crmne/mlflow-tracking repository overview

⁠MLFlow Tracking Server

This repository neatly packages the MLFLow Tracking Server in a Docker container.

⁠Ports

MLFlow Tracking Server runs by default on port 5000. To port forward to localhost:5000:

docker run crmne/mlflow-tracking -p 5000:5000

⁠Volumes

It is recommended that you mount /mlruns and /mlartifacts to persistent storage, e.g.:

docker run crmne/mlflow-tracking -p 5000:5000 -v /mnt/mlflow/mlruns:/mlruns -v /mnt/mlflow/mlartifacts:/mlartifacts

⁠Runs and Artifacts

By default, this container will save the runs in /mlruns/mlruns.db and the artifacts in /mlartifacts, but you can change it by appending the --backend-store-uri and --default-artifact-root options respectively for mlflow server to your docker run. This will allow you to log the runs to files or any database supported by SQLAlchemy⁠, and artifacts to many cloud and network storage services⁠. Example:

docker run crmne/mlflow-tracking -p 5000:5000 --backend-store-uri mysql://scott:tiger@localhost/mlflow --default-artifact-root s3://my-mlflow-bucket/

More information at https://mlflow.org/docs/latest/tracking.html#mlflow-tracking-servers⁠

⁠Test your tracking server

test.py contains an example model to test MLFlow Tracking Server.

1. Run MLFlow Tracking Server

    docker run crmne/mlflow-tracking -p 5000:5000 -d
   

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2. Install Pipenv (if you don't have it)

    pip install pipenv
   

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3. Install dependencies

    pipenv install
   

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4. Run example model

    pipenv run python test.py
   

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