("acceptance") == "accepted" for r in doc.get("records", []) + ) + + +def comparison_refusals(doc, c): + by_id = {r["id"]: r for r in doc.get("records", [])} + rows = [by_id[x] for x in c.get("records", []) if x in by_id] + if len(rows) < 2: + return ["missing comparison endpoints"] + reasons = [] + for k in ("target", "dataset", "model", "pipeline", "stage", "seed"): + values = [r.get(k) for r in rows] + if any(v is None for v in values) and k not in ("pipeline", "stage"): + reasons.append(f"{k} unknown") + if any(v != values[0] for v in values): + reasons.append(f"{k} changed") + if any( + not isinstance(r.get("dataset"), dict) or not _text(r["dataset"].get("id")) for r in rows + ): + reasons.append("dataset identity unknown") + if any(not r.get("dependency_revisions") for r in rows): + reasons.append("dependency revisions unknown") + if any( + r.get("timings", {}).get("definitions") != rows[0].get("timings", {}).get("definitions") + for r in rows + ): + reasons.append("timing definitions differ") + if not c.get("protocol"): + reasons.append("no declared scientific protocol") + return reasons diff --git a/insight/theme.py b/insight/theme.py new file mode 100644 index 0000000..c7dea63 --- /dev/null +++ b/insight/theme.py @@ -0,0 +1,4 @@ +"""Pulse control-room presentation, shared by copying the proven static design.""" + +CSS = '\n:root{--bg:#ffffff;--fg:#1f2328;--muted:#59636e;--card:#f6f8fa;--line:#d1d9e0;\n--accent:#0b7a4b;--hero1:#062b1c;--hero2:#000000;--glow:#2ee88f;--ok:#1a7f37;--warn:#9a6700;\n--bad:#cf222e}\n@media (prefers-color-scheme:dark){:root:not([data-theme="light"]){--bg:#0d1117;--fg:#e6edf3;\n--muted:#9198a1;--card:#151b23;--line:#3d444d;--accent:#34d399;--ok:#3fb950;--warn:#d29922;\n--bad:#f85149}}\n:root[data-theme="dark"]{--bg:#0d1117;--fg:#e6edf3;--muted:#9198a1;--card:#151b23;\n--line:#3d444d;--accent:#34d399;--ok:#3fb950;--warn:#d29922;--bad:#f85149}\n*{box-sizing:border-box}\nbody{margin:0;background:var(--bg);color:var(--fg);\nfont:15px/1.5 -apple-system,BlinkMacSystemFont,"Segoe UI",Helvetica,Arial,sans-serif}\nmain{max-width:1200px;margin:0 auto;padding:0 16px 48px}\n.hero{background:radial-gradient(circle at 30% 20%,var(--hero1),var(--hero2));color:#fff;\npadding:28px 16px 22px;text-align:center}\n.hero 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b{display:block;font-size:12px;color:var(--muted);text-transform:uppercase;letter-spacing:1px}\n.stats{display:flex;flex-wrap:wrap;gap:12px;margin:16px 0}\n.stats div{background:var(--card);border:1px solid var(--line);border-radius:8px;padding:8px 14px}\n.stats b{display:block;font-size:20px;color:var(--accent)}.stats span{font-size:12px;color:var(--muted)}\nbutton[data-copy]{font:12px ui-monospace,SFMono-Regular,Menlo,monospace;text-align:left;\nbackground:var(--bg);color:var(--fg);border:1px solid var(--line);border-radius:6px;\npadding:3px 6px;cursor:pointer;word-break:break-all}\nbutton.copied{border-color:var(--ok)}\n.checkin textarea{display:block;width:100%;font:inherit;font-size:14px;line-height:1.5;background:var(--card);color:var(--fg);border:1px solid var(--line);border-radius:8px;padding:12px;margin:8px 0;resize:vertical}\n#copy-checkin{background:var(--accent);color:var(--bg);border:0;border-radius:6px;padding:10px 16px;cursor:pointer;font-weight:600}\n#copy-status{margin-left:12px}\ncode{color:var(--accent);word-break:break-all}\n' +JS = "\nvar copyCheckin=document.getElementById('copy-checkin');\nif(copyCheckin){copyCheckin.addEventListener('click',async function(){\n var field=document.getElementById('checkin-prompt'), status=document.getElementById('copy-status');\n try {\n if(navigator.clipboard && window.isSecureContext){await navigator.clipboard.writeText(field.value)}\n else {field.focus();field.select();if(!document.execCommand('copy')){throw new Error('copy unavailable')}}\n status.textContent='Copied';\n } catch(error){field.focus();field.select();status.textContent='Select and copy the prompt above.'}\n});}\n\ndocument.querySelectorAll('button[data-copy]').forEach(function(b){\n b.addEventListener('click',function(){\n var t=b.getAttribute('data-copy');\n var done=function(){b.classList.add('copied');setTimeout(function(){b.classList.remove('copied')},1200)};\n 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Do not duplicate Cortex + production science programme. + - source: draft/feature/euclid/single_process_cpu_route_jax_vis_lp_numba_vis_pix.md + sha256: e27220146fcd1860685ea2b5999ede0b84600c1666baa5fa01aec25d8ac766f9 + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/feature/jax_substructure/5_prng_key_vmap_noise.md + sha256: fa2c9768e2d2a66e180b4f37f7c1b3d1e4e5892c587c5355ba53f27b19c21207 + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/feature/pyautoears/community_organ_birth.md + sha256: ed3fba8a98700491c856871bef87e0412ef9a46abfbcaaeba1ccd079722dcd46 + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/feature/pyautohands/smoke_profile_cannot_see_a_missing_sampler.md + sha256: 58f81a12cb5270c4d1e48cf04c1894a4d05d52efffe6e63a07b074b1db2b281b + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/feature/pyautoheart/howto_real_settings_nightly.md + sha256: ed31272cedce3e1f7a5a0f0bc843929893810c8df7c75b1a3b37a5b80cddd011 + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/feature/workspaces/cluster_pixelized_analysisfactor.md + sha256: 69e785e07e8177baf29e7e09a5c8609a13d3483a8c1c29a1049d19ad3d4f4344 + disposition: retained-mixed-implementation + reason: Library/workspace Source and Cluster implementation arc; Pulse owns PointSolver optimisation, Cortex + retains science. No independent inference campaign to move. + - source: draft/feature/workspaces/cluster_source_science.md + sha256: f4eb6ab2bafa0973bb21f89b43945ec26a55300bdca2a2d4c44b443d4b00cc1d + disposition: retained-mixed-implementation + reason: Library/workspace Source and Cluster implementation arc; Pulse owns PointSolver optimisation, Cortex + retains science. No independent inference campaign to move. + - source: draft/feature/workspaces/model_figures_6_rollout.md + sha256: 93ff766aa4c4c72a4f85c9b475770606e21d2b9d0edb790b51e843a6fa9cadd0 + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/maintenance/autofit_visualization/seed_samplers_for_byte_stable_rerenders.md + sha256: 767ae13be6aaf9bca1ce19256d4ac3d6fdd53acc11d4cbc064f82d8211389e6d + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/maintenance/ci/wiki_currency_check_version_gate.md + sha256: 8094c0c21ddb5676f1acfc95d88ea725b9df8ab047be5510a1e654a337bb1d78 + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/maintenance/libraries/dep_cap_refresh_2026_08.md + sha256: e7e97f850a621daaf3bddedb181b8ec07869aaeee7f082041c6a397ed1440b9e + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/maintenance/pyautomind/session_start_hook_copies_regen.md + sha256: eaae74075390cb9de2e68094e5e87c1febd0d39b427b362bc285dc1286ba9890 + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/maintenance/workspaces/hpc_cache_off_home_phase2_euclid.md + sha256: 5c4faac04e44f9e86fb3ee7e5e828ddc05dcb7d6bcfe20d80bf0b79056b3d6ea + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/maintenance/workspaces/read_through_issues.md + sha256: 47f48df9d337082f8f1449de487496a54186b619d36820a3717ed28178d3effa + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/maintenance/workspaces/sync_remaining_workspace_config_priors_copies.md + sha256: 3382810e12699be1f9e7e4b545bd7c16b5f12c7b615ee62fd5beacef3e035249 + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/refactor/autofit/ep_analysis_level_compile_cache.md + sha256: 6574c767342695780eae6efdc8e1b996d3c971c2694128c98ee27906b0149f98 + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/refactor/autofit/split_fitness_batch_size_lh_vs_latent.md + sha256: 2726b1c9a503576f35f13cf724c3b0c94231c3c95ed10a6f4bc973318fa0babc + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/research/autoarray/delaunay_interpolator_pure_callback_vmap_memory.md + sha256: f1620965526fe17afb7a8f00b286605ad8ff3f06f55fbaed93ed90f1d1d289cd + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/research/autoarray/delaunay_research.md + sha256: 2dd027550d7ed5a42d76f5df89d2d20714e1832fb32b84a61a09392b261f30f1 + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/research/autofit/hpc_campaign_epic.md + sha256: 118ea513700bd8e57ef3813bcdb19fc776638f1a3f1b971ea61efc0eb4193f61 + disposition: retained-mixed-implementation + reason: Cross-organ HPC platform/submit tooling, policies and research evidence; not a domain inference + campaign queue. Mind owns implementation phases. + - source: draft/research/autofit/hpc_campaign_epic_report.md + sha256: 17f3fde3362f09969551120ea0808b0d15048b01090ce2c17b5f5f4bdbc6d975 + disposition: retained-mixed-implementation + reason: Cross-organ HPC platform/submit tooling, policies and research evidence; not a domain inference + campaign queue. Mind owns implementation phases. + - source: draft/research/autofit/hpc_campaign_epic_surveys/codebase.md + sha256: 290feb92da486683a465ce7d28c85575de215835bbbd3a5ff20bef3d26b6cb2c + disposition: retained-mixed-implementation + reason: Cross-organ HPC platform/submit tooling, policies and research evidence; not a domain inference + campaign queue. Mind owns implementation phases. + - source: draft/research/autofit/hpc_campaign_epic_surveys/euclid_hpc.md + sha256: c140e1a9ea500365104a336b03812dbcbc7096bc69317a295dabd81d2a016494 + disposition: retained-mixed-implementation + reason: Cross-organ HPC platform/submit tooling, policies and research evidence; not a domain inference + campaign queue. Mind owns implementation phases. + - source: draft/research/autofit/hpc_campaign_epic_surveys/prior_art.md + sha256: 7053807a77bc6da0befe08bb0ac580533b0051035173ecbc9ff29310e73bc086 + disposition: retained-mixed-implementation + reason: Cross-organ HPC platform/submit tooling, policies and research evidence; not a domain inference + campaign queue. Mind owns implementation phases. + - source: draft/research/graphical_ep/ep_campaign.md + sha256: 5347c08c256bf0968b736d2b48871462c54f25f852d4f02b7a14c07a0851b723 + disposition: migrated (only identified fragment for ideas.md) + reason: See exact mapping and original bytes. + - source: draft/research/graphical_ep/ep_scoping.md + sha256: cccb1905347882af600f05d260f1d9861c9a8abae1f98a6cfd9df12667037a3b + disposition: migrated (only identified fragment for ideas.md) + reason: See exact mapping and original bytes. + - source: draft/research/graphical_ep/graphical_scoping.md + sha256: f19c26607778e42d9ef7bf1a54d1ee1eb0a2ab43fa238be307584b3da0d5da8d + disposition: migrated (only identified fragment for ideas.md) + reason: See exact mapping and original bytes. + - source: draft/research/graphical_ep/transformed_message_declares_support.md + sha256: 92899093528898dcbd51037aef259f0d2b51ffac99207624ee6180e9f6ef4c6f + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: draft/research/pyautomemory/checkerboard_psf_mismatch_residual_diagnostic.md + sha256: dfc740d15ff3078627366ef871c8434aa48c353a8814a2d4b6de3ad1f6a3bded + disposition: retained-literature + reason: Literature ingestion/assistant diagnostic guidance; Memory ownership. + - source: draft/research/pyautoreduce/acceptance_noise_rebaseline.md + sha256: 40e71cfbcd26228ea525a49810c435cdb65f07314c8a9090e9fe7f83915106f9 + disposition: retained-reduction + reason: Reduction calibration/parity scope owned by Reduce, not inference sampler campaign. + - source: draft/research/pyautoreduce/legacy_flux_scale_parity.md + sha256: 2e96ea6032f9a09b33548989b4594358f9d5ad0b1b662dc6ef889a55063e9eae + disposition: retained-reduction + reason: Reduction calibration/parity scope owned by Reduce, not inference sampler campaign. + - source: draft/test/workspaces/smoke_workspace_fixes.md + sha256: 852e8f05c931d2d5576797c0f42bfa98b670efa1ea7d1877433f7529fdd7c45d + disposition: retained-implementation + reason: Bounded library/workspace/platform code, docs, tests or profiling task; may be an inference dependency + but is not campaign scheduling intent. + - source: epics.md + sha256: 6bee9b4bfd9e68f9f009454c991cce7b5aca30d44653a1d8b7bf02b43fdd82ca + disposition: retained-registry + reason: Preserve historical epic/claim/lifecycle records; rewrite migrated task links after destination + lands; remove only migrated idea line. + - source: ideas.md + sha256: 53f98ede715716d6b345aca23c76f80dea4fc3580a27ebb5278a238f7b4aff03 + disposition: migrated (only identified fragment for ideas.md) + reason: See exact mapping and original bytes. + - source: parked.md + sha256: 34f8479af4fa80c1e359461a2c018cc9572dd102bd3ec26d20b01c26651f6fe5 + disposition: retained-registry + reason: Preserve historical epic/claim/lifecycle records; rewrite migrated task links after destination + lands; remove only migrated idea line. + - source: planned.md + sha256: 833a69053b6602a40f816ea008319277e61186067a0358812bd2d6e114dcd2b7 + disposition: retained-registry + reason: Preserve historical epic/claim/lifecycle records; rewrite migrated task links after destination + lands; remove only migrated idea line. + ownership_notes: + - Cortex Now/Runs/Log is scientific/run authority. Archived tasks/rulings are historical references, never + a resurrected live workflow. + - Insight coordinates linked EP science campaigns but does not create new producers for them; autolens_inference + is the only registered real producer. + - Mind source epics retain lifecycle and history, with canonical Insight campaign/task links; no independent + domain schedule survives cleanup. + - Pulse owns profiling campaign tasks already migrated there; EP phase 6 and optimisation references resolve + to Pulse, not a second Insight profiling queue. + - Retired inference_programme stays retired; none of its archived baselines, tasks or results were migrated. +reviewed_sources: + PyAutoCortex: d45f198804615c090e6b6694cf725ddb0be2354e + autolens_inference: 420572fc7d055a3c1fe2c68ec1aafdbf1ed28361 