yprov-mlflow-plugin 0.2.0

MLflow plugin that adds W3C PROV provenance tracking to every experiment via yProv4ML

yProv4ML MLflow Plugin

An MLflow plugin that integrates provenance tracking with sustainability metrics for ML experiments built on the yProv4ML library.

Report Bug · Request Feature

An MLflow plugin that integrates W3C PROV-compliant provenance tracking with sustainability metrics for machine learning experiments built on the yProv4ML (prov4ml) library.

🚀 Installation

Prerequisites

• Python 3.9 or higher
• pip package manager
• Git (for installing from source)

Installation (Recommended)

pip install -r requirements.txt

# Clone the repository
git clone https://github.com/yourusername/yProv4ML_MLflow_Plugin.git
cd yProv4ML_MLflow_Plugin

# Install the plugin in development mode
pip install .

Verify Installation

# Check that the plugin is registered
python -c "import mlflow; print(mlflow.__version__)"

# Verify prov4ml is installed
python -c "import prov4ml; print('prov4ml installed successfully')"

# Check plugin entry points
python -c "from importlib.metadata import entry_points; eps = [ep for ep in entry_points().get('mlflow.tracking_store', []) if 'yprov' in ep.name]; print(f'Found {len(eps)} yprov entry points')"

# Test basic functionality
python -c "import mlflow; mlflow.set_tracking_uri('yprov+file:///tmp/test'); print('✅ Plugin activated successfully')"

⚡ Quick Start

The plugin activates automatically when you use the yprov+ URI scheme:

import mlflow

# 🔑 CRITICAL: Use yprov+ prefix to activate the plugin
mlflow.set_tracking_uri("yprov+file:///path/to/mlruns")

# Standard MLflow code - no changes needed!
mlflow.set_experiment("my_experiment")

with mlflow.start_run(run_name="example_run"):
    # Log parameters
    mlflow.log_param("learning_rate", 0.001)
    mlflow.log_param("batch_size", 32)
    
    # Log metrics
    for epoch in range(5):
        mlflow.log_metric("loss", 0.5 - epoch * 0.05, step=epoch)
        mlflow.log_metric("accuracy", 0.7 + epoch * 0.05, step=epoch)
    
    # Log artifacts
    mlflow.log_artifact("model.pt", artifact_path="models")

# ✅ PROV JSON automatically generated in data/prov/my_experiment/

🙏 Acknowledgments

• MLflow - Open source ML lifecycle platform
• yProv4ML (prov4ml) - W3C PROV provenance tracking
• CodeCarbon - Carbon emissions tracking
• W3C PROV - Provenance standard

---