mltrack-py 0.2.0

Universal ML tracking tool for teams

🚀 MLTrack

Drop-in MLflow enhancement with powerful CLI for ML deployment

Features • Quick Start • Documentation • Examples • Contributing

---

🔄 MLflow Compatible

MLTrack is a drop-in enhancement for MLflow, not a replacement. Your existing code keeps working:

# Your existing MLflow code works unchanged
import mlflow
mlflow.start_run()
mlflow.log_param("alpha", 0.5)
mlflow.log_metric("rmse", 0.876)
mlflow.end_run()

# Just add MLTrack for deployment superpowers
from mltrack import get_last_run, deploy
deploy(get_last_run(), platform="modal")

# Deploy the last run from the CLI
mltrack deploy --last --platform modal

---

🎯 Why MLTrack?

Stop experimenting. Start shipping.

MLTrack is a drop-in enhancement for MLflow that focuses on what matters: getting models into production. While MLflow handles experiment tracking beautifully, MLTrack adds the missing pieces for the complete ML lifecycle: Build → Deploy → Monitor.

# Works with your existing MLflow code
import mlflow
from mltrack import track

@track  # Automatic MLflow tracking + deployment readiness
def train_model(learning_rate=0.01, batch_size=32):
    model = train(learning_rate, batch_size)
    return model

# One command to production
mltrack deploy --last --platform modal

# Or from Python
from mltrack import get_last_run, deploy
deploy(get_last_run(), platform="modal")

✨ Features

🏗️ Build: Enhanced MLflow Tracking

• Drop-in replacement for MLflow with zero config changes
• Simple @track decorator adds deployment metadata automatically
• Works with all MLflow features - just better UI and workflows

🚀 Deploy: Production in One Command

• Modal: Serverless GPU deployment with auto-scaling
• AWS Lambda: Cost-effective for lightweight models
• Docker: For Kubernetes, ECS, or any container platform
• Automatic FastAPI endpoints with OpenAPI documentation
• Built-in model versioning and rollback

📊 Monitor: Know What's Happening

• Real-time inference metrics and latency tracking
• Model drift detection and alerts
• Cost analysis (compute + LLM tokens)
• A/B testing and canary deployments built-in

💼 Enterprise Ready

• Works with existing MLflow tracking servers
• Integrates with your current CI/CD pipelines
• Multi-user support with SSO/SAML
• Audit logs and compliance features

🎯 Built for Real ML Teams

• Stop juggling notebooks, scripts, and YAML configs
• Go from experiment to production endpoint in minutes
• Monitor actual business impact, not just model metrics
• Scale from POC to production without rewrites

🎮 Powerful CLI

MLTrack provides a comprehensive CLI that makes ML operations as simple as web development.

Note: You can use either mltrack or the shorter ml command - they're identical!

# Training shortcuts
mltrack train script.py --params learning_rate=0.01 batch_size=32
mltrack train --last  # Re-run last experiment with same params
mltrack train --best  # Re-run best performing experiment

# Deployment commands
mltrack deploy --last --platform modal  # Deploy last trained model
mltrack deploy --best accuracy --platform lambda  # Deploy best model by metric
mltrack deploy --run-id abc123 --platform docker --push-to ecr

# Model management
mltrack models list  # List all registered models
mltrack models promote fraud-detector --from staging --to production
mltrack models rollback fraud-detector  # Instant rollback

# Monitoring and logs
mltrack logs fraud-detector --tail  # Stream production logs
mltrack metrics fraud-detector --window 1h  # Recent performance
mltrack alerts create --model fraud-detector --metric latency --threshold 100ms

# Batch operations
mltrack experiments clean --older-than 30d  # Cleanup old experiments
mltrack deploy-all models.yaml  # Deploy multiple models from config
mltrack benchmark --models v1,v2,v3 --dataset test.csv  # Compare models

# Integration with Unix tools
mltrack list --format json | jq '.[] | select(.metrics.accuracy > 0.9)'
mltrack export --run-id abc123 | aws s3 cp - s3://models/model.pkl

# UI commands
ml ui          # Launch modern MLTrack UI (default port 3000)
ml ui --port 8080  # Custom port
ml flow        # Launch classic MLflow UI (default port 5000)

CLI Highlights

• Intuitive shortcuts: Common workflows in single commands
• Unix-friendly: Pipe-able, scriptable, automation-ready
• Smart defaults: --last, --best flags for quick access
• Batch operations: Handle multiple models/experiments at once
• Real-time monitoring: Stream logs and metrics from production