diff --git a/receipts/lens.json b/receipts/lens.json new file mode 100644 index 0000000..d4fd12c --- /dev/null +++ b/receipts/lens.json @@ -0,0 +1,16 @@ +{ + "instance": "lens", + "repo": "autolens_inference", + "github": "PyAutoLabs/autolens_inference", + "branch": "feature/insight-producer", + "resolved_commit": "9c362a7a298238d766f61e812c694813e94830d1", + "summary_path": "dashboard/summary.json", + "source": "remote", + "fetched_at": "2026-10-04T07:45:10Z", + "outcome": "ok", + "schema": "inference-summary", + "version": 1, + "record_count": 56, + "errors": [], + "cached_from": null +} diff --git a/registry.yaml b/registry.yaml new file mode 100644 index 0000000..38002b0 --- /dev/null +++ b/registry.yaml @@ -0,0 +1,9 @@ +schema: 1 +instances: + - instance: lens + repo: autolens_inference + summary_path: dashboard/summary.json + supported_schema: inference-summary@1 + dashboard_url: https://github.com/PyAutoLabs/autolens_inference#readme + library_refs: [PyAutoLens, PyAutoGalaxy, PyAutoArray, PyAutoFit] + cortex_project: autolens_inference diff --git a/ruff.toml b/ruff.toml new file mode 100644 index 0000000..0a55b00 --- /dev/null +++ b/ruff.toml @@ -0,0 +1,46 @@ +# ruff configuration for PyAutoInsight. +# +# PyAutoInsight is an organ, not an installable package: there is no pyproject.toml, +# and `bin/pyauto-insight` puts the repo root on sys.path so `insight/` imports by its +# top-level name. A standalone ruff.toml at the repository root is the supported +# configuration location for that case (see https://docs.astral.sh/ruff/configuration/). +# The rule set was inherited from PyAutoEyes (via autolens_visualization, copied from +# autolens_inference) and is kept deliberately conservative. + +target-version = "py311" +line-length = 100 + +# Exclude generated / external folders. +extend-exclude = [ + ".venv", + "venv", +] + +[lint] +# Conservative selection — catch real bugs without imposing a strict style. +# Profiling scripts use scientific patterns that can clash with stricter rule +# sets (long lines for table headers, `if __name__ == "__main__":` is rare +# because scripts run top-level, etc.). +select = [ + "E", # pycodestyle errors + "F", # pyflakes (undefined names, unused imports) + "W", # pycodestyle warnings + "I", # isort (import order) + "UP", # pyupgrade (Python version idioms) + "B", # flake8-bugbear (likely bugs) +] +ignore = [ + "E501", # long lines — common in scientific code; ruff format handles wrapping + "E402", # module-level imports not at top of file — inference scripts often + # set env vars (NUMBA_CACHE_DIR etc.) before importing autolens + "F401", # unused imports — some scripts deliberately import-for-side-effect + # (e.g. `from autoconf import jax_wrapper # noqa: F401`) + "B008", # function call in default argument — common autofit pattern + "B905", # zip() without strict= — scripts zip equal-length lists they built + # themselves; threading strict= through adds noise, not safety + "B007", # unused loop-control variable — warm-up/repeat loops (`for _i in + # range(n)`) are the dominant pattern in timing code +] + +[format] +# Defaults: black-compatible. Nothing to override. diff --git a/snapshots/lens.json b/snapshots/lens.json new file mode 100644 index 0000000..99c9744 --- /dev/null +++ b/snapshots/lens.json @@ -0,0 +1,9114 @@ +{ + "instance": "lens", + "repo": "autolens_inference", + "github": "PyAutoLabs/autolens_inference", + "summary_path": "dashboard/summary.json", + "commit": "9c362a7a298238d766f61e812c694813e94830d1", + "fetched_at": "2026-10-04T07:45:10Z", + "summary": { + "comparison_policy": { + "id": "declared-target-seed-stage-v1", + "note": "Targets/seeds/stages retained; no automatic parity certification or best-seed selection", + "ranking": "none" + }, + "comparisons": [], + "coverage": { + "excluded": [], + "expected": { + "reason": "No complete expected-run manifest; unrecorded jobs cannot be enumerated", + "runs": null + }, + "observed": { + "archived_records": 50, + "execution_complete": 55, + "execution_stopped_early": 1, + "records": 56, + "runs": 15 + } + }, + "evidence_updated_at": null, + "evidence_updated_at_reason": "Legacy result rows do not record measurement timestamps", + "generated_at": "2026-10-04T07:41:04Z", + "generation_basis": "Source commit timestamp; not a measurement or check-in time", + "limitations": [ + "No scientific acceptance or convergence inferred from process completion", + "Sample archives remain in project storage; current availability unknown", + "No evidence from the retired inference programme is imported", + "Archived rows belong to this project's September 2026 runs and are historical, not current baselines" + ], + "producer_revision": "4ead1799b05b2218a59fca062e3419891c71c82c", + "project": "autolens_inference", + "records": [ + { + "archived": true, + "backend": "jax_gpu", + "comparison": { + "declared_target": "hst/slam5_delaunay_1250/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_gpu_dense_fp64", + "cores": 8, + "image_mesh": "hilbert", + "image_mesh_edge_points": 30, + "image_mesh_weight_floor": 0.01, + "image_mesh_weight_power": 3.5, + "inversion": "dense", + "memo": "off", + "mesh": "delaunay", + "mesh_areas_factor": 0.5, + "mesh_pixels": 1250, + "mesh_shape": null, + "mesh_zeroed_pixels": 30, + "rect_mesh": "bilinear", + "regularization": "adapt_split", + "stages_requested": null, + "test_mode": false, + "use_jax": true, + "variant": "delaunay_1250" + }, + "dataset": { + "class": "imaging", + "id": null, + "instrument": "hst" + }, + "dependency_revisions": {}, + "diagnostics": { + "status": "missing", + "values": {} + }, + "evidence_paths": [ + "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json" + ], + "execution": { + "completed": null, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "gpu", + "cpu_count": 124, + "device": "cuda:0", + "nvidia_smi": "NVIDIA A100 80GB PCIe, 37549 MiB, 81920 MiB", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + } + }, + "id": "results/archive/2026-09-24_pre_likelihood_speedup/slam/imaging/hst/delaunay_1250/hpc_a100_jax_gpu_dense_fp64/stages_seed0.json", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": null, + "parent_run_id": null, + "pipeline": "slam", + "precision": "fp64", + "sampler": null, + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": null + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": null, + "target": "hst/slam5_delaunay_1250/seed0", + "timings": { + "compile_s": null, + "definitions": { + 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recorded in source result" + } + } + ], + "schema": "inference-summary", + "scope": "inference", + "source_commit": "4ead1799b05b2218a59fca062e3419891c71c82c", + "valid_until": null, + "version": 1 + } +} diff --git a/state.json b/state.json new file mode 100644 index 0000000..2ea8146 --- /dev/null +++ b/state.json @@ -0,0 +1,18 @@ +{ + "schema_version": 1, + "organ": "insight", + "repo": "PyAutoInsight", + "status": "yellow", + "headline": "1 projects \u00b7 56 records \u00b7 0 cached", + "updated": "2026-10-04T07:45:23Z", + "pages_url": "https://pyautolabs.github.io/PyAutoInsight/", + "items": [ + { + "id": "insight:lens", + "severity": "yellow", + "text": "lens: ok \u00b7 56 records \u00b7 0/56 accepted by producer; remaining assessments shown below \u00b7 freshness policy unspecified \u00b7 evidence time unknown", + "url": "https://pyautolabs.github.io/PyAutoInsight/#lens", + "prompt": null + } + ] +} diff --git a/tasks/drop_1e3_prior_centring_after_631.md b/tasks/drop_1e3_prior_centring_after_631.md new file mode 100644 index 0000000..d812a10 --- /dev/null +++ b/tasks/drop_1e3_prior_centring_after_631.md @@ -0,0 +1,10 @@ +# Drop the 1e-3 prior centring for shear / multipole / ell_comps in the benchmark + +Type: feature +Target: @autolens_inference +Filed: 2026-09-27 + +The inference benchmark centres ExternalShear, multipole_comps and ell_comps priors on 1e-3 to avoid NaN +jax.grad at exactly 0. PyAutoGalaxy#631 (PR #634, companion PyAutoLens#754) makes those gradients finite and +correct at 0 (Isothermal included). Once #634 is released, restore the natural 0 prior medians and +confirm gradient searches start cleanly (no NaN on the first step). Blocked on the PyAutoGalaxy release. diff --git a/tasks/ep_campaign.md b/tasks/ep_campaign.md new file mode 100644 index 0000000..31c9b36 --- /dev/null +++ b/tasks/ep_campaign.md @@ -0,0 +1,152 @@ +# EP campaign — phase map for the 2026 Q3 graphical/EP push + +Type: research +Target: graphical_ep +Themes: +- graphical-ep +Difficulty: too-large +Autonomy: human-required +Priority: high +Status: campaign map — phases route through /start_dev one at a time; this file is never issued itself and nothing here is bulk-issued +Consequence: judge +Review-minutes: 25 +Unattended: needs-slicing +Epic: graphical-ep +Filed: 2026-08-19 (backfilled from git) + +Filed 2026-08-19 from James's multi-phase EP brief (raw text preserved in the +intake session). This is the umbrella view; each phase's real content lives in +its own prompt file. Update the table as phases ship. + +## End goals (acceptance for the campaign as a whole) + +1. An analytic Gaussian model that statistically demonstrates the graphical + and EP source code is correct (means *and* errors against closed form). + The model shipped in phase 1 as a single-seed CI gate; the *statistical* + half moved to the Cortex on 2026-09-09 as science project + `analytic_gaussian` (ledger `projects/analytic_gaussian.md`), + which runs it as a seed ensemble on RAL. +2. slope_hierarchy cosmology: accurate graphical + EP results, EP scaling to + 100+ datasets, fast, interpretable/inspectable output for a scientist, + running on RAL A100s. +3. IC50: graphical + EP scaling to 10 000+ datasets, with EP-vs-graphical + parity demonstrated at small N. + +## Phases + +| # | Phase | Prompt | State (updated 2026-09-10) | +|---|-------|--------|--------------------| +| 1 | Analytic Gaussian benchmark (the keystone — start here) | `complete/2026/09/analytic-gaussian-benchmark.md` | **SHIPPED 2026-09-02** — autofit_workspace_test#91, PR #92 merged `54af208398ae7fd336d3d9bca363776ad50da037` (`scripts/graphical/analytic_*.py`); record `complete/2026/09/analytic-gaussian-benchmark.md`. Verdict: closed form, minimal EP and the graphical joint fit agree everywhere; **every failing cell is autofit's EP column, including the exactly Gaussian leg A**. Six PyAutoFit mechanisms root-caused and filed (see Findings below); the autofit-parity scripts are parked NEEDS_FIX until they land, the closed-form reference + minimal EP are curated into the smoke gate. **Review follow-ups shipped 2026-09-07**: Codex review of PR #92 (autofit_workspace_test#96 / PR #97, record `complete/2026/09/ep-review-92-followups.md`) — the θ = σ Laplace leg is the STALE state, not a collapse; `log_sigma` moments pinned against quadrature. D6 shipped 2026-09-07 — findings table complete. **Ensemble scale-up 2026-09-09**: the keystone became Cortex science project `analytic_gaussian` (PyAutoCortex PR #27, human merge pending), the third alongside `slope_hierarchy_scale` and `ic50_workspace`. Wave 1 = job `342413_[0-199]`, 200 independent draws at N=5 on the `ral` partition, submitted against RAL PyAutoFit mirror `66f9f8d5d` (verified to contain #1580 first). Its witness was pre-registered before submission. **First post-D1–D6 measurement: leg A autofit EP is now EXACT** (mu 50.8595 ± 4.1104 against the same reference, a = b = 0.000) — the leg that parked `analytic_gaussian.py` NEEDS_FIX; leg B returns σ 9.37 ± 3.57 against 6.57 ± 2.88, outside the cell tolerance, inside the closed-form [q05, q95], not collapsed. The ensemble turns that caveat into a rate and gives the moment-matching cure a number to beat | +| 1b | Confidence test on the #1405 collapse toy at N=50 (NUTS vs EP x3) | → PyAutoCortex `projects/ep_toy_gaussian.md` | **born 2026-09-10** as Cortex science project `ep_toy_gaussian` (Cortex PR #29, human merge pending — `projects.yaml` is code). The campaign's last confidence test before scaling up: the *original* collapse toy from `complete/2026/07/ep_scale_collapse_assets/ep_toy_diagnostic.py`, model and EP settings verbatim (`DynestyStatic(nlive=100, rwalk)` per factor, `LaplaceOptimiser`, `EPHistory(kl_tol=0.05)`, `max_steps=20`), but at **N=50**, with a joint **BlackJAX NUTS** reference and **three EP repeats on identical data** — none of which this toy has ever had. A pass on the no-collapse / parity / self-agreement / honest-width criteria is the evidence that the D1–D6 + review-wave fixes cured PyAutoFit#1405 on its own toy; a failure isolates it to the Dynesty-per-factor EP path, since the analytic ensemble used Laplace factors. Task `State: ready`, witness pre-registered (7 criteria), **awaiting submission** | +| 2 | Scatter-collapse cure or caveat | `complete/2026/09/ep-scale-collapse-basin-cure-or-caveat.md` | **SHIPPED 2026-09-02** — cure of the mechanism (#1558/#1560/#1562) + caveat (README §3.5); record `complete/2026/09/ep-scale-collapse-basin-cure-or-caveat.md`; cure follow-on filed `draft/feature/autofit/ep_hierarchical_scatter_moment_matching.md` (human decision). Close-out 2026-09-02: `complete/2026/09/ep-collapse-unpark.md` (autofit_workspace_test#94 / PR #95) curated `analytic_gaussian_collapse.py` into the smoke gate (RECOVER 5/5). **Codex review of the fixes shipped 2026-09-07** as bundle `ep-phase2-review`: PyAutoFit#1572 coupled-transform covariance in `from_mode` (`transformed-from-mode-coupled-covariance.md`), #1573 P1 deterministic variables kept cavity covariance on the fd-Hessian path (`ep-laplace-deterministic-hessian.md`), #1574 fully reverted projection counted as updated (`ep-full-revert-not-updated.md`), #1576 + autofit_workspace_test#98 per-(factor, variable) stale tracking with `reverted_variables` in `ep_history.csv` (`ep-stale-tracking-per-variable.md`) | +| 3 | Graphical (non-EP) JAX scaling | → PyAutoCortex `projects/slope_hierarchy_scale.md` (the ledger of project `slope_hierarchy_scale`; the measurement addendum is in `archive/tasks/slope_hierarchy_scale/n25_scale_up.md`) and `graphical_scoping.md` sub-tasks | **gated 2026-09-08** — a fresh Cortex science project `slope_hierarchy_scale` was born for it (Cortex PR #25, issue Cortex#24): the wrapped-up N=5 tree was vaulted to `Science/z_projects_complete/slope_hierarchy` and its row retired, the code borrowed into `Science/slope_hierarchy_scale`, and the task moved `planned → gated` on `Gates: PyAutoFit#1405, #1558, #1560, #1562`. PR #25 **merged 2026-09-08** (merge `866d8f0`; issue Cortex#24 closed, record `complete/2026/09/slope-hierarchy-scale-birth.md`). Next: `/cortex` → `move tasks/slope_hierarchy_scale/n25_scale_up.md ready` → submits the N=25 runs | +| 1b/3 | Check-in (EP arms of 1b and 3) | → PyAutoCortex `projects/ep_toy_gaussian.md`, `projects/slope_hierarchy_scale.md` | 2026-09-24: 343299 shows the Laplace hierarchical projection fails on every update at N=25 lensing; the moment-matching cure is now the blocking decision for phase 3's EP arm; ep_toy_gaussian wave 1 failed on infra (EMFILE, fixed by #1632/#1634), wave 2 awaits a human go. 2026-09-24: the EP per-factor-search wrapper overhead shipped as PyAutoFit#1643/#1644/#1645 (toy factor search 2.45 s → ~0.9–1.0 s; record `complete/2026/09/ep-factor-search-overhead.md`), pending release — the RAL mirror needs a sync before wave 2 benefits. | +| 4 | IC50 EP end-to-end + scale ladder | → PyAutoCortex `projects/ic50_workspace.md` (the ledger of project `ic50_workspace`; dev companion stays in the Mind as `feature/autofit/ep_lbfgs_jax.md`) | **RUNNING ON RAL 2026-09-09 — parity at N=5 achieved.