JupyterLab MLflow Extension

A JupyterLab extension for browsing MLflow experiments, runs, models, and artifacts directly from the JupyterLab sidebar.

Features

• Browse MLflow experiments, runs, models, and artifacts
• Tree view for hierarchical navigation
• Details/Object view for exploring metadata and child objects
• View artifacts in new JupyterLab tabs
• Copy experiment/run/model IDs to clipboard
• Generate and insert MLflow Python API code snippets
• Connect to remote MLflow tracking servers
• Launch local MLflow server with SQLite backend
• Settings UI with environment variable fallback
• MLflow shortcuts panel for common operations

Requirements

• JupyterLab >= 4.0.0
• Python >= 3.8
• MLflow >= 2.0.0

Installation

pip install jupyterlab-mlflow

Or install from source:

git clone https://github.com/BioLM/jupyterlab-mlflow.git
cd jupyterlab-mlflow
pip install -e .
jlpm install
jlpm build

Configuration

The extension can be configured via:

1. Settings UI: Open JupyterLab Settings → Advanced Settings Editor → MLflow
2. Environment Variable: Set MLFLOW_TRACKING_URI environment variable

Custom Request Headers (Authentication)

For MLflow servers that require authentication or custom headers, you can provide a custom RequestHeaderProvider via the MLFLOW_TRACKING_REQUEST_HEADER_PROVIDER environment variable.

Example: Custom Authentication Provider

1. Create a Python module with your custom provider:

# my_auth_provider.py
from mlflow.tracking.request_header.abstract_request_header_provider import RequestHeaderProvider

class MyAuthRequestHeaderProvider(RequestHeaderProvider):
    def in_context(self):
        """Return True to always provide headers"""
        return True
    
    def request_headers(self):
        """Return custom headers for MLflow API requests"""
        import os
        token = os.environ.get("MY_AUTH_TOKEN", "")
        return {
            "Authorization": f"Bearer {token}",
            "X-Custom-Header": "value"
        }

2. Set the environment variable with the full class path:

export MLFLOW_TRACKING_REQUEST_HEADER_PROVIDER="my_auth_provider.MyAuthRequestHeaderProvider"
export MY_AUTH_TOKEN="your-secret-token"

The provider will be automatically imported and registered when the extension creates MLflow clients. Make sure the module containing your provider is in your Python path.

Note: If the provider cannot be loaded or registered, the extension will log a warning but continue to function without custom headers.

Server Extension

The extension includes a server-side component that must be enabled. After installation, enable it with:

jupyter server extension enable jupyterlab_mlflow.serverextension

Or enable it system-wide:

jupyter server extension enable jupyterlab_mlflow.serverextension --sys-prefix

Verify it's enabled:

jupyter server extension list

You should see jupyterlab_mlflow.serverextension in the enabled extensions list.

Note: In some JupyterLab deployments (especially managed environments), the server extension may need to be enabled by an administrator or configured in the deployment settings.

Troubleshooting

If you're experiencing 404 errors when using the extension:

1. Run the diagnostic script:

   python scripts/diagnose_extension.py
   

   This will check:

   • Package installation
   • Entry point discovery
   • Configuration files
   • Extension status
   • Handler registration

2. Check if the extension is enabled:

   jupyter server extension list | grep mlflow
   

   If it's not listed or not enabled, enable it:

   jupyter server extension enable jupyterlab_mlflow.serverextension
   

3. Verify the health endpoint: After starting JupyterLab, try accessing:

   http://your-jupyterlab-url/mlflow/api/health
   

   If this returns {"status": "ok", ...}, the extension is loaded correctly.

4. Check server logs: Look for messages like:

   ✅ Registered jupyterlab-mlflow server extension
   ✅ Registered 11 API handlers with base_url: /jupyter/
   

5. For managed deployments:

   • Ensure the package is installed in the correct Python environment
   • Check that config files are present in /etc/jupyter/ or the deployment's config directory
   • Verify that entry points are discoverable (the diagnostic script checks this)
   • Some managed environments require explicit enablement even with config files

Usage

1. Configure your MLflow tracking URI in the settings or via environment variable
2. The MLflow sidebar will appear in the left sidebar
3. Browse experiments, runs, models, and artifacts
4. Click on artifacts to view them in new tabs
5. Right-click on items to copy IDs to clipboard

Development

Quick Local Testing

To test the extension locally without publishing to PyPI:

# Option 1: Use the test script (recommended)
./test_server_extension.sh

# Option 2: Manual steps
pip install -e .
npm run build:lib
python -m jupyter labextension build . --dev
jupyter server extension enable jupyterlab_mlflow.serverextension
jupyter lab

Testing API Endpoints

After starting JupyterLab, test the server extension API endpoints:

# In another terminal, test the endpoints
./test_api_endpoints.sh http://localhost:8888 http://localhost:5000

Or manually test with curl:

# Test connection endpoint
curl "http://localhost:8888/mlflow/api/connection/test?tracking_uri=http://localhost:5000"

# Test local server status
curl "http://localhost:8888/mlflow/api/local-server"

Development Workflow

# Install dependencies
jlpm install

# Build the extension
jlpm build

# Watch for changes
jlpm watch

# Run tests
pytest

Publishing

This package uses automatic version bumping and is published to PyPI when a new release is created on GitHub.

Automatic Version Bumping

Version bumping is handled automatically by semantic-release based on commit messages:

• feat: something → minor version bump (0.1.0 → 0.2.0)
• fix: something → patch version bump (0.1.0 → 0.1.1)
• BREAKING: something → major version bump (0.1.0 → 1.0.0)

When you push to main, semantic-release will:

1. Analyze commits since last release
2. Bump version in package.json (if needed)
3. Create a git tag
4. Push the tag to GitHub

Publishing to PyPI

1. Create a GitHub Release:

   • Go to: https://github.com/BioLM/jupyterlab-mlflow/releases/new
   • Select the tag created by semantic-release (e.g., v0.2.0)
   • Add release notes
   • Click "Publish release"

2. Automatic Publishing:

   • The publish workflow automatically builds and publishes to PyPI
   • No manual steps required after creating the release

See PUBLISHING.md for detailed instructions.

License

BSD-3-Clause