CCStockfish (**鱼) is a free, powerful UCI chess engine derived from Glaurung 2.1. CCStockfish is not a complete chess program and requires a UCI-compatible graphical user interface (GUI) (e.g. XBoard with PolyGlot, Scid, Cute Chess, eboard, Arena, Sigma Chess, Shredder, Chess Partner or Fritz) in order to be used comfortably. Read the documentation for your GUI of choice for information about how to use CCStockfish with it.
The CCStockfish engine features two evaluation functions for chess. The efficiently updatable neural network (NNUE) based evaluation is the default and by far the strongest. The classical evaluation based on handcrafted terms remains available. The strongest network is integrated in the binary and downloaded automatically during the build process. The NNUE evaluation benefits from the vector intrinsics available on most CPUs (sse2, avx2, neon, or similar).
This distribution of CCStockfish consists of the following files:
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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.
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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 CCStockfish 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.
The Universal Chess Interface (UCI) is a standard protocol used to communicate with a chess engine, and is the recommended way to do so for typical graphical user interfaces (GUI) or chess tools. CCStockfish implements the majority of its options as described in the UCI protocol.
Developers can see the default values for UCI options available in CCStockfish by typing
./CCStockfish uci in a terminal, but the majority of users will typically see them and
change them via a chess GUI. This is a list of available UCI options in CCStockfish:
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The number of CPU threads used for searching a position. For best performance, set this equal to the number of CPU cores available.
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The size of the hash table in MB. It is recommended to set Hash after setting Threads.
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Clear the hash table.
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Let CCStockfish ponder its next move while the opponent is thinking.
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Output the N best lines (principal variations, PVs) when searching. Leave at 1 for best performance.
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Toggle between the NNUE and classical evaluation functions. If set to "true", the network parameters must be available to load from file (see also EvalFile), if they are not embedded in the binary.
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The name of the file of the NNUE evaluation parameters. Depending on the GUI the filename might have to include the full path to the folder/directory that contains the file. Other locations, such as the directory that contains the binary and the working directory, are also searched.
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An option handled by your GUI.
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An option handled by your GUI. If true, CCStockfish will play Chess960.
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If enabled, show approximate WDL statistics as part of the engine output. These WDL numbers model expected game outcomes for a given evaluation and game ply for engine self-play at fishtest LTC conditions (60+0.6s per game).
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Enable weaker play aiming for an Elo rating as set by UCI_Elo. This option overrides Skill Level.
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If enabled by UCI_LimitStrength, aim for an engine strength of the given Elo. This Elo rating has been calibrated at a time control of 60s+0.6s and anchored to CCRL 40/4.
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Lower the Skill Level in order to make CCStockfish play weaker (see also UCI_LimitStrength). Internally, MultiPV is enabled, and with a certain probability depending on the Skill Level a weaker move will be played.
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Path to the folders/directories storing the Syzygy tablebase files. Multiple directories are to be separated by ";" on Windows and by ":" on Unix-based operating systems. Do not use spaces around the ";" or ":".
Example:
C:\tablebases\wdl345;C:\tablebases\wdl6;D:\tablebases\dtz345;D:\tablebases\dtz6It is recommended to store .rtbw files on an SSD. There is no loss in storing the .rtbz files on a regular HDD. It is recommended to verify all md5 checksums of the downloaded tablebase files (
md5sum -c checksum.md5) as corruption will lead to engine crashes. -
Minimum remaining search depth for which a position is probed. Set this option to a higher value to probe less aggressively if you experience too much slowdown (in terms of nps) due to tablebase probing.
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Disable to let fifty-move rule draws detected by Syzygy tablebase probes count as wins or losses. This is useful for ICCF correspondence games.
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Limit Syzygy tablebase probing to positions with at most this many pieces left (including kings and pawns).
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Assume a time delay of x ms due to network and GUI overheads. This is useful to avoid losses on time in those cases.
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Lower values will make CCStockfish take less time in games, higher values will make it think longer.
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Tells the engine to use nodes searched instead of wall time to account for elapsed time. Useful for engine testing.
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Write all communication to and from the engine into a text file.
For developers the following non-standard commands might be of interest, mainly useful for debugging:
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Performs a standard benchmark using various options. The signature of a version (standard node count) is obtained using all defaults.
benchis currentlybench 16 1 13 default depth mixed. -
Give information about the compiler and environment used for building a binary.