** The 2026-09-08 blockers (dirty checkout, no `results/` witness path, row `dormant`) are all cleared; Cortex row `active` and task `submitted` via PyAutoCortex PR #26 (human merge pending). Runs `342408` (EP, sim, nlive 150, max_steps 12, 56.7 s) and `342409` (graphical joint Dynesty, sim, nlive 150, 21.4 s), partition `ral`, against a mirror **verified to contain #1580** — so unlike phase 3's EP arm these carry the full D1–D6 wave. **Result: 33/33 within 3σ for both methods, 0/33 cross-method disagreements** (`results/graphical_ep_comparison_sim.txt`). Caveats: N=5 only, the scale ladder is not started; EP's median σ is still 2.3× the graphical fit's on `hill_coef` (0.980 vs 0.423); and the gain over the archived `_v1` run confounds the D1–D6 fixes, nlive 50→150 and laptop→RAL. Setup shipped as 5 commits on `feature/ep-phase4-ral-setup` (unpushed, public repo — see Phase 4 notes below). The companion dev prompt `draft/feature/autofit/ep_lbfgs_jax.md` is still open and is now the scale lever, not a blocker | +| 5 | Diagnostics sufficiency checkpoint | no prompt yet — deliberate | judge after phases 2 & 4 produce real runs; the 2026-07 diagnostics wave (#1330/#1335) shipped and caught #1383; graphical_scoping sub-tasks 5–6 (summary JSON, dashboard) are the likely follow-ups; concrete item from phase 1–2: STALE = zero SUCCESS updates is now warned by the library (#1562); 2026-09-07: #1574/#1576 extended STALE to full reverts and to per-(factor, variable) tracking, and `ep_history.csv` gained `reverted_variables`; the BIASED-TIGHT guard calibration stays a human call | +| 6 | Profiling: autofit_profiling repo + two epics | `research/autofit/autofit_profiling_bootstrap.md` | filed; repo creation human-gated | + +Adjacent live work this campaign leans on but does not own: priors/messages +phase 4 (`bug/priors/` tracker — design #1500, findings #1498/#1501) and the +slope_hierarchy methods write-up (moved to the Cortex 2026-09-03 as +PyAutoCortex `archive/tasks/slope_hierarchy/methods_writeup.md`, never run — it stayed +under the retired `slope_hierarchy` row on 2026-09-08 and is the human's to move or drop). + +## Findings (phase 1, 2026-09-02) + +The benchmark's referee verdict, from autofit_workspace_test#91 (evidence in +its two phase comments) and the read-only root-cause pass, recorded on +PyAutoFit#1405. Each mechanism is a Mind prompt under `draft/bug/autofit/`: + +| # | Mechanism | Prompt | Size | +|---|-----------|--------|------| +| D1 | The first prior of a process (id 0) compares equal to the `FactorValue` sentinel, so every multi-variable factor gradient is corrupted; leg A `mu` stays at its start (50.00 ± 20.6 vs 50.86 ± 4.11) | `complete/2026/09/ep-prior-id-zero.md` | **SHIPPED 2026-09-02** — PyAutoFit#1558 merge `809b4fd85ce308cb7bcd18b58e1202ad73d82fba` (PyAutoFit#1557). Leg A `mu` mean now exact (a = 0.000), SUCCESS = 20; remaining std bias (3.76 vs 4.11) is D2 | +| D2+D3 | Laplace "covariance" = mean-field precision + one non-accumulating random diagonal secant (no factor curvature; result depends on prior ids through sampling order); a failed line search still projects the start point and overwrites the message | `complete/2026/09/ep-laplace-hessian.md` | **SHIPPED 2026-09-02** — PyAutoFit#1562 merge `5375f4d631547bc946687cf3645d3689654e510d` (PyAutoFit#1561). FD Hessian at the mode (deterministic, K-invariant), skip-not-write on failed/non-concave updates, per-variable backstop naming variables, STALE covers skips. Leg A 18/18 byte-identical across prior ids; collapse config RECOVER 5/5; leg B `sigma` 9.37 ± 3.57 inside the exact interval | +| D4+D5 | Truncation limits dropped by `from_natural_parameters`/`__pow__` (leg B σ message returns limits (−inf, inf)); `TransformedMessage.from_mode` skips the Jacobian for scalar variables (log-σ leg never moves) | `complete/2026/09/ep-message-support.md` | **SHIPPED 2026-09-02** — PyAutoFit#1560 merge `9eb80852233590205bc53366cb4ddf26b0be1ba5` (PyAutoFit#1559) + workspace autofit_workspace_test#93 merge `7d175ddbf3d2774e09fde4131cf35d2d95146351`. Leg B scatter message keeps limits `(0, 100)`; loggaussian `log_sigma` moves 2.30 → 1.55; truncated `mu` row still collapses ~2e-4 = D2/D3 | +| D6 | `errors_at_sigma(as_instance=True)` crashes on a prior-valued global model | `samples_errors_at_sigma_instance_prior_valued_model.md` | **SHIPPED 2026-09-07** — PyAutoFit#1578 merge `6331b800` (PyAutoFit#1577) + autofit_workspace_test#99 merge `93219107`; record `complete/2026/09/samples-errors-at-sigma-instance.md`. Fix keeps the tuple-attribute instance contract (`Model.instance_for_arguments` returns a `ModelInstance` of bounds pairs for Prior-class components). Phase-1 findings D1–D6 all shipped; side finding: `analytic_gaussian.py`'s graphical column is unseeded (`draft/bug/graphical_ep/analytic_gaussian_unseeded_graphical_column.md` → seeded by autofit_workspace_test#105 2026-09-30, record `complete/2026/09/analytic-gaussian-unseeded-graphical-column.md`) | + +Cure follow-on: `draft/feature/autofit/ep_hierarchical_scatter_moment_matching.md` — un-parks `analytic_gaussian.py` / `analytic_gaussian_priors.py` when it lands. +2026-09-30: the cure landed — `LaplaceOptimiser(projection="moments")` as PyAutoFit#1656 (b13169e2c, unreleased) + autofit_workspace_test#105 (8f6e19c); record `complete/2026/09/ep-moment-projection.md`. `analytic_gaussian.py` un-parked (PARITY 41/41; leg B autofit EP σ 6.3401 ± 2.4609, a 0.079 / b 0.146; SUCCESS=25) and in the smoke gate; `analytic_gaussian_priors.py` stays parked on the loggaussian Jacobian defect → `draft/bug/autofit/ep_moments_loggaussian_transformed_scatter.md`. + +Calibration facts to reuse: EP with a Gaussian site on σ is biased on this +model by construction (seeds 0–4 scatter row a ≤ 0.077, b ≤ 0.145 for the +minimal EP) — that is the analytic ceiling any autofit fix should be judged +against, not zero. Phase 5's diagnostics checkpoint gains a concrete item: +zero SUCCESS updates on a factor is a STALE result that no library warning +currently reports. + +## Phase 4 notes (2026-09-09) — IC50 setup + +The setup was almost entirely defect-clearing, not new science: both fit arms, +all 8 SLURM submit scripts and a 560-line `hpc/sync` already existed and had +never once been run end to end. Five defects would each have blocked or +corrupted a RAL run, all now fixed in `ic50_workspace`: + +1. `--partition=cpu` in all four CPU submit scripts — **RAL has no `cpu` + partition** (`ral`, `gpu`, `cam`, `imp`). Every CPU submission would have + been rejected. `slope_hierarchy_scale`'s generic template carries the same + bug; its working script uses `ral`. +2. `PROJECT_PATH` was never exported to `sbatch`, though every submit script + begins `source $PROJECT_PATH/activate.sh`. +3. `activate.sh` put a **two-month-stale `PyAutoConf` checkout on PYTHONPATH**, + shadowing the pip-installed package, and omitted `PyAutoNerves`. `autoconf` + is not even an importable module on RAL any more — the line was pure legacy. +4. A plain `push` shipped **5.6 GB** (the rnaseq CSV plus a symlink into a + sibling `concr` checkout that does not exist on RAL). Excluding them takes + it to 1.7 MB. +5. `PULL_DIRS=(output)` would never have retrieved `results/` — the very path + the Cortex witness glob watches. + +Two findings worth carrying forward: + +- **The existing witness under-reports.** `ep_sim.py`/`graphical_sim.py` assert + only on `coef_mean`; `coef_matrix` and `hill_coef` are printed but never gate. + Run against the archived `_v1` sidecars, the new comparator found the + graphical arm had 3/33 parameters beyond 3σ that no run ever reported as a + failure. Widening the assertion is an open follow-up. +- **IC50's EP cannot hit the dead-pool-worker hang.** It passes + `force_x1_cpu=True`, so Dynesty runs with no process pool — the failure mode + that has had phase 3's `342351` stuck for 27+ hours is structurally absent + here. + +`concr` consolidation (assessed 2026-09-09): `ic50_workspace` is canonical; +concr's cancer code has been frozen since 2026-05-07. Ported +`compare_graphical_ep.py` (the parity table). Still open: `one_by_one.py` (no +per-dataset baseline exists here), `graphical_nuts.py` (a second sampler, only +ever tested at n=3). **Do not copy concr's ~3,500 preprocessed drug-1073 +datasets** — their `x` is `ln(dose index)`, not `ln(µM)`, and their latent is +20-dim against this workspace's 5; the corpus predates concr's own +`n_latent 20→5` reduction. Scaling real data means re-running +`preprocess_real.py` with `N_DATASETS` raised. **Sign convention:** the +inverted form `n*(log_ic50 - x)` is in `concr/model_api.py`, all of +`scripts/cancer`, all of `scripts/cancer_sim` **and `simulators/cancer_sim.py`** +— concr's own simulator. Only `cancer_legacy/real/{ep,viz_hill}.py` match the +canonical decreasing form. `concr/model_api.py`'s log-likelihood is separately +broken (`+0.5·z·z`, no `/noise_sigma`) and must never be copied. + +Scale-ladder blocker now removed: `--n_datasets` / `--nlive` / `--max_steps` +are CLI flags on the sim scripts (they were module constants). `simulator.py`, +`preprocess_real.py`, `ep_real.py` and `graphical_real.py` still hard-code +their counts. + +## Sequencing + +Phase 1 first (cheap, unblocks 2, referees everything). Phase 2 shipped +2026-09-02 from its banked evidence once 1 gave the analytic upper limit +(`complete/2026/09/ep-scale-collapse-basin-cure-or-caveat.md`). Phase 3 runs in +parallel (different machine profile — RAL). Phase 4 after 1–2 establish +trust. Phase 6 epic 1 (general profiling) can start any time; epic 2 (EP +profiling) last, once end-to-end runs exist to profile. + +## Deferred — check-in gates, not scoped work + +- **JAX/gradient/Hessian EP internals** — against changing EP internals while + end-to-end models are the goal; adopt only if a genuinely simple change + dramatically simplifies/speeds things. (`feature/autofit/ep_lbfgs_jax.md` + covers the *factor-fit sampler*, which is allowed; this gate is about the + EP update maths itself.) — 2026-09-02: the moment-matching cure prompt is the first candidate for this gate. 2026-09-07: still the human's call (`Autonomy: supervised`); the phase-2 review bundle did not touch the update maths. 2026-09-24: 343299 shows the Laplace hierarchical projection fails on every update at N=25 lensing; the moment-matching cure is now the blocking decision for phase 3's EP arm; ep_toy_gaussian wave 1 failed on infra (EMFILE, fixed by #1632/#1634), wave 2 awaits a human go. +- **Sampler-result reuse across EP passes** — complicated for efficiency-only + gains; revisit via profiling epic 2 evidence (ep_scoping sub-task 6). +- **Parallel factor updates on HPC** — revisit when scaled runs approach + multi-day wall times (ep_scoping sub-task 5 holds the sketch). +- **Multi-dataset factors (EP/graphical middle ground)** — revisit once + scaling has hit whichever wall (memory or wall-time) shows up first. diff --git a/tasks/ep_scoping.md b/tasks/ep_scoping.md new file mode 100644 index 0000000..cfd5cc9 --- /dev/null +++ b/tasks/ep_scoping.md @@ -0,0 +1,272 @@ +# Expectation Propagation Scale-Up — Scoping + +Type: research +Target: graphical_ep +Themes: +- graphical-ep +Difficulty: too-large +Autonomy: human-required +Priority: high +Status: formalised +Consequence: judge +Review-minutes: 25 +Unattended: needs-slicing +Epic: graphical-ep +Filed: 2026-05-20 (backfilled from git) + +Companion baseline package: `autofit_workspace_developer/ep/`. This +document is grounded in the profile data emitted by that package's +`profiles/baseline.json` and `profiles/N10_hotspots.txt`. The "IC50 tier +breakdown" referenced below was supplied by James from a separate IC50 +cancer use case; one goal of this scoping pass was to rerun the toy +example and confirm/refute that ranking. + +## Problem + +PyAutoFit's EP framework fits a graphical model factor-by-factor: each +EP iteration runs N+1 Dynesty searches (one per dataset factor plus one +global factor, in the general case) and updates the message field via a +LaplaceOptimiser-driven cavity step. For the shared-centre toy this is +N per-dataset Dynesty fits per iteration. Over the +`af.EPHistory(kl_tol=0.05), max_steps=5` budget the toy converges in +fewer than 5 iterations, so the typical workload at N datasets is +roughly `M·N` Dynesty fits where M ∈ {1, 2}. + +Two consequences: + +1. **EP's per-fit wrapper overhead is amplified by M·N.