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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 Running this Example
First, install MLflow (via Note that the name if the image used in the Finally, run the example project using Running Environment variables, such as pip install mlflow) and install
docker build -t mlflow-docker-example -f Dockerfile .
docker build command, mlflow-docker-example,
matches the name of the image referenced in the MLProject file.mlflow run examples/docker -P alpha=0.5.
What happens when the project is run?
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.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.
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