Common Metadata Framework (CMF)

Common Metadata Framework (CMF) is a metadata tracking and versioning system for ML pipelines. It tracks code, data, and pipeline metrics—offering Git-like metadata management across distributed environments.

🚀 Features

• ✅ Track artifacts (datasets, models, metrics) using content-based hashes
• ✅ Automatically logs code versions (Git) and data versions (DVC)
• ✅ Push/pull metadata via CLI across distributed sites
• ✅ REST API for direct server interaction
• ✅ Implicit & explicit tracking of pipeline execution
• ✅ Fine-grained or coarse-grained metric logging

🏛 Quick Start

Get started with CMF in minutes using our example ML pipeline:

📖 Try the Getting Started Example

This example demonstrates:

• Initializing a CMF project
• Tracking an ML pipeline with multiple stages (parse → featurize → train → test)
• Versioning datasets and models
• Pushing artifacts and metadata
• Querying tracked metadata

📦 Installation

Requirements

• Linux/Ubuntu/Debian
• Python: Version 3.9 to 3.11 (3.10 recommended)
• Git (latest)

Virtual Environment

conda create -n cmf python=3.10
conda activate cmf

virtualenv --python=3.10 .cmf
source .cmf/bin/activate

Install CMF

pip install git+https://github.com/HewlettPackard/cmf

pip install cmflib

Server Setup

📖 Follow the CMF Server Installation Guide

📘 Documentation

• Getting Started
• API Reference
• Command Reference
• Related Docs

🧠 How It Works

CMF tracks pipeline stages, inputs/outputs, metrics, and code. It supports decentralized execution across datacenters, edge, and cloud.

• Artifacts are versioned using DVC (.dvc files).
• Code is tracked with Git.
• Metadata is logged to relational DB (e.g., SQLite, PostgreSQL)
• Sync metadata with cmf metadata push and cmf metadata pull.

🏛 Architecture

CMF is composed of:

• cmflib - Metadata library provides API to log/query metadata
• CMF Client – CLI to sync metadata with server, push/pull artifacts to the user-specified repo, push/pull code from Git
• CMF Server – REST API for metadata merge
• Central Repositories – Git (code), DVC (artifacts), CMF (metadata)

🔧 Sample Usage

from cmflib.cmf import Cmf
from ml_metadata.proto import metadata_store_pb2 as mlpb

metawriter = Cmf(filepath="mlmd", pipeline_name="test_pipeline")

context: mlpb.Context = metawriter.create_context(
    pipeline_stage="prepare",
    custom_properties={"user-metadata1": "metadata_value"}
)

execution: mlpb.Execution = metawriter.create_execution(
    execution_type="Prepare",
    custom_properties={"split": split, "seed": seed}
)

artifact: mlpb.Artifact = metawriter.log_dataset(
    "artifacts/data.xml.gz", "input",
    custom_properties={"user-metadata1": "metadata_value"}
)

cmf                          # CLI to manage metadata and artifacts
cmf init                     # Initialize artifact repository
cmf init show                # Show current CMF config
cmf metadata push            # Push metadata to server
cmf metadata pull            # Pull metadata from server

➡️ For the complete list of commands, please refer to the Command Reference

✅ Benefits

• Full ML pipeline observability
• Unified metadata, artifact, and code tracking
• Scalable metadata syncing
• Team collaboration on metadata

🎤 Talks & Publications

• 🎙 Monterey Data Conference 2022

🌐 Related Projects

• 📚 Common Metadata Ontology
• 🧠 AI Metadata Knowledge Graph (AIMKG)

🤝 Community

• 💬 Join CMF on Slack
• 📧 Contact: annmary.roy@hpe.com

📄 License

Licensed under the Apache 2.0 License

© Hewlett Packard Enterprise. Built for reproducibility in ML.