** A graphical + joint fit runs Dynesty once at high dimension; EP runs it many times + at low dimension. Every per-fit cost (visualisation, latent samples, + sampler initialisation, search-internal-folder cleanup) is paid + ≥M·N times. +2. **autofit-internal overhead has nowhere to hide.** Graphical fits + amortise prior-transform cost across the inner Dynesty sample loop; + EP runs many short Dynesty fits where the relative overhead of + non-sampling work dominates. + +The original IC50 profiling produced this tier breakdown +(reproduced verbatim from the task prompt): + +> Tier 1 — Dynesty wrapper overhead (~86% of optimise time) +> Tier 2 — Local Hill LL evaluation (~6%) +> Tier 3 — Global LL evaluation (~5%) +> Tier 4 — EP-loop orchestration (~4%) +> Tier 5 — set_model_approx (~0%) + +The goal of this scoping pass is to rerun on toy, confirm/refute Tier 1's +86% claim, and produce a ranked list of sub-task prompts. + +## Measured Baseline (toy 1D Gaussian, shared centre) + +| N | wall time | peak RSS | output disk | sanity | +|---|-----------|----------|-------------|--------| +| 3 | 251.9 s (4.2 min) | 599 MB | 8.1 MB | PASS | +| 10 | 566.9 s (9.4 min) | 672 MB | 27.0 MB | PASS | +| 30 | 1758.7 s (29.3 min) | 1.1 GB | 84.7 MB | **FAIL** | + +Reproduce from `~/Code/PyAutoLabs-wt//autofit_workspace_developer`: + +```bash +python3 ep/simulator.py --total_datasets=30 +for N in 3 10 30; do + python3 ep/fit.py --total_datasets=$N --name=N$N +done +``` + +**Observations:** +- **EP is ~11× slower than graphical at every N tested** (cf. + `graphical_scoping.md`). At N=3 it's 252 s vs 23 s; at N=10 it's + 567 s vs 51 s; at N=30 it's 1759 s vs 133 s. The 11× ratio is stable + across N — this is per-fit wrapper overhead × number of fits, not a + scaling issue. +- **EP wall time scales linearly in N** (slope ~60 s per added dataset + after fixed overhead). This validates the IC50 projection that the + wrapper bucket is N-linear. +- **Peak RSS is ~constant in N** (~700 MB ± 200) because EP fits one + factor at a time. This is EP's structural advantage over graphical's + joint fit (which hit 2.9 GB at N=30) — at N=300+ EP is the only + approach that fits in memory. +- **Output disk grows linearly** at ~3 MB per dataset (8 MB → 85 MB + from N=3 to N=30). At N=1000 that's ~3 GB per run. +- **Sanity at N=30 fails** — most per-dataset normalization/sigma + recoveries pass, but one or two factors drift outside 3σ. Likely + symptomatic of EP convergence with `max_steps=5`; needs more EP + iterations or KL-tolerance tightening to investigate (out of scope + for this scoping pass). + +## Bottleneck Inventory — Toy vs IC50 + +cProfile attribution at N=10 (5 EP iterations × 10 factor fits = 50 total +Dynesty fits inside the 723 s profiled run). Source: +`ep/profiles/N10_hotspots.txt`. + +| Family | cumulative time | % of fit | Toy vs IC50 | +|--------|-----------------|----------|--------------| +| Per-factor matplotlib viz (`corner_anesthetic` + `plot_2d` + `savefig` + `backend_agg.draw`) | 346 s | **48%** | **Confirms IC50 Tier 1's wrapper bucket — and toy weights it even higher than IC50's 86% (since IC50 included other overheads in that bucket).** | +| Dynesty sampling (`run_nested`, `_fill_queue`) | 300 s | **41%** | Higher % than IC50's split because the toy LL is cheap (no JAX-jitted Hill curve to amortise against). | +| Prior transforms via `scipy.stats.truncnorm` | 185 s | 25% | Not separately broken out in IC50, but a known issue (same bottleneck as graphical). 116 s is `_distn_infrastructure.cdf` alone — 16% of total. | +| EP-loop orchestration (`factor_graph.optimise`, message updates) | small | <5% | Matches IC50's Tier 4 estimate of ~4%. | +| Latent-sample regeneration (`Creating latent samples by drawing 100 from the PDF`) | ~15 s | ~2% | Inside the wrapper bucket but worth calling out — it runs per fit even though EP factors don't need latent samples. | + +**Headline:** toy confirms IC50's Tier 1 (wrapper overhead dominates) but +weights matplotlib visualisation as the single largest sub-bucket — 48% +just for per-factor corner plots that nobody inspects. The +`corner_anesthetic` log line "posterior estimate not yet sufficient. +Should succeed in a later update" — which the user already flagged in +the prompt — fires on every fit and contributes no end-of-run artefact. +This is the biggest single bang-for-buck cut. + +What we **don't** see in toy that was prominent in IC50: +- A separate "global factor" cost. The toy doesn't have a hierarchical + global factor; the shared centre is just another Variable in the + factor graph. IC50's Tier 3 (Laplace replacement on a global Dynesty + fit) is real for IC50's 18-param hill-curve global factor but does + not apply here. +- "Force x1 cpu" / pool-related overhead. The toy runs single-core + by default; no pool to reuse. + +So the toy run **does not refute** any IC50 finding, and **strongly +confirms** that the wrapper bucket — specifically per-factor +visualisation — is where the first cuts should land. + +## Ranked Sub-Task Prompt Scopes + +Order is by payoff × ease, based on the toy + IC50 evidence. Each is +written as a future `/start_dev` candidate under `autofit/...` (most +are PyAutoFit library work). + +### 1. Suppress per-factor visualisation in EP iterations + +**~48% of EP runtime** on the toy and a similar share in IC50. EP +factors don't need per-iteration corner plots, latent-sample draws, or +PNG saves — only the final aggregated posterior matters. Add a +`paths.ep_mode = True` (or `paths.suppress_plots = True`) flag that the +EP loop sets on each factor's paths before each iteration's `search.fit` +call, and route `nest_plotters.corner_anesthetic`, `samples.plot_2d`, +and `output_figure` through this gate. Expected wall-time reduction: +40–50% on multi-factor EP runs; 0% on `searches_minimal/` single +searches. Lands cleanly with graphical_scoping.md's prompt (3). + +### 2. JAX-native fast path for `TruncatedGaussianPrior.value_for` + +Same prompt as graphical_scoping.md's (2). 25% of EP runtime is +prior-transform-bound; ~16% is the underlying `scipy.stats.truncnorm.cdf`. +A direct erf-based implementation or autonerves-shared JAX-aware helper +would knock off ~20% of EP wall time. **Single prompt that covers both +graphical and EP** — issue once and both scaling stories benefit. + +### 3. Skip search-internal-folder cleanup when output-to-disk is disabled + +When the user already opted into "Output to hard-disk disabled" by not +supplying a search `name`, the per-fit "Removing search internal folder" +step has nothing to do but still runs. Toy data doesn't isolate this as +a separately measurable line (it's buried inside `perform_update`), but +the IC50 prompt's Tier 1.3 calls it out, and the toy log shows the +INFO line firing on every fit. Cheap fix, modest payoff. Bundle with +(1) since both are paths flags. + +### 4. cProfile attribution inside `factor_graph.optimise` + +IC50's Tier 4 — the EP loop orchestration. Toy's <5% share suggests +this is not currently a bottleneck, but at N=10000 (per IC50's +projection) a 4% bucket grows to a 30-hour problem. Drop a cProfile on +`factor_graph.optimise(...)` excluding `search.fit(...)` cumulative +time, and report which `autofit/graphical/` functions exceed 5% time. +Cheap (~30 min to write the harness, ~30 min to run), informative, +and may surface a quadratic prior-walk that the toy is too small to +expose. Issue this *before* (5) and (6) because the data informs +whether parallelisation or sampler reuse is the better next bet. + +### 5. Parallelise local Dynesty fits across CPU cores per EP iteration + +IC50's Tier 2 win. Each EP iteration's N local fits are +embarrassingly parallel — no message passing within an iteration. A +process pool around the `analysis_factor_list` loop inside +`factor_graph.optimise` would give near-linear speedup up to the core +count. Care needed: the existing `force_x1_cpu` carve-out for +JAX-pooled searches and the search-cache state need to be re-entrant. +Sanity: results must match within numerical noise of the serial run. + +### 6. Reuse Dynesty sampler / pool across fits + +IC50's Tier 1.2 — at every EP iteration each factor's `search.fit` +re-instantiates a Dynesty sampler from scratch. EP's structure +("fit the same factor with mildly different priors each iteration") +is the textbook caching target. The first cut can be small: cache the +sampler object on the `AnalysisFactor` and re-seed its priors at each +iteration. Mandatory deliverable: matching posteriors to within a +tightened tolerance on the toy sanity checks. + +### 7. (Out of scope for toy) Replace Dynesty on a global factor with +LaplaceOptimiser + +IC50's Tier 3, item 6 — replace `DynestyStatic` on the global factor +(18 free params, smooth Gaussian likelihood) with a Newton-step +Laplace approximation. The toy doesn't have a hierarchical global +factor so this isn't measurable here, but the prompt should still +exist for the IC50 / cosmology use cases. Defer until a project with a +distinct global factor is the use case (cancer / cosmology). Note in +the prompt that PyAutoFit already exposes `LaplaceOptimiser` — wiring +it through `factor_graph.optimise(..., search_global=)` is the work. + +## Recommended Sequencing + +Mirrors IC50's prescribed ordering but tuned by the toy data: + +1. **(1) Suppress per-factor visualisation + (3) skip internal-folder + cleanup** — biggest single-PR win (~50% of EP runtime). Bundle into + one prompt under the `paths.ep_mode` flag. Issue this first; the + measured baseline tables will need a refresh afterwards. +2. **(2) TruncatedGaussianPrior fast-path** — shared with + graphical_scoping. Issue independently, can land in parallel. +3. **(4) cProfile inside `factor_graph.optimise`** — small, fast, + data-gathering prompt. Runs after (1) and (2) so the attribution + isn't dominated by visualisation/prior noise. +4. **Re-measure baseline at N=100 toy.** Project to N=1000. If wall + time projections look feasible, move to (5); if memory grows + unexpectedly, divert to memory profiling. +5. **(5) Parallelise local fits** and **(6) sampler reuse** — likely + the next big wins once the wrapper bucket is gone. Issue (5) before + (6) because parallelisation is more straightforward and yields a + bigger absolute reduction. +6. **(7) Global-factor Laplace** — defer until a non-toy use case + needs it. + +## Open data needed before issuing the next prompt + +- Re-confirm the visualisation share at N=30 (where there are 30+ + factors × 5 iterations × 2 plot calls per iteration). The toy + cProfile at N=10 already shows 48%; at N=30 we expect this to + approach 60%+. Worth one extra cProfile run after the worktree is + spun back up. +- Verify the `paths.ep_mode` flag interacts cleanly with + `autofit_workspace_test`'s graphical/ep scripts — those run the + same visualisation paths and may need their own toggle. +- The N=30 sanity FAIL needs root-causing before being treated as a + regression baseline. Possible causes: `max_steps=5` insufficient at + large N, KL tolerance too loose, message-field convergence stuck. + This is a small standalone prompt of its own (likely in autofit: + "investigate EP convergence on toy at N=30") — list it as a + prerequisite to the optimisation prompts so we don't accidentally + validate optimisations against a known-broken baseline. + +## Reference — IC50 detail used as input + +(Preserved verbatim from the task prompt for traceability when issuing +follow-up prompts.) + +> Tier 1 — Dynesty wrapper overhead (~86%): corner_anesthetic plotting, +> latent-sample draw, search-internal-folder cleanup, sampler +> reconstruction, paths setup. +> Tier 2 — Local Hill LL (~6%): linear in N; parallelise across CPU +> cores per EP iteration. +> Tier 3 — Global LL (~5%): constant in N (18 free params); replace +> Dynesty with LaplaceOptimiser. +> Tier 4 — EP-loop orchestration (~4%): cProfile to find any +> quadratic-in-N loops. +> Tier 5 — `set_model_approx` (~0%): skip. +> Sequence: cProfile attribution → suppress plotting + folder cleanup +> → Laplace global → measure at N=100 → sampler reuse + parallelisation +> → orchestration profile. + + diff --git a/tasks/gradient_sampler_validated_likelihoods.md b/tasks/gradient_sampler_validated_likelihoods.md new file mode 100644 index 0000000..857dcef --- /dev/null +++ b/tasks/gradient_sampler_validated_likelihoods.md @@ -0,0 +1 @@ +- [from: research jax-autodiff-gradients-audit (#87) · validated-likelihood set] NUTS/HMC sampler trial on the FD-validated likelihoods (weak lensing, point-source source-plane, imaging with RectangularUniform or parametric sources) via the sampler pipeline — gradients are now certified correct end-to-end, removing the main risk that stalled gradient-based inference. diff --git a/tasks/graphical_scoping.md b/tasks/graphical_scoping.md new file mode 100644 index 0000000..0b4c49e --- /dev/null +++ b/tasks/graphical_scoping.md @@ -0,0 +1,221 @@ +# Graphical Model Scale-Up — Scoping + +Type: research +Target: graphical_ep +Themes: +- graphical-ep +- samplers +Difficulty: too-large +Autonomy: human-required +Priority: high +Status: formalised +Consequence: judge +Review-minutes: 25 +Unattended: needs-slicing +Epic: graphical-ep +Phase: 3 +Filed: 2026-05-20 (backfilled from git) + +Companion baseline package: `autofit_workspace_developer/graphical/`. This +document is grounded in the profile data emitted by that package's +`profiles/baseline.json` and `profiles/N10_hotspots.txt`. Re-running the +sweep refreshes both files; the tables below should be re-derived before +issuing follow-up prompts. + +## Problem + +PyAutoFit's graphical-model framework fits a joint posterior over N +datasets that share parameters via a single `factor_graph.global_prior_model` +search. At small N (3–10) it works fine; at moderate N (30+) three +limitations stack up: + +1. **Sampler cost grows badly with dimensionality.