Skip to content
Snippets Groups Projects
Select Git revision
  • master default protected
1 result
Forked from ifs / Example Running ML Flow Locally
3 commits ahead of the upstream repository.
Name Last commit Last update
Docker
Python
.gitignore
README.md
example_plots.py
README.md

Running MLFlow locally

This repo contains code to run MLFlow locally in two configurations.

Docker

This approach to running MLFlow locally builds the MLFlow server with a MySQL DB and S3 Storage with a single docker-compose file. The S3 storage and MySQL DB are persistent. Currently, it runs version 1.30.0 of MLFlow.

The Docker folder contains a docker-compose.yml file. To build and run the containers just run:

    docker-compose up -d --build

This will start three Docker containers

  • MLFlow Server
  • S3 storage -> MinIO Instance
  • MySQL Database
  • mc container which will run the setup for the S3 storage

Python

This approach is based on the newly released version 2.0.0 of MLFlow, which allows running MLFlow directly on the user machine without docker. All the files required for the MLFlow ui will be stored in the file system (mlruns)