If you want to boot up mlflow project with one-liner - this repo is for you.

The only requirement is docker installed on your system and we are going to use Bash on linux/windows.

• Setup by one file (.env)
• Production-ready docker volumes
• Separate artifacts and data containers
• Artifacts GUI
• Ready bash scripts to copy and paste for colleagues to use your server!

1. Configure .env file for your choice. You can put there anything you like, it will be used to configure you services

2. Run the Infrastructure by this one line:

$ docker-compose up -d
Creating network "mlflow-basis_A" with driver "bridge"
Creating mlflow_db      ... done
Creating tracker_mlflow ... done
Creating aws-s3         ... done

3. Create mlflow bucket. You can use my bundled script.

Just run

bash ./run_create_bucket.sh

You can also do it either using AWS CLI or Python Api.

aws configure

aws --endpoint-url=http://localhost:9000 s3 mb s3://mlflow

pip install Minio

from minio import Minio
from minio.error import ResponseError

s3Client = Minio(
    'localhost:9000',
    access_key='<YOUR_AWS_ACCESSS_ID>', # copy from .env file
    secret_key='<YOUR_AWS_SECRET_ACCESS_KEY>', # copy from .env file
    secure=False
)
s3Client.make_bucket('mlflow')

4. Open up http://localhost:5000 for MlFlow, and http://localhost:9000/minio/mlflow/ for S3 bucket (you artifacts) with credentials from .env file

5. Configure your client-side

For running mlflow files you need various environment variables set on the client side. To generate them user the convienience script ./bashrc_install.sh, which installs it on your system or ./bashrc_generate.sh, which just displays the config to copy & paste.

$ ./bashrc_install.sh
[ OK ] Successfully installed environment variables into your .bashrc!

The script installs this variables: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, MLFLOW_S3_ENDPOINT_URL, MLFLOW_TRACKING_URI. All of them are needed to use mlflow from the client-side.

6. Test the pipeline with below command with conda. If you dont have conda installed run with --no-conda

mlflow run [email protected]:databricks/mlflow-example.git -P alpha=0.5
# or
python ./quickstart/mlflow_tracking.py

7. (Optional) If you are constantly switching your environment you can use this environment variable syntax

MLFLOW_S3_ENDPOINT_URL=http://localhost:9000 MLFLOW_TRACKING_URI=http://localhost:5000 mlflow run [email protected]:databricks/mlflow-example.git -P alpha=0.5