** The toy model has + `3·N + 1` free parameters (per-dataset normalization + sigma plus one + shared centre). At N=30 that's 91 dimensions. Dynesty (a nested sampler + without gradient information) doesn't scale; with default `nlive=50` it + exits the sampling early and the max log likelihood doesn't converge + to the truth-evaluated value. +2. **Per-factor output overhead.** Every factor's fit emits corner plots, + latent-sample draws, samples-to-CSV passes, and a search-internal-folder + cleanup. Visualisation alone accounts for ~20% of wall time on a + converged joint fit and grows ∝ N. +3. **Single-point-of-reference inspection breaks down.** Once N is past + ~10, every dataset has its own folder of corner plots, samples, and + diagnostics. A scientist cannot scan 30+ folders to confirm a fit is + sensible. There is no aggregated "all-factor posterior" view. + +Scale-up has to address all three; the order of attack matters because +some optimisations make others measurable. + +## Measured Baseline (toy 1D Gaussian, shared centre) + +| N | wall time | peak RSS | output disk | max log L | truth log L | sanity | +|---|-----------|----------|-------------|-----------|-------------|--------| +| 3 | 22.8 s | 556 MB | 1.1 MB | -125.1 | -128.4 | PASS | +| 10 | 50.7 s | 844 MB | 3.7 MB | -519.3 | -472.3 | PASS | +| 30 | 132.8 s | 2.9 GB | 12.6 MB | -1658.7 | -1500.6 | **FAIL** | + +**Reproduce** from `~/Code/PyAutoLabs-wt//autofit_workspace_developer`: + +```bash +python3 graphical/simulator.py --total_datasets=30 +for N in 3 10 30; do + python3 graphical/fit.py --total_datasets=$N --name=N$N +done +``` + +**Observations:** +- Wall time scales sub-linearly in N — Dynesty's per-sample cost is + amortised across more datasets that share the JAX-jitted likelihood. +- **Peak RSS scales ~5×** between N=3 and N=30 (556 MB → 2.9 GB). At N=100+ + this becomes the binding constraint on a 16 GB laptop. +- **Sanity check FAILS at N=30**: max log L gap to truth is 158 nats vs a + tolerance of 150 (5 nats/dataset × 30). For 90 free params, Wilks + predicts the fit max should sit *above* truth by ~45 nats — getting + 158 nats below means the sampler did not converge. With `nlive=50` (the + current config default) and 91 dimensions, this is unsurprising. +- Output disk size is linear in N — at N=30 it's 12.6 MB, projecting to + ~420 MB at N=1000. Manageable, but tilts toward "sparse output" being a + real win at the high end. + +## Bottleneck Inventory (cProfile attribution at N=10) + +Source: `graphical/profiles/N10_hotspots.txt` (full top-30; condensed +families below). Total wall time inside the profiled section: ~46 s of +the 59 s cProfile run. + +| Family | cumulative time | % of fit | Notes | +|--------|-----------------|----------|-------| +| `scipy.stats.truncnorm.cdf` (inside `TruncatedGaussianPrior`) | 19.5 s | 33% | 184 200 calls, 7.3 s tottime | +| Dynesty sampling (`run_nested`, `_fill_queue`, `dynesty.__call__`) | 26.7 s | 45% | dominated by inner-loop prior transform + LL evaluation | +| `vector_from_unit_vector` + `prior_transform` (drives the truncnorm calls) | 23.2 s | 39% | wrapped by Dynesty but the cost is the truncnorm | +| `perform_update` + `visualize` | 18.9 s | 32% | corner-plot generation + samples_to_csv + latent-sample resampling | +| `Analysis.log_likelihood_function` (JAX-jitted) | small | <5% | numpy/JAX is fast; the prior transforms dominate | + +**Headline:** ~38% of the entire fit is spent transforming uniform unit-cube +points to the TruncatedGaussian via `scipy.stats.truncnorm.cdf/ppf`. This +function is notoriously slow — it falls back to a generic +`_distn_infrastructure` path that re-builds args and broadcasts per call. +A direct erf-based implementation or a JAX-jitted prior transform that +shares the array library with the likelihood would collapse this bucket. + +## Ranked Sub-Task Prompt Scopes + +Order is by payoff × ease, after the cProfile attribution. Each is +written as a future `/start_dev` candidate — issue them one at a time +through `autofit/...` after the previous lands. + +### 1. Gradient-based sampler for the joint factor graph + +**Highest scaling payoff.** Replace `DynestyStatic` on +`factor_graph.global_prior_model` with a NUTS/HMC sampler (BlackJAX or +NumPyro) that exploits the JAX-jitted likelihood's gradients. Dynesty +fundamentally cannot scale to N=300+ (dim ≈ 900); HMC sees linear +per-sample cost in dimensionality and converges in O(condition number) +iterations. Suggested first cut: re-use the existing +`autofit_workspace_developer/searches_minimal/nuts_jax.py` / +`nss_grad.py` machinery, wired in through `af.AnalysisFactor`'s +`optimiser=` kwarg. Mandatory deliverable: the sanity-check block at +N=30 must PASS (recovered shared centre within 3σ of truth, max log L +within Wilks-style tolerance of truth log L sum). This is the single +biggest win for scaling. + +### 2. JAX-native fast path for `TruncatedGaussianPrior.value_for` + +**Highest current-stack payoff, smallest change.** Replace the +`scipy.stats.truncnorm.cdf/ppf` call inside +`autofit/mapper/prior/truncated_gaussian.py:value_for` with an +erf-based closed-form (under the trunc bounds) or an +`autonerves.jax_wrapper`-aware path that uses `jax.scipy.special.erf` / +`erfinv` when the consumer is JAX-jitted. Expected fit-time reduction: +30–35% on any Dynesty workflow that uses TruncatedGaussianPrior heavily +(this includes most of `autofit_workspace_test` and the EP scaling +follow-ups). Sanity check: the toy graphical fit at N=3 should produce +the same max log likelihood within numerical noise. + +### 3. Suppress per-factor visualisation on the joint fit + +`updater.visualize` and `perform_update` together account for ~20% of +the joint fit. Most of this is producing per-dataset corner plots and +latent-sample draws that nobody inspects once N > 10. Add an +explicit `paths.suppress_per_factor_plots = True` (or similar) that +turns off everything except a single end-of-run summary view. Cross- +references EP's existing `corner_anesthetic` per-factor warning — the +plot family is doing wasted work on EP factors too. Expected payoff: +15–25% reduction in wall time on multi-factor fits; 0% effect on +single-factor / `searches_minimal/` runs. + +### 4. Sparse output-to-disk with run resumability + +At N=30 each fit produces 12.6 MB; at N=1000 we project ~420 MB per +run. More importantly, autofit's per-iteration `samples_to_csv` and +`search_internal/` overhead are I/O-bound during the fit, not just at +the end. Add an `output.mode = "sparse"` config knob that keeps only +the final samples + the model and skips per-iteration dumps, and +expose enough metadata that an interrupted run can resume from the +last samples file. The "remove search internal folder" wrapper line +(see Tier 1.3 of the IC50 prompt at the bottom) is the same bucket; +share infrastructure with the EP work. + +### 5. Single-point-of-reference results summary + +Once N > ~10, no scientist will scan 30 corner plots. Add an +end-of-fit aggregator that emits one JSON with the full per-factor +posterior table (param × dataset × mean ± σ), one composite corner +showing the shared latent variable across all factors, and one +parameter-recovery table comparing means/sigmas to the per-dataset +`ground_truth.json`. The sanity infrastructure already in +`graphical/sanity.py` is the seed for the parameter-recovery table. +This is the lowest payoff in time but the largest in scientist +confidence — it's the difference between "the fit converged" and "I +can read the result at a glance". + +### 6. Posterior dashboard for cross-dataset comparison + +After (5) lands, build a one-page HTML dashboard (or a notebook +template) that plots all factor posteriors aligned against the shared +latent variable's posterior, with the per-dataset truth overlaid. The +dashboard becomes the natural artefact a scientist links to in a +paper appendix or a Slack thread. Defer until (5) is shipped and the +JSON schema is stable. + +## Recommended Sequencing + +1. **(1) Gradient-based sampler** — unblocks N=100+ work and validates + that the sanity-check infrastructure correctly distinguishes a + converged from an unconverged fit. Without this nothing else + matters at scale. +2. **(2) TruncatedGaussianPrior fast-path** — easiest 30% wall-time + reduction across all Dynesty workflows. Independent of (1) and + could ship in parallel. Lands first if (1) hits architectural + surprises. +3. **(3) Suppress per-factor visualisation** — 15–25% wall-time + reduction; small code change. Land before any large N=100+ + benchmark so the per-factor noise doesn't pollute attribution. +4. **(4) Sparse output + resumability** — defer until (1) is in + place; the I/O overhead matters more once the sampling cost is + knocked down. +5. **(5) Single-point-of-reference summary** — issue alongside (1), + because the resulting JSON is what the gradient-sampler PR's + sanity-pass evidence will reference. +6. **(6) Posterior dashboard** — last; requires (5)'s schema to be + stable. + +## Open data needed before issuing the next prompt + +- Cross-check the cProfile attribution at N=30 too (the bucket mix may + shift once the fit is unconverged — sampling-loop time grows). Add a + `profile_N30.pstats` run to the sweep before issuing prompt (1). +- Confirm that the `searches_minimal/nuts_jax.py` and `nss_grad.py` + prototypes work against the new `graphical/fit.py` model layout — + they were written for `searches_minimal` and may need an + `AnalysisFactor`-friendly wrapper. Worth a quick read before + scoping prompt (1). +- Decide whether prompt (2) ships as a PyAutoFit-side change (modify + `truncated_gaussian.py` directly) or an autonerves-side helper that + both numpy and JAX call into. The dependency-graph note in + PyAutoFit's AGENTS.md (CLAUDE.md is a one-line import of it) says + "Shared utilities (e.g. `test_mode`, `jax_wrapper`) belong in + autonerves" — lean toward autonerves if the helper is non-trivial. + + diff --git a/tasks/reconcile_cortex_ledger_references.md b/tasks/reconcile_cortex_ledger_references.md new file mode 100644 index 0000000..a2887bb --- /dev/null +++ b/tasks/reconcile_cortex_ledger_references.md @@ -0,0 +1,33 @@ +# Reconcile inference documentation with the current Cortex ledger contract + +Type: docs +Target: @autolens_inference +Repos: +- autolens_inference +Difficulty: small +Autonomy: supervised +Priority: normal +Consequence: judge +Filed: 2026-10-01 +Status: draft + +## Original user direction (verbatim) + +Adopt the terminology and reconcile existing documentation (recommended) + +## Scope + +Follow the discrepancies documented by Brain#441 and +`PyAutoBrain/docs/research/ecosystem_levels.md`. The `point-source-search-nautilus-leaf` claim was released on 2026-10-02 +(record `complete/2026/10/point-source-search-nautilus-leaf.md`). Inspect current Cortex +AGENTS.md/REFERENCE.md and the project registry row, then update inference +`CORTEX.md` and affected passages of `AGENTS.md` to describe the current +Now / Runs / Log model, science/development boundary and actual ledger locations. +Remove live instructions that route to archived tasks/rulings; preserve legitimate +historical references as historical. Results and lessons remain the human's words. + +Do not edit Cortex state, science results, run tooling or historical archives. +Do not move ledgers or infer the live ledger path from a stale instruction. +Validate every changed path against the owning contract. This task complements +`complete/2026/10/ecosystem-role-docs.md` but has its own +repo, issue and PR. Existing claim is a scheduling constraint, not merge authorization. diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..b2b1675 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,163 @@ +"""Hermetic fixtures: a fabricated body map, registry and GitHub, no network.""" + +import json +import sys +import urllib.request +from pathlib import Path + +import pytest +import yaml + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +FIXTURES = Path(__file__).resolve().parent / "fixtures" +SHA_A = "a" * 40 +SHA_B = "b" * 40 +SHA_G = "c" * 40 + +LENS_ROW = { + "instance": "lens", + "repo": "autolens_inference", + "summary_path": "dashboard/summary.json", + "supported_schema": "inference-summary@1", + "dashboard_url": "https://pyautolabs.github.io/autolens_inference/", + "library_refs": ["PyAutoLens", "PyAutoFit"], + "cortex_project": None, +} +SECOND_ROW = { + "instance": "galaxy", + "repo": "autogalaxy_inference", + "summary_path": "out/inference_summary.json", + "supported_schema": "inference-summary@1", + "dashboard_url": "https://example.invalid/autogalaxy_inference/", + "library_refs": ["PyAutoGalaxy"], + "cortex_project": None, +} +BODY_MAP = { + "repos": { + "autolens_inference": { + "path": "lens/autolens_inference", + "github": "PyAutoLabs/autolens_inference", + "category": "project", + }, + "autogalaxy_inference": { + "path": "galaxy/autogalaxy_inference", + "github": "PyAutoLabs/autogalaxy_inference", + "category": "project", + }, + "PyAutoLens": {"path": "lens/PyAutoLens", "github": "PyAutoLabs/PyAutoLens"}, + "PyAutoGalaxy": {"path": "galaxy/PyAutoGalaxy", "github": "PyAutoLabs/PyAutoGalaxy"}, + "PyAutoFit": {"path": "fit/PyAutoFit", "github": "PyAutoLabs/PyAutoFit"}, + } +} + + +def fixture_doc(name: str) -> dict: + return json.loads((FIXTURES / name).read_text()) + + +@pytest.fixture(autouse=True) +def no_network(monkeypatch): + """No test may reach the network; tests that need GitHub use ``web``.""" + + def