Easily deploy an MLflow tracking server with 1 command.

MinIO S3 is used as the artifact store and MySQL server is used as the backend store.

1. Clone (download) this repository

   git clone https://github.com/sachua/mlflow-docker-compose.git

2. cd into the mlflow-docker-compose directory

3. Build and run the containers with docker-compose

   docker-compose up -d --build

4. Access MLflow UI with http://localhost:5000

5. Access MinIO UI with http://localhost:9000

The MLflow tracking server is composed of 4 docker containers:

• MLflow server
• MinIO object storage server
• MySQL database server

1. Install conda

2. Install MLflow with extra dependencies, including scikit-learn

   pip install mlflow boto3

3. Set environmental variables

   export MLFLOW_TRACKING_URI=http://localhost:5000
   export MLFLOW_S3_ENDPOINT_URL=http://localhost:9000

4. Set MinIO credentials

   cat <<EOF > ~/.aws/credentials
   [default]
   aws_access_key_id=minio
   aws_secret_access_key=minio123
   EOF

5. Train a sample MLflow model

   mlflow run https://github.com/sachua/mlflow-example.git -P alpha=0.42

6. Serve the model (replace ${MODEL_ID} with your model's ID)

   export MODEL_ID=0ced24069348417fbbcb2cd41a7d2f07 # Replace this with your model's ID
   mlflow models serve -m runs:/${MODEL_ID}/model -p 1234 --env-manager conda

7. You can check the input with this command

   curl -X POST -H "Content-Type:application/json" --data '{"dataframe_split":{"columns":["fixed acidity", "volatile acidity", "citric acid", "residual sugar", "chlorides", "free sulfur dioxide", "total sulfur dioxide", "density", "pH", "sulphates", "alcohol"],"data":[[6.2, 0.66, 0.48, 1.2, 0.029, 29, 75, 0.98, 3.33, 0.39, 12.8]]}}' http://127.0.0.1:1234/invocations