Non-official docker image for MLflow

MLflow documentation

Build

git clone https://github.com/ex00/docker-mlflow.git
cd docker-mlflow
docker build -t ex00/docker-mlflow .

Usage

docker run -d -p 5000:5000 -p 6233:6233 --name mlflow-container ex00/docker-mlflow

after first build you can use next command:

docker start mlflow-container

Run examples

docker exec -i mlflow-container python /examples/example.py

You should get same output in console

Running example.py
Train model
Score of LogisticRegression: 0.6666666666666666
Model saved in run 92568a4725634a6abbca71bf1ef3fdfc
Success

Also you can check results in mlflow ui - http://$(docker-machine ip):5000

run and check model:

docker exec -i mlflow-container mlflow sklearn serve --port 6233 --host 0.0.0.0 -r 92568a4725634a6abbca71bf1ef3fdfc model
curl -d '[{"x": 1}, {"x": -1}]' -H 'Content-Type: application/json' -X POST $(docker-machine ip):6233/invocations

result:

{
    "predictions": [
        1,
        0
    ]
}

Run example project in docker

docker exec -i mlflow-container mlflow run https://github.com/mlflow/mlflow-example.git -P alpha=0.4

Example log local model in docker mlflow

python examples/test_connect_to_docker.py http://$(docker-machine ip):5000