refuse(request, *a, **k): + url = getattr(request, "full_url", request) + raise OSError(f"network disabled in tests: {url}") + + monkeypatch.setattr(urllib.request, "urlopen", refuse) + + +@pytest.fixture(autouse=True) +def isolated_env(monkeypatch, tmp_path): + """No test resolves the real PyAutoMind or a token from the shell.""" + monkeypatch.delenv("PYAUTO_MIND", raising=False) + monkeypatch.delenv("GH_TOKEN", raising=False) + monkeypatch.setenv("PYAUTO_ROOT", str(tmp_path / "no-workspace-here")) + + +@pytest.fixture +def fake_mind(tmp_path): + """A fabricated PyAutoMind checkout holding a minimal repos.yaml.""" + root = tmp_path / "PyAutoMind" + root.mkdir() + (root / "repos.yaml").write_text(yaml.safe_dump(BODY_MAP)) + return root + + +@pytest.fixture +def body_map(): + return {k: dict(v) for k, v in BODY_MAP["repos"].items()} + + +@pytest.fixture +def registry_file(tmp_path): + path = tmp_path / "registry.yaml" + path.write_text(yaml.safe_dump({"schema": 1, "instances": [dict(LENS_ROW)]})) + return path + + +@pytest.fixture +def two_registry_file(tmp_path): + path = tmp_path / "registry2.yaml" + path.write_text(yaml.safe_dump({"schema": 1, "instances": [dict(LENS_ROW), dict(SECOND_ROW)]})) + return path + + +class _Response: + def __init__(self, body: bytes): + self._body = body + + def read(self): + return self._body + + def __enter__(self): + return self + + def __exit__(self, *exc): + return False + + +class FakeGitHub: + """Serves the commits API and raw files from a dict; logs every request. + + ``branches[github] = sha`` answers the commits API; ``files[(github, sha, + path)] = bytes`` answers raw.githubusercontent.com. ``down = True`` makes + every request fail like a dropped connection. + """ + + def __init__(self): + self.branches: dict[str, str] = {} + self.files: dict[tuple[str, str, str], bytes] = {} + self.requests: list = [] + self.down = False + + def publish(self, github, sha, path, doc): + self.branches[github] = sha + body = doc if isinstance(doc, bytes) else json.dumps(doc).encode() + self.files[(github, sha, path)] = body + + def __call__(self, request, timeout=None): + url = request.full_url + self.requests.append(request) + if self.down: + raise OSError("connection reset by peer") + api = "https://api.github.com/repos/" + raw = "https://raw.githubusercontent.com/" + if url.startswith(api): + rest = url[len(api) :] + github, _, branch = rest.partition("/commits/") + if github in self.branches: + return _Response(json.dumps({"sha": self.branches[github]}).encode()) + elif url.startswith(raw): + owner, repo, sha, path = url[len(raw) :].split("/", 3) + body = self.files.get((f"{owner}/{repo}", sha, path)) + if body is not None: + return _Response(body) + raise OSError(f"404 {url}") + + +@pytest.fixture +def web(monkeypatch): + fake = FakeGitHub() + monkeypatch.setattr(urllib.request, "urlopen", fake) + return fake diff --git a/tests/fixtures/duplicate_id.json b/tests/fixtures/duplicate_id.json new file mode 100644 index 0000000..383acd3 --- /dev/null +++ b/tests/fixtures/duplicate_id.json @@ -0,0 +1,410 @@ +{ + "comparison_policy": { + "id": "declared-target-seed-stage-v1", + "note": "Targets/seeds/stages retained; no automatic parity certification or best-seed selection", + "ranking": "none" + }, + "comparisons": [], + "coverage": { + "excluded": [], + "expected": { + "reason": "No complete expected-run manifest; unrecorded jobs cannot be enumerated", + "runs": null + }, + "observed": { + "records": 1, + "runs": 1, + "archived_records": 0, + "execution_complete": 1 + } + }, + "evidence_updated_at": null, + "evidence_updated_at_reason": "Legacy result rows do not record measurement timestamps", + "generated_at": "2026-10-04T07:35:14Z", + "generation_basis": "Source commit timestamp; not a measurement or check-in time", + "limitations": [ + "No scientific acceptance or convergence inferred from process completion", + "Sample archives remain in project storage; current availability unknown", + "No evidence from the retired inference programme is imported", + "Archived rows belong to this project's September 2026 runs and are historical, not current baselines" + ], + "producer_revision": "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", + "project": "autolens_inference", + "records": [ + { + "archived": false, + "backend": "jax_cpu", + "comparison": { + "declared_target": "simple/source_plane_solved/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_cpu_dense_fp64", + "cores": 8, + "fit_positions_cls": "FitPositionsSourceSolved", + "free_parameters": 5, + "inversion": "dense", + "n_batch": 50, + "n_live": 100, + "priors": { + "einstein_radius": "U(0.5, 4.0)", + "others": "library-default Isothermal priors" + }, + "resumed": false, + "test_mode": false, + "use_jax": true, + "variant": "source_plane_solved" + }, + "dataset": { + "class": "point_source", + "id": null, + "instrument": "simple" + }, + "dependency_revisions": { + "autoarray": "9428eca2", + "autofit": "404b3e5f7", + "autogalaxy": "c9609825", + "autolens": "21b520be4", + "autonerves": "bf10410" + }, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": false, + "likelihood_evals": 4850, + "likelihood_share": 0.00040501777436034486, + "log_evidence": -7.153937371523832, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "log_likelihood_at_truth": 15.902398430038737, + "max_log_likelihood": 16.48568552789147, + "per_call_s": 4.723259480670094e-06, + "positions_info_present": false, + "posterior": { + "centre_0": { + "lower_1": -0.04216816245874099, + "median": -0.017453424264998282, + "sigma": 0.025797484788372172, + "upper_1": 0.00942680711800335 + }, + "centre_1": { + "lower_1": -0.04336377661022216, + "median": -0.018067299498348146, + "sigma": 0.025791414665263836, + "upper_1": 0.00821905272030551 + }, + "einstein_radius": { + "lower_1": 1.5945063508229387, + "median": 1.6056260743312727, + "sigma": 0.01115243788494824, + "upper_1": 1.6168112265928352 + }, + "ell_comps_0": { + "lower_1": 0.047688746799381704, + "median": 0.06559594956905983, + "sigma": 0.01762818058418965, + "upper_1": 0.082945107967761 + }, + "ell_comps_1": { + "lower_1": -0.00019838121507798336, + "median": 2.4804878302780597e-05, + "sigma": 0.00022114345613303182, + "upper_1": 0.0002439056971880803 + } + }, + "timing": { + "batched": { + "median_s": 0.00023616297403350472, + "min_s": 0.00019111903384327888, + "n": 200, + "p16_s": 0.00021696899784728885, + "p84_s": 0.00025908800307661295, + "warmup": 10 + }, + "compile_s": 1.9011598539655097, + "compile_single_s": 1.3180420650169253, + "single": { + "median_s": 0.0002068985195364803, + "min_s": 0.00016070902347564697, + "n": 200, + "p16_s": 0.00019189901649951935, + "p84_s": 0.00022341799922287464, + 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"not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": null, + "target": "simple/source_plane_solved/seed0", + "timings": { + "compile_s": 1.9011598539655097, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 56.56000781059265, + "setup_s": null, + "total_s": 65.21035292901797 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "measured_at": "Not recorded in source result" + } + } + ], + "schema": "inference-summary", + "scope": "inference", + "source_commit": "e60fd387cd5d112782888671d5a9471432abcc93", + "valid_until": null, + "version": 1 +} diff --git a/tests/fixtures/unsupported_schema.json b/tests/fixtures/unsupported_schema.json new file mode 100644 index 0000000..0876396 --- /dev/null +++ b/tests/fixtures/unsupported_schema.json @@ -0,0 +1,225 @@ +{ + "comparison_policy": { + "id": "declared-target-seed-stage-v1", + "note": "Targets/seeds/stages retained; no automatic parity certification or best-seed selection", + "ranking": "none" + }, + "comparisons": [], + "coverage": { + "excluded": [], + "expected": { + "reason": "No complete expected-run manifest; unrecorded jobs cannot be enumerated", + "runs": null + }, + "observed": { + "records": 1, + "runs": 1, + "archived_records": 0, + "execution_complete": 1 + } + }, + "evidence_updated_at": null, + "evidence_updated_at_reason": "Legacy result rows do not record measurement timestamps", + "generated_at": "2026-10-04T07:35:14Z", + "generation_basis": "Source commit timestamp; not a measurement or check-in time", + "limitations": [ + "No scientific acceptance or convergence inferred from process completion", + "Sample archives remain in project storage; current availability unknown", + "No evidence from the retired inference programme is imported", + "Archived rows belong to this project's September 2026 runs and are historical, not current baselines" + ], + "producer_revision": "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", + "project": "autolens_inference", + "records": [ + { + "archived": false, + "backend": "jax_cpu", + "comparison": { + "declared_target": "simple/source_plane_solved/seed0", + "group": null, + "note": "Declared target is not a verified comparison group; no ranking" + }, + "configuration": { + "config_name": "hpc_a100_jax_cpu_dense_fp64", + "cores": 8, + "fit_positions_cls": "FitPositionsSourceSolved", + "free_parameters": 5, + "inversion": "dense", + "n_batch": 50, + "n_live": 100, + "priors": { + "einstein_radius": "U(0.5, 4.0)", + "others": "library-default Isothermal priors" + }, + "resumed": false, + "test_mode": false, + "use_jax": true, + "variant": "source_plane_solved" + }, + "dataset": { + "class": "point_source", + "id": null, + "instrument": "simple" + }, + "dependency_revisions": { + "autoarray": "9428eca2", + "autofit": "404b3e5f7", + "autogalaxy": "c9609825", + "autolens": "21b520be4", + "autonerves": "bf10410" + }, + "diagnostics": { + "status": "available", + "values": { + "inversion_applies": false, + "likelihood_evals": 4850, + "likelihood_share": 0.00040501777436034486, + "log_evidence": -7.153937371523832, + "log_evidence_err_note": "nautilus 1.0.5 exposes no log_z_err; samples_info carries only log_evidence, total_samples, time and number_live_points.", + "log_likelihood_at_truth": 15.902398430038737, + "max_log_likelihood": 16.48568552789147, + "per_call_s": 4.723259480670094e-06, + "positions_info_present": false, + "posterior": { + "centre_0": { + "lower_1": -0.04216816245874099, + "median": -0.017453424264998282, + "sigma": 0.025797484788372172, + "upper_1": 0.00942680711800335 + }, + "centre_1": { + "lower_1": -0.04336377661022216, + "median": -0.018067299498348146, + "sigma": 0.025791414665263836, + "upper_1": 0.00821905272030551 + }, + "einstein_radius": { + "lower_1": 1.5945063508229387, + "median": 1.6056260743312727, + "sigma": 0.01115243788494824, + "upper_1": 1.6168112265928352 + }, + "ell_comps_0": { + "lower_1": 0.047688746799381704, + "median": 0.06559594956905983, + "sigma": 0.01762818058418965, + "upper_1": 0.082945107967761 + }, + "ell_comps_1": { + "lower_1": -0.00019838121507798336, + "median": 2.4804878302780597e-05, + "sigma": 0.00022114345613303182, + "upper_1": 0.0002439056971880803 + } + }, + "timing": { + "batched": { + "median_s": 0.00023616297403350472, + "min_s": 0.00019111903384327888, + "n": 200, + "p16_s": 0.00021696899784728885, + "p84_s": 0.00025908800307661295, + "warmup": 10 + }, + "compile_s": 1.9011598539655097, + "compile_single_s": 1.3180420650169253, + "single": { + "median_s": 0.0002068985195364803, + "min_s": 0.00016070902347564697, + "n": 200, + "p16_s": 0.00019189901649951935, + "p84_s": 0.00022341799922287464, + "warmup": 10 + }, + "vector": [ + 0.0, + 0.0, + 0.0, + 0.0, + 2.25 + ] + }, + "truth_delta_sigma": { + "centre_0": -0.6765552691735727, + "centre_1": -0.7005160334489673, + "einstein_radius": 0.5044703578995735, + "ell_comps_0": 0.7354344119505386, + "ell_comps_1": 0.112166458535656 + }, + "truths": { + "centre_0": 0.0, + "centre_1": 0.0, + "einstein_radius": 1.6, + "ell_comps_0": 0.05263157894736841, + "ell_comps_1": 3.2227547345982974e-18 + } + } + }, + "evidence_paths": [ + "results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed0.json" + ], + "execution": { + "completed": true, + "reason": null, + "status": "complete" + }, + "hardware": { + "cores": 8, + "device": { + "backend": "cpu", + "cpu_count": 124, + "device": "cpu:0", + "omp_num_threads": null, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" + }, + "host": "euclid-ral-gpu-2", + "slurm_array_job_id": null, + "slurm_array_task_id": null, + "slurm_job_id": "366937" + }, + "id": "results/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/search_seed0.json", + "library_version": "2026.8.17.1", + "measured_at": null, + "model": "L5: Isothermal (5 free) + PointSolved source", + "parent_run_id": null, + "pipeline": null, + "precision": "fp64", + "sampler": "nautilus", + "samples": { + "access": "project-storage-not-published", + "availability": "unknown", + "note": "Committed results do not verify current archive/sample availability; unsafe absolute paths omitted", + "path": "output/searches/point_source/nautilus/simple/source_plane_solved/hpc_a100_jax_cpu_dense_fp64/seed_0/nautilus/b39f17cc6d57dba03e090e4d9682ce2b" + }, + "scientific": { + "acceptance": "not_assessed", + "convergence": "not_assessed", + "reason": "Exporter does not make scientific rulings; consult Cortex" + }, + "seed": 0, + "stage": null, + "target": "simple/source_plane_solved/seed0", + "timings": { + "compile_s": 1.9011598539655097, + "definitions": { + "compile_s": "Separate likelihood warm-up probe; not added to total_s", + "sampling_s": "wall_s: sampler clock from samples_info.json; includes sampler overhead", + "setup_s": "Not measured separately; unknown, not total minus sampling", + "total_s": "total_wall_s: elapsed search.fit call; excludes prior compile probe and dataset/model setup" + }, + "sampling_s": 56.56000781059265, + "setup_s": null, + "total_s": 65.21035292901797 + }, + "unknown_reasons": { + "dataset.id": "Dataset content identity not recorded", + "measured_at": "Not recorded in source