Skip to main content

deep-chess-playground

Where deep learning meets chess.

Chess AI

This repository aims to implement techniques for neural chess engines, providing an in-depth look at the practical application of AI in chess game.

Table of contents

  1. Play chessbots on lichess
  2. Run chessbots locally
  3. Train your own chessbots

Play chessbots on lichess

COMING SOON

Run chessbots locally

Setup

It is recommended to use Anaconda for managing your Python environment, especially if you plan to use GPU acceleration. However, we also provide instructions for standard Python with pip.

  1. Download and install Anaconda from the official website: https://www.anaconda.com/products/distribution.

  2. Open Anaconda Prompt and run the following command to create a virtual environment:

    conda create --name deep_chess_playground python=3.10
    
  3. Activate the environment:

    conda activate deep_chess_playground
    
  4. Install PyTorch. Follow the instructions at PyTorch website. Choose the compute platform (CUDA or CPU) depending on whether you have a GPU or not.

  5. Install PyTorch Lightning:

    conda install pytorch-lightning -c conda-forge
    
  6. Install deep-chess-playground:

    pip install deep-chess-playground
    

Option 2: Using standard Python and pip

  1. Ensure you have Python 3.10 or later installed. You can download it from python.org.

  2. Create a virtual environment:

    python -m venv deep_chess_env
    
  3. Activate the virtual environment:

    • On Windows:
      deep_chess_env\Scripts\activate
      
    • On macOS and Linux:
      source deep_chess_env/bin/activate
      
  4. Install PyTorch. Follow the instructions at PyTorch website. Choose the compute platform (CUDA or CPU) depending on whether you have a GPU or not.

  5. Install PyTorch Lightning:

    pip install pytorch-lightning
    
  6. Install deep-chess-playground:

    pip install deep-chess-playground
    

Play

COMING SOON

Train your own chessbots

Data loading

Data loading

If you need a lot of training data, you can use the lichess.org open database which has more than 5 000 000 000 games recorded starting from January 2013!

Training

COMING SOON

Metadata

Release files for deep-chess-playground 0.0.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for deep-chess-playground 0.0.4
File Size Uploaded Provenance Actions
deep_chess_playground-0.0.4.tar.gz 24.2 kB Without provenance Details for deep_chess_playground-0.0.4.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for deep-chess-playground 0.0.4
File Interpreter ABI Platform Actions
deep_chess_playground-0.0.4-py3-none-any.whl Python 3 none any Details for deep_chess_playground-0.0.4-py3-none-any.whl

Total release size: 55.3 kB

Release files / deep_chess_playground-0.0.4.tar.gz

Download URL deep_chess_playground-0.0.4.tar.gz
Size 24.2 kB
Tags Source
SHA-256 checksum
How to use checksums
e7afe990bb20596c58bce931b04a15f70122f2d918cae3a0b10a13bc265a3aa5
BLAKE2b-256 checksum
How to use checksums
e4146009d61ad4ff1e8d21dcaae4a81468319f8631d0492647bf1d5d89e01dbb
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.1 CPython/3.11.9

Release files / deep_chess_playground-0.0.4-py3-none-any.whl

Download URL deep_chess_playground-0.0.4-py3-none-any.whl
Size 31.0 kB
Tags Python 3
SHA-256 checksum
How to use checksums
b35e40368c724efe801765b2627d961607727841336189331ee0703e90d2bc24
BLAKE2b-256 checksum
How to use checksums
348ce4f08d11c1df04492028ec7ac88941178d24e4bc3445fca69c000d679e23
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.1 CPython/3.11.9

Security for deep-chess-playground 0.0.4

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page