🚀 Quick Start

Installation

uv add ml-track

Basic Usage

# 1. BUILD - Works with your existing MLflow code
from mltrack import track, get_last_run, deploy
import mlflow

@track  # Enhances MLflow tracking
def train_model(n_estimators=100, max_depth=10):
    # Your normal training code
    model = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth)
    model.fit(X_train, y_train)
    
    # Log metrics as usual with MLflow
    mlflow.log_metric("accuracy", accuracy_score(y_test, model.predict(X_test)))
    mlflow.sklearn.log_model(model, "model")
    
    return model
model = train_model(n_estimators=150)

# Deploy via Python
deployment = deploy(
    get_last_run(),
    platform="modal",
    name="fraud-detection-v1",
)
print(f"Model deployed to: {deployment.get('endpoint_url')}")

# Docker image
deploy(get_last_run(), platform="docker", name="fraud-detection-v1")

# Lambda package
deploy(
    get_last_run(),
    platform="lambda",
    name="fraud-detection-v1",
    lambda_zip_path="fraud-detection-v1-lambda.zip",
)

# 2. DEPLOY - One line to production
mltrack deploy --last --platform modal --name fraud-detection-v1

# 3. MONITOR - Track production performance
mltrack ui  # http://localhost:3000/deployments/fraud-detection-v1

The Full Workflow

# Start with your existing MLflow setup
export MLFLOW_TRACKING_URI=http://your-mlflow-server:5000

# Add MLTrack for better UI and deployment
uv add ml-track

# Train and deploy in one script
python train.py  # Tracks with MLflow, deploys with MLTrack

# Monitor everything in one place
mltrack ui  # Beautiful dashboard at http://localhost:3000

📚 Documentation

• Getting Started Guide - Set up MLTrack in 5 minutes
• User Guide - Comprehensive feature documentation
• API Reference - Detailed API documentation
• Deployment Guide - Deploy models to production
• Examples - Sample projects and notebooks

🎓 Examples

Computer Vision

from mltrack import track
import torch
import torchvision

@track(project="image-classification")
def train_resnet(learning_rate=0.001, epochs=10):
    model = torchvision.models.resnet18(pretrained=True)
    # Training code...
    return model

Natural Language Processing

from mltrack import track
from transformers import AutoModelForSequenceClassification

@track(project="sentiment-analysis") 
def fine_tune_bert(model_name="bert-base-uncased", batch_size=16):
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    # Fine-tuning code...
    return model

LLM Applications

from mltrack import track_llm
from openai import OpenAI

client = OpenAI()

@track_llm(name="rag-query")
def test_rag_pipeline(question: str, temperature=0.7):
    # Your RAG implementation
    return client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": question}],
        temperature=temperature,
    )

response = test_rag_pipeline("Test query")
print(response.choices[0].message.content)

🏗️ Architecture

graph TD
    A[Your ML Code] -->|Existing MLflow calls| B[MLflow Tracking Server]
    A -->|@track decorator| C[MLTrack Enhancement Layer]
    C --> B
    C --> D[MLTrack Deployment Service]
    D --> E[Modal/Lambda/Docker]
    C --> F[MLTrack UI]
    F --> G[Monitoring Dashboard]
    B --> H[(MLflow Store)]
    
    style C fill:#7c3aed,color:#fff
    style D fill:#7c3aed,color:#fff
    style F fill:#7c3aed,color:#fff

Key Points:

• MLTrack sits alongside MLflow, not in front of it
• Your MLflow tracking server stays unchanged
• MLTrack adds deployment and monitoring capabilities
• All MLflow features remain accessible

🤝 Contributing

We love contributions! Please see our Contributing Guide for details.

Development Setup

# Clone the repository
git clone https://github.com/EconoBen/mltrack.git
cd mltrack

# Install in development mode
pip install -e ".[dev]"

# Install frontend dependencies
cd frontend
npm install

# Run tests
pytest
npm test

Code Style

• Python: Black + isort + flake8
• TypeScript: ESLint + Prettier
• Pre-commit hooks included

🗺️ Roadmap

• v0.2.0 - AutoML integration and hyperparameter tuning
• v0.3.0 - Distributed training support
• v0.4.0 - Model monitoring and drift detection
• v0.5.0 - Kubernetes operator for deployment
• v1.0.0 - Production-ready with enterprise features

See our full roadmap for more details.

🙏 Acknowledgments

MLTrack is built on the shoulders of giants:

• MLflow - The core tracking engine
• Modal - Serverless deployment platform
• Next.js - React framework for the UI
• All our contributors

📝 License

MLTrack is MIT licensed. See the LICENSE file for details.

🌟 Star History

---

Made with ❤️ by the MLTrack community

Twitter • Discord • Website