70ba9de85c
Creating this net involved: - a 6-stage training process from scratch. The datasets used in stages 1-5 were fully minimized. - permuting L1 weights with https://github.com/official-stockfish/nnue-pytorch/pull/254 A strong epoch after each training stage was chosen for the next. The 6 stages were: ``` 1. 400 epochs, lambda 1.0, default LR and gamma UHOx2-wIsRight-multinet-dfrc-n5000 (135G) nodes5000pv2_UHO.binpack data_pv-2_diff-100_nodes-5000.binpack wrongIsRight_nodes5000pv2.binpack multinet_pv-2_diff-100_nodes-5000.binpack dfrc_n5000.binpack 2. 800 epochs, end-lambda 0.75, LR 4.375e-4, gamma 0.995, skip 12 LeelaFarseer-T78juntoaugT79marT80dec.binpack (141G) T60T70wIsRightFarseerT60T74T75T76.binpack test78-junjulaug2022-16tb7p.no-db.min.binpack test79-mar2022-16tb7p.no-db.min.binpack test80-dec2022-16tb7p.no-db.min.binpack 3. 800 epochs, end-lambda 0.725, LR 4.375e-4, gamma 0.995, skip 20 leela93-v1-dfrc99-v2-T78juntosepT80jan-v6dd-T78janfebT79aprT80aprmay.min.binpack leela93-filt-v1.min.binpack dfrc99-16tb7p-filt-v2.min.binpack test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.binpack test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.binpack test78-janfeb2022-16tb7p.min.binpack test79-apr2022-16tb7p.min.binpack test80-apr2022-16tb7p.min.binpack test80-may2022-16tb7p.min.binpack 4. 800 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 24 leela96-dfrc99-v2-T78juntosepT79mayT80junsepnovjan-v6dd-T80mar23-v6-T60novdecT77decT78aprmayT79aprT80may23.min.binpack leela96-filt-v2.min.binpack dfrc99-16tb7p-filt-v2.min.binpack test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.binpack test79-may2022-16tb7p.filter-v6-dd.min.binpack test80-jun2022-16tb7p.filter-v6-dd.min.binpack test80-sep2022-16tb7p.filter-v6-dd.min.binpack test80-nov2022-16tb7p.filter-v6-dd.min.binpack test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.binpack test80-mar2023-2tb7p.v6-sk16.min.binpack test60-novdec2021-16tb7p.min.binpack test77-dec2021-16tb7p.min.binpack test78-aprmay2022-16tb7p.min.binpack test79-apr2022-16tb7p.min.binpack test80-may2023-2tb7p.min.binpack 5. 960 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 28 Increased max-epoch to 960 near the end of the first 800 epochs 5af11540bbfe dataset: https://github.com/official-stockfish/Stockfish/pull/4635 6. 1000 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 28 Increased max-epoch to 1000 near the end of the first 800 epochs 1ee1aba5ed dataset: https://github.com/official-stockfish/Stockfish/pull/4782 ``` L1 weights permuted with: ```bash python3 serialize.py $nnue $nnue_permuted \ --features=HalfKAv2_hm \ --ft_optimize \ --ft_optimize_data=/data/fishpack32.binpack \ --ft_optimize_count=10000 ``` Speed measurements from 100 bench runs at depth 13 with profile-build x86-64-avx2: ``` sf_base = 1329051 +/- 2224 (95%) sf_test = 1163344 +/- 2992 (95%) diff = -165706 +/- 4913 (95%) speedup = -12.46807% +/- 0.370% (95%) ``` Training data can be found at: https://robotmoon.com/nnue-training-data/ Local elo at 25k nodes per move (vs. L1-2048 nn-1ee1aba5ed4c.nnue) ep959 : 16.2 +/- 2.3 Failed 10+0.1 STC: https://tests.stockfishchess.org/tests/view/6501beee2cd016da89abab21 LLR: -2.92 (-2.94,2.94) <0.00,2.00> Total: 13184 W: 3285 L: 3535 D: 6364 Ptnml(0-2): 85, 1662, 3334, 1440, 71 Failed 180+1.8 VLTC: https://tests.stockfishchess.org/tests/view/6505cf9a72620bc881ea908e LLR: -2.94 (-2.94,2.94) <0.00,2.00> Total: 64248 W: 16224 L: 16374 D: 31650 Ptnml(0-2): 26, 6788, 18640, 6650, 20 Passed 60+0.6 th 8 VLTC SMP (STC bounds): https://tests.stockfishchess.org/tests/view/65084a4618698b74c2e541dc LLR: 2.95 (-2.94,2.94) <0.00,2.00> Total: 90630 W: 23372 L: 23033 D: 44225 Ptnml(0-2): 13, 8490, 27968, 8833, 11 Passed 60+0.6 th 8 VLTC SMP: https://tests.stockfishchess.org/tests/view/6501d45d2cd016da89abacdb LLR: 2.95 (-2.94,2.94) <0.50,2.50> Total: 137804 W: 35764 L: 35276 D: 66764 Ptnml(0-2): 31, 13006, 42326, 13522, 17 closes https://github.com/official-stockfish/Stockfish/pull/4795 bench 1246812 |
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| scripts | ||
| src | ||
| tests | ||
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| 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.
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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.
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src, a subdirectory containing the full source code, including a Makefile that can be used to compile Stockfish on Unix-like systems.
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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.