marimo-flow 0.1.1

Marimo Flow - Interactive ML notebooks with MLflow integration

Marimo Flow 🌊

Modern reactive ML development with Marimo notebooks and MLflow experiment tracking

Why Marimo Flow is Powerful 🚀

Marimo Flow combines the best of reactive notebook development with robust ML experiment tracking:

• 🔄 Reactive Development: Marimo's dataflow graph ensures your notebooks are always consistent - change a parameter and watch your entire pipeline update automatically
• 🤖 AI-Enhanced Workflow: Built-in GitHub Copilot support and AI assistants accelerate your ML development
• 📊 Seamless ML Pipeline: MLflow integration tracks every experiment, model, and metric without breaking your flow

This combination eliminates the reproducibility issues of traditional notebooks while providing enterprise-grade experiment tracking.

Features ✨

• 📓 Marimo Reactive Notebooks: Git-friendly .py notebooks with automatic dependency tracking
• 🔬 MLflow Experiment Tracking: Complete ML lifecycle management with model registry
• 🐳 Docker Deployment: One-command setup with docker-compose
• 💾 SQLite Backend: Lightweight, file-based storage for experiments
• 🎯 Interactive ML Development: Real-time parameter tuning with instant feedback

Quick Start 🏃‍♂️

With Docker (Recommended)

# Clone and start services
git clone <repository-url>
cd marimo-flow
docker-compose up -d

# Access services
# Marimo: http://localhost:2718
# MLflow: http://localhost:5000

Local Development

# Install dependencies
uv sync

# Start MLflow server
uv run mlflow server --host 0.0.0.0 --port 5000 --backend-store-uri sqlite:///data/mlflow/db/mlflow.db --default-artifact-root ./data/mlflow/artifacts

# Start Marimo (in another terminal)
uv run marimo edit examples/

Example Notebooks 📚

Explore our focused, production-ready notebooks:

🚀 Basic ML Workflow

• Simple classification with Random Forest
• Real-time parameter tuning
• MLflow experiment tracking
• Interactive visualizations

🏆 Model Comparison

• Compare multiple algorithms (RF, GB, LR, SVM)
• Cross-validation analysis
• Performance benchmarking
• Built-in datasets (Wine, Iris, Breast Cancer)

🔍 Data Exploration

• Interactive statistical analysis
• Correlation heatmaps
• Distribution visualizations
• PCA and t-SNE dimensionality reduction

Each notebook demonstrates reactive development principles and follows Marimo best practices for maintainable, reproducible ML code.

Project Structure 📁

marimo-flow/
├── examples/                    # Marimo notebooks
│   ├── 01_basic_ml_workflow.py     # Basic ML pipeline
│   ├── 02_model_comparison.py      # Multi-model comparison
│   └── 03_data_exploration.py      # Interactive data analysis
├── data/
│   └── mlflow/                  # MLflow storage
│       ├── artifacts/           # Model artifacts
│       ├── db/                  # SQLite database
│       └── prompts/             # Prompt templates
├── docker-compose.yaml          # Service orchestration
├── Dockerfile                   # Container definition
├── pyproject.toml              # Dependencies
└── README.md                   # This file

Configuration ⚙️

Environment Variables

• MLFLOW_TRACKING_URI: MLflow server URL (default: http://localhost:5000)
• MLFLOW_BACKEND_STORE_URI: Database connection string
• MLFLOW_DEFAULT_ARTIFACT_ROOT: Artifact storage location

Docker Services

• Marimo: Port 2718 - Interactive notebook environment
• MLflow: Port 5000 - Experiment tracking UI

Pre-installed ML & Data Science Stack 📦

Machine Learning & Scientific Computing

• scikit-learn ^1.5.2 - Machine learning library
• NumPy ^2.1.3 - Numerical computing
• pandas ^2.2.3 - Data manipulation and analysis
• PyArrow ^18.0.0 - Columnar data processing
• SciPy ^1.14.1 - Scientific computing
• matplotlib ^3.9.2 - Plotting library

High-Performance Data Processing

• Polars ^1.12.0 - Lightning-fast DataFrame library
• DuckDB ^1.1.3 - In-process analytical database
• Altair ^5.4.1 - Declarative statistical visualization

AI & LLM Integration

• OpenAI ^1.54.4 - GPT API integration
• FastAPI ^0.115.4 - Modern web framework
• Pydantic ^2.10.2 - Data validation

Database & Storage

• SQLAlchemy ^2.0.36 - SQL toolkit and ORM
• Alembic ^1.14.0 - Database migrations
• SQLGlot ^25.30.2 - SQL parser and transpiler

Web & API

• Starlette ^0.41.2 - ASGI framework
• Uvicorn ^0.32.0 - ASGI server
• httpx ^0.27.2 - HTTP client

Development Tools

• Black ^24.10.0 - Code formatter
• Ruff ^0.7.4 - Fast Python linter
• pytest ^8.3.3 - Testing framework
• MyPy ^1.13.0 - Static type checker

API Endpoints 🔌

MLflow REST API

• Experiments: GET /api/2.0/mlflow/experiments/list
• Runs: GET /api/2.0/mlflow/runs/search
• Models: GET /api/2.0/mlflow/registered-models/list

Marimo Server

• Notebooks: GET / - File browser and editor
• Apps: GET /run/<notebook> - Run notebook as web app

Contributing 🤝

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes following the coding standards
4. Test your changes: uv run pytest
5. Submit a pull request

License 📄

This project is licensed under the MIT License - see the LICENSE file for details.

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Built with ❤️ using Marimo and MLflow