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MLflow Artifacts Example

This directory contains a set of files for demonstrating the MLflow Artifacts Service.

What does the MLflow Artifacts Service do?

The MLflow Artifacts Service serves as a proxy between the client and artifact storage (e.g. S3) and allows the client to upload, download, and list artifacts via REST API without configuring a set of credentials required to access resources in the artifact storage (e.g. AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY for S3).

Quick start

First, launch the tracking server with the artifacts service via mlflow server:

# Launch a tracking server with the artifacts service
$ mlflow server \
    --serve-artifacts \
    --artifacts-destination ./mlartifacts \
    --default-artifact-root http://localhost:5000/api/2.0/mlflow-artifacts/artifacts/experiments \
    --gunicorn-opts "--log-level debug"

Notes:

  • --serve-artifacts enables the MLflow Artifacts service endpoints to enable proxied serving of artifacts through the REST API
  • --artifacts-destination specifies the base artifact location from which to resolve artifact upload/download/list requests. In this examples, we're using a local directory ./mlartifacts, but it can be changed to a s3 bucket or
  • --default-artifact-root points to the experiments directory of the artifacts service. Therefore, the default artifact location of a newly-created experiment is set to ./mlartifacts/experiments/<experiment_id>.
  • --gunicorn-opts "--log-level debug" is specified to print out request logs but can be omitted if unnecessary.
  • --artifacts-only disables all other endpoints for the tracking server apart from those involved in listing, uploading, and downloading artifacts. This makes the MLflow server a single-purpose proxy for artifact handling only.

Then, run example.py that performs upload, download, and list operations for artifacts:

$ MLFLOW_TRACKING_URI=http://localhost:5000 python example.py

After running the command above, the server should print out request logs for artifact operations:

...
[2021-11-05 19:13:34 +0900] [92800] [DEBUG] POST /api/2.0/mlflow/runs/create
[2021-11-05 19:13:34 +0900] [92800] [DEBUG] GET /api/2.0/mlflow/runs/get
[2021-11-05 19:13:34 +0900] [92802] [DEBUG] PUT /api/2.0/mlflow-artifacts/artifacts/0/a1b2c3d4/artifacts/a.txt
[2021-11-05 19:13:34 +0900] [92802] [DEBUG] PUT /api/2.0/mlflow-artifacts/artifacts/0/a1b2c3d4/artifacts/dir/b.txt
[2021-11-05 19:13:34 +0900] [92802] [DEBUG] POST /api/2.0/mlflow/runs/update
[2021-11-05 19:13:34 +0900] [92802] [DEBUG] GET /api/2.0/mlflow-artifacts/artifacts
...

The contents of the mlartifacts directory should look like this:

$ tree mlartifacts
mlartifacts
└── experiments
    └── 0  # experiment ID
        └── a1b2c3d4  # run ID
            └── artifacts
                ├── a.txt
                └── dir
                    └── b.txt

5 directories, 2 files

Clean up

# Remove experiment and run data
$ rm -rf mlruns

# Remove artifacts
$ rm -rf mlartifacts

Advanced example using docker-compose

docker-compose.yml provides a more advanced setup than the quick-start example above:

  • Tracking service uses PostgreSQL as a backend store.
  • Artifact service uses MinIO as a artifact store.
  • Tracking and artifacts services are running on different servers.
# Build services
$ docker-compose build

# Launch tracking and artifacts servers in the background
$ docker-compose up -d

# Run `example.py` in the client container
$ docker-compose run -v ${PWD}/example.py:/app/example.py client python example.py

You can view the logged artifacts on MinIO Console served at http://localhost:9001. The login username and password are user and password.

Clean up

# Remove containers, networks, volumes, and images
$ docker-compose down --rmi all --volumes --remove-orphans

Development

# Build services using the dev version of mlflow
$ ./build.sh
$ docker-compose run -v ${PWD}/example.py:/app/example.py client python example.py