v0.30.0

[borg323]

In this version:

• Support for networks with attention body and smolgen added to blas, cuda, metal and onnx backends.
• WDL conversion for more realistic WDL score and contempt. Adds an Elo based WDL transformation of the NN value head output. Helps with more accurate play at high level (WDL sharpening), more aggressive play against weaker opponents and draw avoiding openings (contempt), piece odds play. For details on how it works see https://lczero.org/blog/2023/07/the-lc0-v0.30.0-wdl-rescale/contempt-implementation/.
• A new score type WDL_mu which follows the new eval convention, where +1.00 means 50% white win chance.
• Changed mlh threshold effect to create a smooth transition.
• WDL_mu score type is now the default and the --moves-left-threshold default was changed from 0 to 0.8.
• Simplified to a single --draw-score parameter, adjusting the draw score from white's perspective: 0 gives standard scoring, -1 gives Armageddon scoring.
• Updated describenet for new net architectures.
• Added a first-move-bonus option to the legacy time manager, to accompany book-ply-bonus for shallow openings.
• Persistent L2 cache optimization for the cuda backend. Use the cache_opt=true backend option to turn it on.
• Some performance improvements for the cuda, onnx and blas backends.
• Added the threads backend option to onnx, defaults to 0 (let the onnxruntime decide) except for onnx-cpu that defaults to 1.
• The onnx-dml package now includes a directml.dll installation script.
• Some users experienced memory issues with onnx-dml, so the defaults were changed. This may affect performance, in which case you can use the steps=8 backend option to get the old behavior.
• The Python bindings are available as a package, see the README for instructions.
• Revised 'simple' time manager.
• A new spinlock implementation (selected with --search-spin-backoff) to help with many cpu threads (e.g. 128 threads), obviously for cpu backends only.
• Fixes for contempt with infinite search/pondering and for the wdl display when pondering.
• Some assorted fixes and code cleanups.

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This discussion was created from the release v0.30.0.

[elcriollito]

which one would you recommend me to use on Chessbase?

[texium]

depends on your hardware

[EnricoArtese]

if i have a MacBook Air 2020, which links should i have download ?

[almaudoh]

@AtreusArtic what version of MacOs are you running? Lc0 metal backend is supported only on MacOs 12.3 and above.

[tonyzhigit]

Why is the training client not provided in lc0-v0.30.0-windows-onnx-dml.zip?

[borg323]

Unfortunately, only a very small subset of the potential onnx-dml users have a gpu that will be efficient in generating training data. If you want to use it, you can get the latest client from https://github.com/LeelaChessZero/lczero-client/releases/latest.

[saikrishna-1996]

I have an apple M3 Max (128GB). I want to use leela with chessbase on parallels desktop. What is the recommended setup?

[saikrishna-1996]

I am getting the following error when I try to add on chessbase (UCI was created without any errors)

[TerranceKhumalo-absa]

Hi everyone, is a JavaScript version of Leela like the one for stockfish.js https://github.com/nmrugg/stockfish.js