https://github.com/pierric/smart-chess-rust
https://github.com/pierric/smart-chess-rust
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/pierric/smart-chess-rust
- Owner: pierric
- License: bsd-3-clause
- Created: 2023-11-01T14:52:14.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-19T09:45:53.000Z (4 months ago)
- Last Synced: 2025-02-19T10:33:01.968Z (4 months ago)
- Language: Jupyter Notebook
- Size: 3.33 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
[](https://github.com/pierric/smart-chess-rust/actions/workflows/rust.yml)
# Build
## Python part
```
poetry install
```
## Rust part
```
poetry shell
export LIBTORCH_USE_PYTORCH=1
export LIBTORCH_BYPASS_VERSION_CHECK=1
cargo build
```
Tch is always built against the torch with patch-version being 0. But very safe to be run with the other patch versions. It is necessary to export the environment variable `LIBTORCH_BYPASS_VERSION_CHECK=1`.
If Cargo added multiple versions of ndarray and results in some type error, see this [link](https://github.com/PyO3/rust-numpy?tab=readme-ov-file#dependency-on-ndarray) for the explanation and solution.
# Quantization
Experiments with quantized model (int8) is also tried. No convicing results are seen, neither faster nor better performance. If interested, installing pytorch_quantization as described here: