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The MLflow environment serves as a tracking server for managing the machine learning lifecycle. The repo contains several infrastructure services that start in a Docker stack.
The Docker stack uses four infrastructure services. These services can be started manually so that you get an MLflow working environment in Docker.
- JupyterLab (docker-container: jupyter)
- MLflow Tracking Server (docker-container: mlflow)
- Artifact Store as SFTP Server (docker-container: sftp)
- Backend Storage as postgres db (docker-container: postgres)
- Start MLflow workspace:
sh start_docker_stack.sh - Visit http://127.0.0.1:5001 for MLflow UI
- Visit http://127.0.0.1:8888 for JupyterLab UI (enter the password
mlflow) - Stop MLflow workspace:
sh stop_docker_stack.sh
- Visit the JupyterLab (http://127.0.0.1:8888) and execute the notebook mlflow_example.ipynb. If no error appears, then your MLflow workspace is set up correctly.
- Add new dependencies to file docker/jupyter/requirements.txt
- Please open the .env file and change the variable
SFTP_IMAGEtotinztwins/sftp-server:arm64.
- On Linux systems there may be permission problems with the Artifact Store. The current workaround is to set the permissions manually (sudo chmod -R 777).
- SFTP-Server: Cyberduck
- Deploy Your Own MLflow Workspace On-Premise with Docker in Towards Data Science / April 17, 2023
- How to setup an MLflow 2.0 Workspace with Docker? in Dev Genius / Dec 4, 2022