MLflow Docker container

Docker Hub image

This is a simple container for MLflow. It is based on the official MLflow container and adds extended package management and functionality:

• Additional Python packages through the requirements.txt.
• Packages to connect to PostgreSQL to store tracking data.
• Packages to connect to S3 storage to store tracking artifacts.

MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc.), wherever you currently run ML code (e.g., in notebooks, standalone applications, or the cloud). © MLflow

Usage

Run MLflow

docker run --rm \
       -p 5000:5000 \
       --env MLFLOW_HOST=0.0.0.0 \
       --env MLFLOW_PORT=5000 \
       --env-file MLFLOW_CONNECTION_SECRETS.env \
       mdernovoi/mlflow

• MLflow listens on all addresses (0.0.0.0).
• MLflow is published on the host machine on port 5000.
• The MLFLOW_CONNECTION_SECRETS.env environment file contains configuration secrets for storage access (database passwords, etc.). An example is provided in the repository.

Improvements and Suggestions

Please suggest any improvements or express wishes in the Issues section. A pull request with a corresponding implementation or fix is not required but would be appreciated.