f09adaa4a4
Created by training an L1-128 net from scratch with a wider range of
evals in the training data and wld-fen-skipping disabled during
training. The differences in this training data compared to the first
dual nnue PR are:
- removal of all positions with 3 pieces
- when piece count >= 16, keep positions with simple eval above 750
- when piece count < 16, remove positions with simple eval above 3000
The asymmetric data filtering was meant to flatten the training data
piece count distribution, which was previously heavily skewed towards
positions with low piece counts.
Additionally, the simple eval range where the smallnet is used was
widened to cover more positions previously evaluated by the big net and
simple eval.
```yaml
experiment-name: 128--S1-hse-S7-v4-S3-v1-no-wld-skip
training-dataset:
- /data/hse/S3/leela96-filt-v2.min.high-simple-eval-1k.binpack
- /data/hse/S3/dfrc99-16tb7p-eval-filt-v2.min.high-simple-eval-1k.binpack
- /data/hse/S3/test80-apr2022-16tb7p.min.high-simple-eval-1k.binpack
- /data/hse/S7/test60-2020-2tb7p.v6-3072.high-simple-eval-v4.binpack
- /data/hse/S7/test60-novdec2021-12tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test77-nov2021-2tb7p.v6-3072.min.high-simple-eval-v4.binpack
- /data/hse/S7/test77-dec2021-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test77-jan2022-2tb7p.high-simple-eval-v4.binpack
- /data/hse/S7/test78-jantomay2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test79-apr2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test79-may2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test80-may2022-16tb7p.high-simple-eval-v4.binpack
- /data/hse/S7/test80-jun2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test80-jul2022-16tb7p.v6-dd.min.high-simple-eval-v4.binpack
- /data/hse/S7/test80-aug2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test80-sep2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test80-oct2022-16tb7p.v6-dd.high-simple-eval-v4.binpack
- /data/hse/S7/test80-nov2022-16tb7p-v6-dd.min.high-simple-eval-v4.binpack
- /data/hse/S7/test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test80-feb2023-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test80-mar2023-2tb7p.v6-sk16.min.high-simple-eval-v4.binpack
- /data/hse/S7/test80-apr2023-2tb7p-filter-v6-sk16.min.high-simple-eval-v4.binpack
- /data/hse/S7/test80-may2023-2tb7p.v6.min.high-simple-eval-v4.binpack
- /data/hse/S7/test80-jun2023-2tb7p.v6-3072.min.high-simple-eval-v4.binpack
- /data/hse/S7/test80-jul2023-2tb7p.v6-3072.min.high-simple-eval-v4.binpack
- /data/hse/S7/test80-aug2023-2tb7p.v6.min.high-simple-eval-v4.binpack
- /data/hse/S7/test80-sep2023-2tb7p.high-simple-eval-v4.binpack
- /data/hse/S7/test80-oct2023-2tb7p.high-simple-eval-v4.binpack
wld-fen-skipping: False
start-from-engine-test-net: False
nnue-pytorch-branch: linrock/nnue-pytorch/L1-128
engine-test-branch: linrock/Stockfish/L1-128-nolazy
engine-base-branch: linrock/Stockfish/L1-128
num-epochs: 500
start-lambda: 1.0
end-lambda: 1.0
```
Experiment yaml configs converted to easy_train.sh commands with:
https://github.com/linrock/nnue-tools/blob/4339954/yaml_easy_train.py
Binpacks interleaved at training time with:
https://github.com/official-stockfish/nnue-pytorch/pull/259
FT weights permuted with 10k positions from fishpack32.binpack with:
https://github.com/official-stockfish/nnue-pytorch/pull/254
Data filtered for high simple eval positions (v4) with:
https://github.com/linrock/Stockfish/blob/b9c8440/src/tools/transform.cpp#L640-L675
Training data can be found at:
https://robotmoon.com/nnue-training-data/
Local elo at 25k nodes per move of
L1-128 smallnet (nnue-only eval) vs. L1-128 trained on standard S1 data:
nn-epoch319.nnue : -241.7 +/- 3.2
Passed STC vs.
|
||
|---|---|---|
| .github | ||
| scripts | ||
| src | ||
| tests | ||
| .clang-format | ||
| .git-blame-ignore-revs | ||
| .gitignore | ||
| AUTHORS | ||
| CITATION.cff | ||
| CONTRIBUTING.md | ||
| Copying.txt | ||
| README.md | ||
| Top CPU Contributors.txt | ||
Stockfish
A free and strong UCI chess engine.
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Overview
Stockfish is a free and strong UCI chess engine derived from Glaurung 2.1 that analyzes chess positions and computes the optimal moves.
Stockfish does not include a graphical user interface (GUI) that is required to display a chessboard and to make it easy to input moves. These GUIs are developed independently from Stockfish and are available online. Read the documentation for your GUI of choice for information about how to use Stockfish with it.
See also the Stockfish documentation for further usage help.
Files
This distribution of Stockfish consists of the following files:
-
README.md, the file you are currently reading.
-
Copying.txt, a text file containing the GNU General Public License version 3.
-
AUTHORS, a text file with the list of authors for the project.
-
src, a subdirectory containing the full source code, including a Makefile that can be used to compile Stockfish on Unix-like systems.
-
a file with the .nnue extension, storing the neural network for the NNUE evaluation. Binary distributions will have this file embedded.
Contributing
See Contributing Guide.
Donating hardware
Improving Stockfish requires a massive amount of testing. You can donate your hardware resources by installing the Fishtest Worker and viewing the current tests on Fishtest.
Improving the code
In the chessprogramming wiki, many techniques used in Stockfish are explained with a lot of background information. The section on Stockfish describes many features and techniques used by Stockfish. However, it is generic rather than focused on Stockfish's precise implementation.
The engine testing is done on Fishtest. If you want to help improve Stockfish, please read this guideline first, where the basics of Stockfish development are explained.
Discussions about Stockfish take place these days mainly in the Stockfish Discord server. This is also the best place to ask questions about the codebase and how to improve it.
Compiling Stockfish
Stockfish has support for 32 or 64-bit CPUs, certain hardware instructions, big-endian machines such as Power PC, and other platforms.
On Unix-like systems, it should be easy to compile Stockfish directly from the
source code with the included Makefile in the folder src. In general, it is
recommended to run make help to see a list of make targets with corresponding
descriptions. An example suitable for most Intel and AMD chips:
cd src
make -j profile-build ARCH=x86-64-avx2
Detailed compilation instructions for all platforms can be found in our documentation. Our wiki also has information about the UCI commands supported by Stockfish.
Terms of use
Stockfish is free and distributed under the GNU General Public License version 3 (GPL v3). Essentially, this means you are free to do almost exactly what you want with the program, including distributing it among your friends, making it available for download from your website, selling it (either by itself or as part of some bigger software package), or using it as the starting point for a software project of your own.
The only real limitation is that whenever you distribute Stockfish in some way, you MUST always include the license and the full source code (or a pointer to where the source code can be found) to generate the exact binary you are distributing. If you make any changes to the source code, these changes must also be made available under GPL v3.