MLflow On-Premise Deployment using Docker Compose

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.

How to run

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

Containerization

The MLflow tracking server is composed of 4 docker containers:

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

Example

1. Install conda

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

   pip install mlflow[extras]

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/mlflow/mlflow-example.git -P alpha=0.42

   • Note: To fix ModuleNotFoundError: No module named 'boto3'

     #Switch to the conda env
     conda env list
     conda activate mlflow-3eee9bd7a0713cf80a17bc0a4d659bc9c549efac #replace with your own generated mlflow-environment
     pip install boto3

6. Serve the model (replace with your model's actual path)

   mlflow models serve -m S3://mlflow/0/98bdf6ec158145908af39f86156c347f/artifacts/model -p 1234

7. You can check the input with this command

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