result" + } + } + ], + "schema": "inference-summary", + "scope": "inference", + "source_commit": "e60fd387cd5d112782888671d5a9471432abcc93", + "valid_until": null, + "version": 77 +} diff --git a/tests/fixtures/valid_empty.json b/tests/fixtures/valid_empty.json new file mode 100644 index 0000000..9a6700d --- /dev/null +++ b/tests/fixtures/valid_empty.json @@ -0,0 +1,37 @@ +{ + "comparison_policy": { + "id": "declared-target-seed-stage-v1", + "note": "Targets/seeds/stages retained; no automatic parity certification or best-seed selection", + "ranking": "none" + }, + "comparisons": [], + "coverage": { + "excluded": [], + "expected": { + "reason": "No complete expected-run manifest; unrecorded jobs cannot be enumerated", + "runs": null + }, + "observed": { + "records": 0, + "runs": 0 + } + }, + "evidence_updated_at": null, + "evidence_updated_at_reason": "Legacy result rows do not record measurement timestamps", + "generated_at": "2026-10-04T07:35:14Z", + "generation_basis": "Source commit timestamp; not a measurement or check-in time", + "limitations": [ + "No scientific acceptance or convergence inferred from process completion", + "Sample archives remain in project storage; current availability unknown", + "No evidence from the retired inference programme is imported", + "Archived rows belong to this project's September 2026 runs and are historical, not current baselines" + ], + "producer_revision": "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", + "project": "autolens_inference", + "records": [], + "schema": "inference-summary", + "scope": "inference", + "source_commit": "e60fd387cd5d112782888671d5a9471432abcc93", + "valid_until": null, + "version": 1 +} diff --git a/tests/test_board.py b/tests/test_board.py new file mode 100644 index 0000000..4645fb0 --- /dev/null +++ b/tests/test_board.py @@ -0,0 +1,82 @@ +"""Reproducibility, honesty, and the single editable control-room entry point.""" + +import json +from pathlib import Path + +from conftest import fixture_doc + +from insight import board, campaigns, ingest, registry + + +def view(registry_file, fake_mind, name="lens_summary_v1.json"): + inst = registry.load(registry_file, mind=fake_mind)[0] + return ingest.Snapshot( + inst, + "ok", + "remote", + doc=fixture_doc(name), + commit="a" * 40, + fetched_at="2026-10-04T00:00:00Z", + ) + + +def test_controlroom_order_and_domain_labels(registry_file, fake_mind): + s = view(registry_file, fake_mind) + html = board.render_html([s], now="2026-10-04T00:00:00Z") + assert ( + html.index('id="checkin-prompt"') + < html.index('id="campaigns"') + < html.index('id="tasks"') + < html.index('id="evidence"') + ) + assert "Inference evidence" in html + assert "Profiling evidence" not in html + assert "Recent progress" in html and "Blockers" in html and "Job status" in html + assert "Execution completion is separate" in html + + +def test_cached_stale_evidence_keeps_age_and_separate_qualification(registry_file, fake_mind): + s = view(registry_file, fake_mind) + s.cached = True + s.outcome = "unavailable" + s.doc["evidence_updated_at"] = "2026-09-01T00:00:00Z" + s.doc["valid_until"] = "2026-09-02T00:00:00Z" + assert "cached" in board.integrity(s) + assert "stale" in board.freshness(s, "2026-10-04T00:00:00Z") + assert "0/1 accepted" in board.qualification(s) + assert board.render_state([s], now="2026-10-04T00:00:00Z")["status"] == "yellow" + assert ( + s.fetched_at == "2026-10-04T00:00:00Z" + and s.doc["evidence_updated_at"] == "2026-09-01T00:00:00Z" + ) + + +def test_empty_and_unavailable_do_not_become_green(registry_file, fake_mind): + s = view(registry_file, fake_mind, "valid_empty.json") + assert board.render_state([s])["status"] == "yellow" + assert "No measurements" in board.render_html([s]) + s.doc = None + s.outcome = "unavailable" + assert board.render_state([s])["status"] == "red" + + +def test_repeat_render_is_byte_identical_and_does_not_stamp_checkin( + tmp_path, registry_file, fake_mind +): + s = view(registry_file, fake_mind) + ledger = Path(campaigns.ORGAN_ROOT) / "campaigns.yaml" + before = ledger.read_bytes() + board.write([s], tmp_path, now="2026-10-04T00:00:00Z") + files = {p.name: p.read_bytes() for p in tmp_path.glob("*") if p.is_file()} + board.write([s], tmp_path, now="2026-10-04T00:00:00Z") + assert {p.name: p.read_bytes() for p in tmp_path.glob("*") if p.is_file()} == files + assert ledger.read_bytes() == before + assert json.loads((tmp_path / "state.json").read_text())["organ"] == "insight" + + +def test_render_respects_supplied_clock_for_freshness(registry_file, fake_mind): + s = view(registry_file, fake_mind) + s.doc["evidence_updated_at"] = "2020-01-01T00:00:00Z" + s.doc["valid_until"] = "2020-01-03T00:00:00Z" + assert "valid · deadline" in board.render_html([s], now="2020-01-02T00:00:00Z") + assert "stale · deadline" in board.render_html([s], now="2020-01-04T00:00:00Z") diff --git a/tests/test_campaigns.py b/tests/test_campaigns.py new file mode 100644 index 0000000..5116c3d --- /dev/null +++ b/tests/test_campaigns.py @@ -0,0 +1,89 @@ +"""Ledger/migration integrity and actual editable-copy JavaScript behavior.""" + +import copy +import json +import os +import shutil +import subprocess +from pathlib import Path + +import pytest +import yaml + +from insight import campaigns +from insight.theme import JS + + +def copied_ledger(tmp_path): + root = Path(campaigns.ORGAN_ROOT) + for file in ["campaigns.yaml", "migration.yaml"]: + shutil.copy(root / file, tmp_path / file) + shutil.copytree(root / "tasks", tmp_path / "tasks") + return yaml.safe_load((tmp_path / "campaigns.yaml").read_text()) + + +def test_current_ledger_and_migration_validate(): + data = campaigns.load() + migration = campaigns.validate_migration() + assert len(data["campaigns"]) >= 8 and len(migration["tasks"]) >= 6 + assert data["last_checkin"] is None + assert any(c["status"] == "blocked" for c in data["campaigns"]) + assert any(c["status"] == "needs-decision" for c in data["campaigns"]) + assert all(c["job_status"].startswith("unknown") for c in data["campaigns"]) + + +@pytest.mark.parametrize( + "change", ["duplicate", "unknown-campaign", "path-traversal", "invalid-date"] +) +def test_invalid_ledgers_are_rejected(tmp_path, change): + data = copied_ledger(tmp_path) + if change == "duplicate": + data["campaigns"].append(copy.deepcopy(data["campaigns"][0])) + if change == "unknown-campaign": + data["tasks"][0]["campaign"] = "missing" + if change == "path-traversal": + data["tasks"][0]["path"] = "tasks/../campaigns.yaml" + if change == "invalid-date": + data["campaigns"][0]["updated"] = "not-a-date" + (tmp_path / "campaigns.yaml").write_text(yaml.safe_dump(data)) + with pytest.raises(campaigns.CampaignError): + campaigns.load(tmp_path) + + +def test_migration_rejects_changed_original_bytes(tmp_path): + copied_ledger(tmp_path) + migration = yaml.safe_load((tmp_path / "migration.yaml").read_text()) + target = tmp_path / migration["tasks"][0]["destination"] + target.write_text(target.read_text() + "altered") + with pytest.raises(campaigns.CampaignError, match="original changed"): + campaigns.validate_migration(tmp_path) + + +def test_closed_tasks_hidden_but_parked_stays_visible(): + data = copy.deepcopy(campaigns.load()) + task = data["tasks"][0] + task["title"] = "UNIQUE_PARKED_TASK" + task["status"] = "parked" + assert "UNIQUE_PARKED_TASK" in campaigns.render_html(data) + task["status"] = "complete" + assert "UNIQUE_PARKED_TASK" not in campaigns.render_html(data) + + +@pytest.mark.parametrize("mode", ["clipboard", "fallback", "rejected", "failed-fallback"]) +def test_copy_button_copies_edited_text_or_selects_manual_fallback(mode): + node = os.environ.get("CODEX_PRIMARY_RUNTIME_NODE") or shutil.which("node") + if not node: + pytest.fail("Node runtime required to verify actual copy behavior") + harness = r""" +const assert=require('node:assert/strict'); +let callback,copied=null,focused=false,selected=false; +let field={value:'USER EDITED: focus seed 4',focus(){focused=true},select(){selected=true}}; +let status={textContent:''}; +let mode=MODE; +global.document={getElementById(id){return id==='copy-checkin'?{addEventListener(_,cb){callback=cb}}:id==='checkin-prompt'?field:status},querySelectorAll(){return []},execCommand(command){assert.equal(command,'copy');if(mode==='failed-fallback')return false;copied=field.value;return true}}; +global.window={isSecureContext:mode!=='fallback'&&mode!=='failed-fallback'}; +Object.defineProperty(global,'navigator',{value:{clipboard:{async writeText(t){if(mode==='rejected')throw Error('denied');copied=t}}},configurable:true}); +SCRIPT +(async()=>{await callback(); if(mode==='rejected'||mode==='failed-fallback'){assert.equal(copied,null);assert.ok(focused&&selected);assert.match(status.textContent,/Select and copy/)}else{assert.equal(copied,field.value);assert.equal(status.textContent,'Copied')}})().catch(e=>{console.error(e);process.exitCode=1}); +""".replace("MODE", json.dumps(mode)).replace("SCRIPT", JS) + subprocess.run([node, "-e", harness], check=True, capture_output=True, text=True) diff --git a/tests/test_cli.py b/tests/test_cli.py new file mode 100644 index 0000000..381c39b --- /dev/null +++ b/tests/test_cli.py @@ -0,0 +1,38 @@ +"""Offline check catches stale evidence and registry/receipt mismatch.""" + +import json + +from conftest import SHA_A, SHA_B, fixture_doc + +from insight import cli, ingest, registry + + +def test_offline_roundtrip_and_stale_dashboard_detection(tmp_path, registry_file, fake_mind, web): + inst = registry.load(registry_file, mind=fake_mind)[0] + web.publish(inst.github, SHA_A, inst.summary_path, fixture_doc("lens_summary_v1.json")) + ingest.ingest(inst, tmp_path, now="2026-10-04T00:00:00Z") + base = ["--registry", str(registry_file)] + flags = ["--mind", str(fake_mind), "--out", str(tmp_path), "--offline"] + assert cli.main([*base, "board", *flags]) == 0 + assert cli.main([*base, "check", *flags]) == 0 + web.publish(inst.github, SHA_B, inst.summary_path, fixture_doc("lens_summary_v1.json")) + ingest.ingest(inst, tmp_path, now="2026-10-05T00:00:00Z") + assert cli.main([*base, "check", *flags]) == 1 + assert cli.main([*base, "board", *flags]) == 0 + assert cli.main([*base, "check", *flags]) == 0 + + +def test_check_rejects_receipt_revision_different_from_snapshot( + tmp_path, registry_file, fake_mind, web +): + inst = registry.load(registry_file, mind=fake_mind)[0] + web.publish(inst.github, SHA_A, inst.summary_path, fixture_doc("lens_summary_v1.json")) + ingest.ingest(inst, tmp_path, now="2026-10-04T00:00:00Z") + base = ["--registry", str(registry_file)] + flags = ["--mind", str(fake_mind), "--out", str(tmp_path), "--offline"] + assert cli.main([*base, "board", *flags]) == 0 + path = tmp_path / "receipts/lens.json" + doc = json.loads(path.read_text()) + doc["resolved_commit"] = SHA_B + path.write_text(json.dumps(doc)) + assert cli.main([*base, "check", *flags]) == 1 diff --git a/tests/test_ingest.py b/tests/test_ingest.py new file mode 100644 index 0000000..4b45414 --- /dev/null +++ b/tests/test_ingest.py @@ -0,0 +1,144 @@ +"""One-commit ingest, receipts, last-good snapshots and cached semantics.""" + +import json +import subprocess + +import pytest +from conftest import SHA_A, SHA_B, fixture_doc + +from insight import ingest, registry + +GH = "PyAutoLabs/autolens_inference" +PATH = "dashboard/summary.json" + + +@pytest.fixture +def lens(registry_file, fake_mind): + return registry.get(registry.load(registry_file, mind=fake_mind), "lens") + + +def test_ok_reads_the_file_at_the_resolved_commit(lens, web, tmp_path, monkeypatch): + monkeypatch.setenv("GH_TOKEN", "t0k") + web.publish(GH, SHA_A, PATH, fixture_doc("lens_summary_v1.json")) + s = ingest.ingest(lens, tmp_path, now="2026-10-02T00:00:00Z") + assert s.outcome == "ok" and not s.cached and s.commit == SHA_A + api, raw = (r.full_url for r in web.requests) + assert api == f"https://api.github.com/repos/{GH}/commits/main" + assert raw == f"https://raw.githubusercontent.com/{GH}/{SHA_A}/{PATH}" + assert web.requests[0].get_header("Authorization") == "Bearer t0k" + assert web.requests[1].get_header("Authorization") is None # the token never leaves the API + assert web.requests[0].get_header("User-agent") == ingest.USER_AGENT + receipt = ingest.read_receipt(tmp_path, "lens") + assert receipt["resolved_commit"] == SHA_A and receipt["outcome"] == "ok" + assert receipt["record_count"] == 1 and receipt["errors"] == [] + assert receipt["cached_from"] is None + snap = ingest.read_snapshot(tmp_path, "lens") + assert snap["commit"] == SHA_A and snap["fetched_at"] == "2026-10-02T00:00:00Z" + + +def test_unchanged_reingest_rewrites_nothing(lens, web, tmp_path): + web.publish(GH, SHA_A, PATH, fixture_doc("lens_summary_v1.json")) + ingest.ingest(lens, tmp_path, now="2026-10-02T00:00:00Z") + before = {p: p.read_bytes() for p in tmp_path.glob("*s/lens.json")} + ingest.ingest(lens, tmp_path, now="2026-10-03T00:00:00Z") + assert {p: p.read_bytes() for p in tmp_path.glob("*s/lens.json")} == before + web.publish(GH, SHA_B, PATH, fixture_doc("lens_summary_v1.json")) + s = ingest.ingest(lens, tmp_path, now="2026-10-04T00:00:00Z") + assert s.commit == SHA_B and s.fetched_at == "2026-10-04T00:00:00Z" + + +def test_unavailable_keeps_the_cached_snapshot_with_its_original_times(lens, web, tmp_path): + web.publish(GH, SHA_A, PATH, fixture_doc("lens_summary_v1.json")) + good = ingest.ingest(lens, tmp_path, now="2026-10-02T00:00:00Z") + web.down = True + s = ingest.ingest(lens, tmp_path, now="2026-10-09T00:00:00Z") + assert s.outcome == "unavailable" and s.cached + assert s.commit == SHA_A and s.fetched_at == "2026-10-02T00:00:00Z" + assert s.doc["evidence_updated_at"] == good.doc["evidence_updated_at"] + assert "connection reset" in s.errors[0] + receipt = ingest.read_receipt(tmp_path, "lens") + assert receipt["outcome"] == "unavailable" and receipt["resolved_commit"] is None + assert receipt["fetched_at"] == "2026-10-09T00:00:00Z" + assert