Setup MLFlow

[aashish24]

• Install the mlflow docker containers within the system - https://mlflow.org/docs/latest/docker.html
• Get familiar with the interface and loading data for training. This will help for domain specific vocab when converting to using the REST endpoints -
• Get data from Jason for Training and understand pre-processing required of the WAV files for training (conversion to spectrograms)
• Map out using the REST API for mlflow for how we could modify and transfer data from the batai celery/django container and send it to mlflow for training
• Understand how to get the resulting model data out of mlflow and store it locally to the celery container for use in the future. This may also require creating a Django Model for model trained versions.
• Investigate getting additional artifacts out of Mlflow, for example visualizations such as confusion matrices that could be viewed through the BatAI interface
• Create a celery task that trains some model as proof-of-concept for integration with Mlflow. It should retrieve data from the database, do some training, and log the run in Mlflow with performance metrics and a manifest file

[BryonLewis]

add a basic query:
SpeciesId is from the species we utilize.
Takes the email from the JWT token for pushing value.
It has an assumed manual species classifier list.

Query VetQuery {
updateAcousticFileVet(
surveyEventId: 4481017
acousticFileId: 174988990
softwareId: 81
speciesId: 29
) {
acousticFileBatchId
}
}

query PresignedUrlQuery {
presignedUrlFromAcousticFile(acousticFileId: "174988990")
{
s3PresignedUrl
}
}