mlenv

A comprehensive machine learning environment and toolkit built with modern Python packaging and containerized deployment.

Overview

mlenv provides a ready-to-use machine learning environment with popular ML/AI libraries and frameworks. It's designed for data scientists, ML engineers, and researchers who need a consistent, reproducible environment for machine learning projects.

Features

• Deep Learning: PyTorch ecosystem (torch, torchaudio, torchvision)
• NLP & Transformers: Hugging Face transformers and hub integration
• Computer Vision: OpenCV and Ultralytics (YOLOv8+)
• Web Interfaces: FastAPI for APIs and Gradio for interactive demos
• Data Analysis: Pandas, NumPy for data manipulation
• Financial Analysis: mplfinance for financial data visualization
• Interactive Computing: Jupyter kernel support
• Gaming/Graphics: Pygame for game development and graphics
• Containerized: Docker support with multi-architecture builds
• Modern Packaging: Uses uv for fast dependency management

Requirements

• Python 3.11 or higher
• Docker (optional, for containerized deployment)

Installation

Using uv (recommended)

# Install uv if you haven't already
pip install uv

# Clone the repository
git clone https://github.com/pnstack/mlenv.git
cd mlenv

# Install dependencies
uv sync

Using pip

# Clone the repository
git clone https://github.com/pnstack/mlenv.git
cd mlenv

# Install dependencies
pip install -e .

Using Docker

# Build the Docker image
docker build -t mlenv .

# Run a Python script
docker run --rm -v $(pwd):/workspace mlenv /workspace/hello.py

# Run interactively
docker run --rm -it mlenv

Quick Start

Test the installation with the included hello script:

python hello.py

This should output: Hello from mlenv!

Key Dependencies

Package	Purpose
torch, torchaudio, torchvision	PyTorch deep learning framework
transformers	Hugging Face transformers for NLP
ultralytics	YOLOv8+ object detection
opencv-python	Computer vision library
fastapi	Modern web framework for APIs
gradio	Interactive ML demos and UIs
pandas, numpy	Data manipulation and analysis
mplfinance	Financial data visualization
pygame	Game development and graphics

Development

Running Tests

pytest

Code Style

# Check code style
pep8 .

Docker Development

The project includes a multi-stage Dockerfile optimized for production use:

# Build for development
docker build -t mlenv:dev .

# Build for production with optimizations
docker build -t mlenv:prod --target=base .

CI/CD

The project uses GitHub Actions for automated:

• Multi-architecture Docker builds (linux/amd64, linux/arm64)
• Container publishing to GitHub Container Registry
• Automated testing on push and pull requests

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is open source. Please check the repository for license details.

Support

For questions, issues, or contributions, please open an issue on the GitHub repository.