receipt["cached_from"] == "2026-10-02T00:00:00Z" + + +def test_unavailable_with_no_snapshot_has_nothing_to_show(lens, tmp_path): + s = ingest.ingest(lens, tmp_path, now="2026-10-02T00:00:00Z") # no_network refuses + assert s.outcome == "unavailable" and s.doc is None and not s.cached + assert ingest.read_snapshot(tmp_path, "lens") is None + + +@pytest.mark.parametrize( + "fixture, outcome", + [("unsupported_schema.json", "unsupported"), ("duplicate_id.json", "invalid")], +) +def test_bad_feeds_fail_and_keep_the_last_good_snapshot(lens, web, tmp_path, fixture, outcome): + web.publish(GH, SHA_A, PATH, fixture_doc("lens_summary_v1.json")) + ingest.ingest(lens, tmp_path, now="2026-10-02T00:00:00Z") + web.publish(GH, SHA_B, PATH, fixture_doc(fixture)) + s = ingest.ingest(lens, tmp_path, now="2026-10-03T00:00:00Z") + assert s.outcome == outcome and s.cached + assert s.commit == SHA_A and s.attempt_commit == SHA_B + assert ingest.read_snapshot(tmp_path, "lens")["commit"] == SHA_A + + +def test_not_json_is_invalid(lens, web, tmp_path): + web.publish(GH, SHA_A, PATH, b"404") + s = ingest.ingest(lens, tmp_path, now="2026-10-02T00:00:00Z") + assert s.outcome == "invalid" and "not JSON" in s.errors[0] + + +def test_offline_reads_committed_files_and_writes_nothing(lens, web, tmp_path): + web.publish(GH, SHA_A, PATH, fixture_doc("lens_summary_v1.json")) + ingest.ingest(lens, tmp_path, now="2026-10-02T00:00:00Z") + web.requests.clear() + s = ingest.ingest(lens, tmp_path, offline=True) + assert s.outcome == "ok" and s.commit == SHA_A and web.requests == [] + + +def test_a_local_read_reports_revision_and_dirty_state_and_writes_nothing(lens, tmp_path): + checkout = tmp_path / "autolens_inference" + (checkout / "dashboard").mkdir(parents=True) + (checkout / PATH).write_text(json.dumps(fixture_doc("valid_empty.json"))) + git = ["git", "-C", str(checkout), "-c", "user.email=t@t", "-c", "user.name=t"] + subprocess.run([*git, "init", "-q"], check=True) + subprocess.run([*git, "add", "."], check=True) + subprocess.run([*git, "commit", "-qm", "x"], check=True) + out = tmp_path / "organ" + s = ingest.ingest(lens, out, local_path=checkout) + assert s.source == "local" and s.dirty is False and len(s.commit) == 40 + (checkout / "scratch.txt").write_text("edit") + assert ingest.ingest(lens, out, local_path=checkout).dirty is True + assert not (out / "receipts").exists() and not (out / "snapshots").exists() + + +def test_project_identity_mismatch_is_invalid(lens, web, tmp_path): + web.publish(GH, SHA_A, PATH, fixture_doc("galaxy_summary_v1.json")) + s = ingest.ingest(lens, tmp_path) + assert s.outcome == "invalid" and s.doc is None + + +def test_second_producer_uses_same_reader(two_registry_file, fake_mind, web, tmp_path): + instances = registry.load(two_registry_file, mind=fake_mind) + web.publish(GH, SHA_A, PATH, fixture_doc("lens_summary_v1.json")) + web.publish( + "PyAutoLabs/autogalaxy_inference", + SHA_B, + "out/inference_summary.json", + fixture_doc("galaxy_summary_v1.json"), + ) + views = [ingest.ingest(i, tmp_path) for i in instances] + assert [s.outcome for s in views] == ["ok", "ok"] + assert [s.doc["project"] for s in views] == ["autolens_inference", "autogalaxy_inference"] + assert len(list((tmp_path / "receipts").glob("*.json"))) == 2 + + +def test_offline_rejects_corrupt_snapshot_identity(lens, web, tmp_path): + web.publish(GH, SHA_A, PATH, fixture_doc("lens_summary_v1.json")) + ingest.ingest(lens, tmp_path) + path = tmp_path / "snapshots/lens.json" + d = json.loads(path.read_text()) + d["summary"]["project"] = "other_project" + path.write_text(json.dumps(d)) + s = ingest.ingest(lens, tmp_path, offline=True) + assert s.outcome == "invalid" and s.doc is None diff --git a/tests/test_registry.py b/tests/test_registry.py new file mode 100644 index 0000000..e9ed759 --- /dev/null +++ b/tests/test_registry.py @@ -0,0 +1,103 @@ +"""registry.yaml: schema, body-map resolution, disjoint scopes.""" + +import pytest +import yaml +from conftest import LENS_ROW, SECOND_ROW + +from insight import ORGAN_ROOT, registry + + +def _doc(*rows): + return {"schema": 1, "instances": [dict(r) for r in rows]} + + +def test_the_committed_registry_is_valid_against_a_body_map(body_map): + data = yaml.safe_load((ORGAN_ROOT / "registry.yaml").read_text()) + body_map.update({"PyAutoArray": {"github": "PyAutoLabs/PyAutoArray"}}) + assert registry.validate(data, body_map) == [] + assert [r["instance"] for r in data["instances"]] == ["lens"] + + +def test_load_resolves_path_and_github_from_the_body_map(registry_file, fake_mind): + (inst,) = registry.load(registry_file, mind=fake_mind) + assert inst.github == "PyAutoLabs/autolens_inference" + assert inst.path == "lens/autolens_inference" + assert inst.schema_name == "inference-summary" and inst.schema_version == 1 + assert inst.blob_url("f" * 40, "results/x.json").endswith( + "/blob/" + "f" * 40 + "/results/x.json" + ) + + +def test_a_registry_row_may_not_carry_its_own_path_or_github(body_map): + row = dict(LENS_ROW, path="lens/autolens_inference", github="PyAutoLabs/autolens_inference") + problems = registry.validate(_doc(row), body_map) + assert any("unknown field(s) github, path" in p for p in problems) + + +def test_repo_must_be_a_body_map_identity(body_map): + problems = registry.validate(_doc(dict(LENS_ROW, repo="nowhere_inference")), body_map) + assert any("not in the body map" in p for p in problems) + problems = registry.validate(_doc(dict(LENS_ROW, library_refs=["PyAutoNope"])), body_map) + assert any("library_refs not in the body map: PyAutoNope" in p for p in problems) + + +def test_the_same_summary_cannot_be_registered_twice(body_map): + twin = dict(LENS_ROW, instance="lens2") + problems = registry.validate(_doc(LENS_ROW, twin), body_map) + assert any("already registered as 'lens'" in p for p in problems) + assert registry.validate(_doc(LENS_ROW, SECOND_ROW), body_map) == [] + + +@pytest.mark.parametrize( + "field, value, needle", + [ + ("summary_path", "../outside.json", "summary_path"), + ("summary_path", "/abs/summary.json", "summary_path"), + ("supported_schema", "inference-summary", "supported_schema"), + ("dashboard_url", "http://insecure.invalid/", "dashboard_url"), + ("instance", "Lens", "instance must match"), + ], +) +def test_malformed_rows_are_refused(body_map, field, value, needle): + problems = registry.validate(_doc(dict(LENS_ROW, **{field: value})), body_map) + assert any(needle in p for p in problems), problems + + +def test_explicit_mind_wins_over_the_environment(tmp_path, fake_mind, monkeypatch): + elsewhere = tmp_path / "other" / "PyAutoMind" + elsewhere.mkdir(parents=True) + (elsewhere / "repos.yaml").write_text("repos: {}\n") + monkeypatch.setenv("PYAUTO_MIND", str(elsewhere)) + assert registry.body_map_path(fake_mind) == fake_mind / "repos.yaml" + assert registry.body_map_path() == elsewhere / "repos.yaml" + + +def test_pyauto_root_grouped_and_flat_layouts_resolve(tmp_path, monkeypatch): + monkeypatch.setattr(registry, "ORGAN_ROOT", tmp_path / "x" / "y" / "PyAutoInsight") + for layout in ("organs/PyAutoMind", "PyAutoMind"): + root = tmp_path / layout.replace("/", "_") + (root / layout).mkdir(parents=True) + (root / layout / "repos.yaml").write_text("repos: {}\n") + monkeypatch.setenv("PYAUTO_ROOT", str(root)) + assert registry.body_map_path() == root / layout / "repos.yaml" + + +def test_no_body_map_is_an_error_not_a_guess(tmp_path, monkeypatch): + monkeypatch.setattr(registry, "ORGAN_ROOT", tmp_path / "x" / "y" / "PyAutoInsight") + with pytest.raises(registry.RegistryError, match="cannot find PyAutoMind/repos.yaml"): + registry.body_map_path() + with pytest.raises(registry.RegistryError, match="no repos.yaml there"): + registry.body_map_path(tmp_path) + + +def test_get_names_the_known_instances(registry_file, fake_mind): + instances = registry.load(registry_file, mind=fake_mind) + assert registry.get(instances, "lens").repo == "autolens_inference" + with pytest.raises(registry.RegistryError, match="known: lens"): + registry.get(instances, "nope") + + +def test_evidence_urls_quote_spaces_and_parentheses(registry_file, fake_mind): + inst = registry.load(registry_file, mind=fake_mind)[0] + result = inst.blob_url("a" * 40, "results/a (1).json") + assert result.endswith("/results/a%20%281%29.json") diff --git a/tests/test_summary.py b/tests/test_summary.py new file mode 100644 index 0000000..c80e7ca --- /dev/null +++ b/tests/test_summary.py @@ -0,0 +1,174 @@ +"""Scientific coverage survives ingestion without inventing acceptance.""" + +from copy import deepcopy + +import pytest +from conftest import fixture_doc + +from insight import summary + + +def test_current_project_fixture_is_valid_and_completion_is_not_acceptance(): + d = fixture_doc("lens_summary_v1.json") + assert summary.classify(d) == ("ok", []) + assert d["records"][0]["execution"]["status"] == "complete" + assert summary.qualified_records(d) == 0 + + +def test_empty_feed_is_valid_coverage_not_success(): + d = fixture_doc("valid_empty.json") + assert summary.classify(d) == ("ok", []) + assert summary.qualified_records(d) == 0 + + +@pytest.mark.parametrize("value", [float("nan"), float("inf"), -float("inf")]) +def test_nonfinite_diagnostics_are_rejected(value): + d = fixture_doc("lens_summary_v1.json") + d["records"][0]["diagnostics"]["values"]["ess"] = value + assert summary.classify(d)[0] == "invalid" + + +@pytest.mark.parametrize( + "path", + [ + "../secret", + "/etc/passwd", + "https://bad/a", + "x/%2e%2e/a", + "x\\secret", + "x?query", + "x#anchor", + "x//y", + "x/./y", + ], +) +def test_unsafe_result_and_archive_paths_are_rejected(path): + d = fixture_doc("lens_summary_v1.json") + d["records"][0]["evidence_paths"] = [path] + assert summary.classify(d)[0] == "invalid" + d = fixture_doc("lens_summary_v1.json") + d["records"][0]["samples"]["path"] = path + assert summary.classify(d)[0] == "invalid" + + +@pytest.mark.parametrize( + "field,value", + [ + ("generated_at", "not-a-date"), + ("evidence_updated_at", "2026-10-03"), + ("producer_revision", "short"), + ], +) +def test_dates_and_provenance_are_validated(field, value): + d = fixture_doc("lens_summary_v1.json") + d[field] = value + assert summary.classify(d)[0] == "invalid" + + +def test_unknown_provenance_requires_explanation_not_fabrication(): + d = fixture_doc("lens_summary_v1.json") + d["producer_revision"] = None + d.pop("producer_revision_reason", None) + assert summary.classify(d)[0] == "invalid" + d["producer_revision_reason"] = "Not recorded by legacy producer" + assert summary.classify(d)[0] == "ok" + + +def test_failed_seed_and_partial_pipeline_and_missing_archive_remain_visible(): + d = fixture_doc("lens_summary_v1.json") + parent = d["records"][0] + parent.update(pipeline="five-stage", execution={"status": "incomplete", "completed": False}) + stage = deepcopy(parent) + stage.update(id="failed-stage", parent_run_id=parent["id"], stage="source_pix[1]", seed=1) + stage["execution"]["status"] = "failed" + stage["diagnostics"] = { + "status": "missing", + "values": {}, + "reason": "Job failed before diagnostics", + } + stage["samples"] = { + "availability": "missing", + "path": None, + "reason": "Job failed before archive", + } + d["records"].append(stage) + assert summary.classify(d) == ("ok", []) + assert len(d["records"]) == 2 and summary.qualified_records(d) == 0 + stage["parent_run_id"] = "absent-parent" + assert summary.classify(d)[0] == "invalid" + + +@pytest.mark.parametrize( + "field,value", + [ + ("target", "changed-target"), + ("seed", 42), + ("dataset", {"id": "changed"}), + ("model", "changed-model"), + ("stage", "different-stage"), + ], +) +def test_changed_scientific_identity_refuses_comparison(field, value): + d = fixture_doc("lens_summary_v1.json") + a = d["records"][0] + b = deepcopy(a) + b["id"] = "second" + b[field] = value + d["records"].append(b) + c = {"id": "pair", "records": [a["id"], b["id"]], "protocol": "fixed target parity"} + d["comparisons"] = [c] + assert summary.classify(d) == ("ok", []) + assert any(field in reason for reason in summary.comparison_refusals(d, c)) + + +def test_comparison_requires_real_endpoints_and_protocol(): + d = fixture_doc("lens_summary_v1.json") + d["comparisons"] = [{"id": "pair", "records": ["missing", "also-missing"], "protocol": ""}] + assert summary.classify(d)[0] == "invalid" + + +@pytest.mark.parametrize("value", [None, 123]) +def test_exclusions_bad_type_returns_validation_error(value): + d = fixture_doc("lens_summary_v1.json") + d["coverage"]["excluded"] = value + assert summary.classify(d)[0] == "invalid" + + +def test_producer_exclusion_path_shape_and_path_safety(): + d = fixture_doc("lens_summary_v1.json") + d["coverage"]["excluded"] = [ + {"path": "results/broken.json", "reason": "Invalid source", "status": "invalid"} + ] + assert summary.classify(d) == ("ok", []) + d["coverage"]["excluded"][0]["path"] = "../outside.json" + assert summary.classify(d)[0] == "invalid" + + +@pytest.mark.parametrize("field,value", [("target", {})]) +def test_record_target_and_optional_source_commit(field, value): + d = fixture_doc("lens_summary_v1.json") + d["records"][0][field] = value + assert summary.classify(d)[0] == "invalid" + + +def test_completed_flag_is_boolean_when_present(): + d = fixture_doc("lens_summary_v1.json") + d["records"][0]["execution"]["completed"] = "yes" + assert summary.classify(d)[0] == "invalid" + + +def test_unknown_dataset_refuses_comparison_even_when_both_unknown(): + d = fixture_doc("lens_summary_v1.json") + a = d["records"][0] + a["dataset"]["id"] = None + b = deepcopy(a) + b["id"] = "second" + d["records"].append(b) + c = {"id": "pair", "records": [a["id"], b["id"]], "protocol": "parity"} + assert any("dataset" in r for r in summary.comparison_refusals(d, c)) + + +def test_optional_envelope_source_commit_is_full_sha(): + d = fixture_doc("lens_summary_v1.json") + d["source_commit"] = "short" + assert summary.classify(d)[0] == "invalid"