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Add comprehensive README file for mlenv project
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README.md

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# mlenv
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A comprehensive machine learning environment and toolkit built with modern Python packaging and containerized deployment.
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## Overview
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`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.
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## Features
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- **Deep Learning**: PyTorch ecosystem (torch, torchaudio, torchvision)
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- **NLP & Transformers**: Hugging Face transformers and hub integration
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- **Computer Vision**: OpenCV and Ultralytics (YOLOv8+)
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- **Web Interfaces**: FastAPI for APIs and Gradio for interactive demos
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- **Data Analysis**: Pandas, NumPy for data manipulation
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- **Financial Analysis**: mplfinance for financial data visualization
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- **Interactive Computing**: Jupyter kernel support
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- **Gaming/Graphics**: Pygame for game development and graphics
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- **Containerized**: Docker support with multi-architecture builds
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- **Modern Packaging**: Uses uv for fast dependency management
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## Requirements
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- Python 3.11 or higher
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- Docker (optional, for containerized deployment)
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## Installation
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### Using uv (recommended)
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```bash
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# Install uv if you haven't already
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pip install uv
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# Clone the repository
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git clone https://github.com/pnstack/mlenv.git
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cd mlenv
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# Install dependencies
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uv sync
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```
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### Using pip
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```bash
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# Clone the repository
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git clone https://github.com/pnstack/mlenv.git
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cd mlenv
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# Install dependencies
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pip install -e .
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```
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### Using Docker
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```bash
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# Build the Docker image
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docker build -t mlenv .
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# Run a Python script
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docker run --rm -v $(pwd):/workspace mlenv /workspace/hello.py
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# Run interactively
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docker run --rm -it mlenv
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```
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## Quick Start
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Test the installation with the included hello script:
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```bash
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python hello.py
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```
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This should output: `Hello from mlenv!`
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## Key Dependencies
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| Package | Purpose |
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|---------|---------|
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| **torch, torchaudio, torchvision** | PyTorch deep learning framework |
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| **transformers** | Hugging Face transformers for NLP |
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| **ultralytics** | YOLOv8+ object detection |
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| **opencv-python** | Computer vision library |
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| **fastapi** | Modern web framework for APIs |
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| **gradio** | Interactive ML demos and UIs |
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| **pandas, numpy** | Data manipulation and analysis |
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| **mplfinance** | Financial data visualization |
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| **pygame** | Game development and graphics |
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## Development
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### Running Tests
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```bash
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pytest
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```
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### Code Style
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```bash
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# Check code style
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pep8 .
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```
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### Docker Development
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The project includes a multi-stage Dockerfile optimized for production use:
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```bash
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# Build for development
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docker build -t mlenv:dev .
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# Build for production with optimizations
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docker build -t mlenv:prod --target=base .
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```
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## CI/CD
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The project uses GitHub Actions for automated:
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- Multi-architecture Docker builds (linux/amd64, linux/arm64)
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- Container publishing to GitHub Container Registry
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- Automated testing on push and pull requests
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## Contributing
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1. Fork the repository
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2. Create a feature branch (`git checkout -b feature/amazing-feature`)
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3. Commit your changes (`git commit -m 'Add some amazing feature'`)
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4. Push to the branch (`git push origin feature/amazing-feature`)
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5. Open a Pull Request
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## License
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This project is open source. Please check the repository for license details.
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## Support
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For questions, issues, or contributions, please open an issue on the GitHub repository.

pyproject.toml

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[project]
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name = "mlenv"
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version = "0.1.0"
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description = "Add your description here"
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description = "A comprehensive machine learning environment and toolkit with PyTorch, transformers, and modern Python packaging"
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readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [

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