README.md

metadata

title: Mlflow Server
emoji: 🏃
colorFrom: gray
colorTo: pink
sdk: docker
pinned: false
license: apache-2.0
short_description: A sample MLFlow server for demo

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

🚀 MLflow Tracking Server Configuration

This Space hosts a remote MLflow Tracking Server. It allows you to log parameters, metrics, and models from your training pipelines (like GitHub Actions) to a centralized location.

🛠 Environment Variables (Secrets)

To make this server functional, you need to go to Settings > Variables and secrets in your Hugging Face Space and add the following secrets.

Variable Name	Description	Example Value
BACKEND_STORE_URI	The database URI where metrics and parameters are stored.	postgresql://user:password@host:port/db or sqlite:///mlflow.db
ARTIFACT_STORE_URI	The remote storage location for model artifacts (S3, GCS, etc.).	s3://my-mlflow-bucket/artifacts
AWS_ACCESS_KEY_ID	Required if your artifact store is on S3.	AKIA...
AWS_SECRET_ACCESS_KEY	Required if your artifact store is on S3.	wJalr...
PORT	The port the application listens on (HF Spaces defaults to 7860).	7860

Note: The AWS_ credentials are required because the Dockerfile installs the AWS CLI to handle interactions with S3 buckets.

🐳 Dockerfile Overview

The Dockerfile is built on top of continuumio/miniconda3 to ensure a robust Python environment. Here is what happens during the build:

1. Tool Installation: Installs utility tools (nano, unzip, curl) and the AWS CLI (via the official installer) to allow MLflow to communicate with S3 buckets.
2. Dependencies: Installs Python packages listed in requirements.txt (typically mlflow, boto3, psycopg2, etc.).
3. Startup Command: The container launches the MLflow server with specific flags to ensure it works in a cloud environment.

Understanding the Launch Parameters

The CMD in the Dockerfile uses the following flags:

• --host 0.0.0.0: Binds the server to all network interfaces so it is accessible from outside the container.
• --serve-artifacts: Enables the MLflow server to act as a proxy for artifact downloads/uploads (useful if the client doesn't have direct S3 access).
• --allowed-hosts '*': Disables the Host Header validation check. This is necessary in cloud environments (like HF Spaces) where the internal IP and public DNS might mismatch.
• --cors-allowed-origins '*': Sets the Cross-Origin Resource Sharing policy to allow any domain to access this API. This is useful for teaching environments but can be restricted for production.