Self-host MLflow with Docker

Track experiments, compare runs, and manage models with a self-hosted MLflow tracking server.

This page describes the Levelrail template for MLflow (AI). It deploys the services below as one Docker Compose app on your own server.

Project site: https://mlflow.org/docs/latest.

Recommended memory: about 1024 MiB.

Services, ports and volumes

Service	Image	Container ports	Volumes
mlflow	ghcr.io/mlflow/mlflow:v3.16.1	5000	mlflow_data -> /mlflow

Environment variables

This template sets no environment variables.

Passwords and keys marked as generated are created for you when the app is deployed and stored as secrets. Values are not shown here.

Deploy MLflow with Levelrail

In the dashboard, open Apps, choose New app, then Browse templates, and select MLflow. Review the Compose body and deploy.

With the CLI:

levelrail-cli templates deploy mlflow --name my-mlflow

See Service template catalog for how templates work, and Getting started if you have not installed Levelrail yet.

More AI templates

• AnythingLLM
• ComfyUI
• Flowise
• Flowise (Postgres + Qdrant)
• Jupyter GPU Notebook
• Label Studio

All templates are listed in the self-host gallery.