MLFlow Docker Setup

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.

🚀 1-2-3! Setup guide

1. Configure .env file for your choice. You can put there anything you like, it will be used to configure you services
2. Run docker compose up
3. Open up http://localhost:5000 for MlFlow, and http://localhost:9001/ to browse your files in S3 artifact store

👇Video tutorial how to set it up + BONUS with Microsoft Azure 👇

Features

• One file setup (.env)
• Minio S3 artifact store with GUI
• MySql mlflow storage
• Ready to use bash scripts for python development!
• Automatically-created s3 buckets

How to use in ML development in python

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1. Configure your client-side

For running mlflow files you need various environment variables set on the client side. To generate them use 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.

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

mlflow run git@github.com:databricks/mlflow-example.git -P alpha=0.5
# or
python ./quickstart/mlflow_tracking.py

3. (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 git@github.com:databricks/mlflow-example.git -P alpha=0.5

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Licensing

Copyright (c) 2021 Tomasz Dłuski

Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License by reviewing the file LICENSE in the repository.