This directory contains an MLflow project that trains a linear regression model on the UC Irvine Wine Quality Dataset. The project uses a Docker image to capture the dependencies needed to run training code. Running a project in a Docker environment (as opposed to Conda) allows for capturing non-Python dependencies, e.g. Java libraries. In the future, we also hope to add tools to MLflow for running Dockerized projects e.g. on a Kubernetes cluster for scale out.

This MLflow project contains a train.py file that trains a scikit-learn model and uses MLflow Tracking APIs to log the model and its metadata (e.g., hyperparameters and metrics) for later use and reference. train.py operates on the Wine Quality Dataset, which is included in wine-quality.csv.

Most importantly, the project also includes an MLproject file, which specifies the Docker container environment in which to run the project using the docker_env field:

docker_env:
  image:  mlflow-docker-example

Here, image can be any valid argument to docker run, such as the tag, ID or URL of a Docker image (see Docker docs). The above example references a locally-stored image (mlflow-docker-example) by tag.

Finally, the project includes a Dockerfile that is used to build the image referenced by the MLproject file. The Dockerfile specifies library dependencies required by the project, such as mlflow and scikit-learn.

First, install MLflow (via pip install mlflow) and install Docker.

Then, build the image for the project's Docker container environment. You must use the same image name that is given by the docker_env.image field of the MLproject file. In this example, the image name is mlflow-docker-example. Issue the following command to build an image with this name:

docker build -t mlflow-docker-example -f Dockerfile .

Note that the name if the image used in the docker build command, mlflow-docker-example, matches the name of the image referenced in the MLproject file.

Finally, run the example project using mlflow run examples/docker -P alpha=0.5.

Running mlflow run examples/docker builds a new Docker image based on mlflow-docker-example that also contains our project code. The resulting image is tagged as mlflow-docker-example-<git-version> where <git-version> is the git commit ID. After the image is built, MLflow executes the default (main) project entry point within the container using docker run.

Environment variables, such as MLFLOW_TRACKING_URI, are propagated inside the container during project execution. When running against a local tracking URI, MLflow mounts the host system's tracking directory (e.g., a local mlruns directory) inside the container so that metrics and params logged during project execution are accessible afterwards.