MLflow On-Premise Deployment using Docker Compose

Easily deploy an MLflow tracking server with 1 command.

The main difference between this fork and the original is that the Mlflow tracking server proxies the requests to the artifact which were improperly handled with the original code base.

Additionally, I have replaced minio with S3 for the sake of convenience. The previous improvement (proper proxying) can be layered on top of minio if needed.

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