jupyterlab-mlflow 0.2.2

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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

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.

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