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Display the current position, with ascii art and fen.
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Return the evaluation of the current position.
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Exports the currently loaded network to a file. If the currently loaded network is the embedded network and the filename is not specified then the network is saved to the file matching the name of the embedded network, as defined in evaluate.h. If the currently loaded network is not the embedded network (some net set through the UCI setoption) then the filename parameter is required and the network is saved into that file.
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Flips the side to move.
Both approaches assign a value to a position that is used in alpha-beta (PVS) search to find the best move. The classical evaluation computes this value as a function of various chess concepts, handcrafted by experts, tested and tuned using fishtest. The NNUE evaluation computes this value with a neural network based on basic inputs (e.g. piece positions only). The network is optimized and trained on the evaluations of millions of positions at moderate search depth.
The NNUE evaluation was first introduced in shogi, and ported to CCStockfish afterward. It can be evaluated efficiently on CPUs, and exploits the fact that only parts of the neural network need to be updated after a typical chess move. The nodchip repository provided the first version of the needed tools to train and develop the NNUE networks. Today, more advanced training tools are available in the nnue-pytorch repository, while data generation tools are available in a dedicated branch.
On CPUs supporting modern vector instructions (avx2 and similar), the NNUE evaluation results in much stronger playing strength, even if the nodes per second computed by the engine is somewhat lower (roughly 80% of nps is typical).
Notes:
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the NNUE evaluation depends on the CCStockfish binary and the network parameter file (see the EvalFile UCI option). Not every parameter file is compatible with a given CCStockfish binary, but the default value of the EvalFile UCI option is the name of a network that is guaranteed to be compatible with that binary.
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to use the NNUE evaluation, the additional data file with neural network parameters needs to be available. Normally, this file is already embedded in the binary or it can be downloaded. The filename for the default (recommended) net can be found as the default value of the
EvalFileUCI option, with the formatnn-[SHA256 first 12 digits].nnue(for instance,nn-c157e0a5755b.nnue). This file can be downloaded from
https://tests.CCStockfishchess.org/api/nn/[filename]
replacing [filename] as needed.
If the engine is searching a position that is not in the tablebases (e.g. a position with 8 pieces), it will access the tablebases during the search. If the engine reports a very large score (typically 153.xx), this means it has found a winning line into a tablebase position.
If the engine is given a position to search that is in the tablebases, it will use the tablebases at the beginning of the search to preselect all good moves, i.e. all moves that preserve the win or preserve the draw while taking into account the 50-move rule. It will then perform a search only on those moves. The engine will not move immediately, unless there is only a single good move. The engine likely will not report a mate score, even if the position is known to be won.
It is therefore clear that this behaviour is not identical to what one might be used to with Nalimov tablebases. There are technical reasons for this difference, the main technical reason being that Nalimov tablebases use the DTM metric (distance-to-mate), while the Syzygy tablebases use a variation of the DTZ metric (distance-to-zero, zero meaning any move that resets the 50-move counter). This special metric is one of the reasons that the Syzygy tablebases are more compact than Nalimov tablebases, while still storing all information needed for optimal play and in addition being able to take into account the 50-move rule.
CCStockfish supports large pages on Linux and Windows. Large pages make the hash access more efficient, improving the engine speed, especially on large hash sizes. Typical increases are 5..10% in terms of nodes per second, but speed increases up to 30% have been measured. The support is automatic. CCStockfish attempts to use large pages when available and will fall back to regular memory allocation when this is not the case.
Large page support on Linux is obtained by the Linux kernel transparent huge pages functionality. Typically, transparent huge pages are already enabled, and no configuration is needed.
The use of large pages requires "Lock Pages in Memory" privilege. See Enable the Lock Pages in Memory Option (Windows) on how to enable this privilege, then run RAMMap to double-check that large pages are used. We suggest that you reboot your computer after you have enabled large pages, because long Windows sessions suffer from memory fragmentation, which may prevent CCStockfish from getting large pages: a fresh session is better in this regard.
CCStockfish 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 CCStockfish
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.
cd src
make help
make net
make build ARCH=x86-64-modern
When not using the Makefile to compile (for instance, with Microsoft MSVC) you need to manually set/unset some switches in the compiler command line; see file types.h for a quick reference.
When reporting an issue or a bug, please tell us which CCStockfish version and which compiler you used to create your executable. This information can be found by typing the following command in a console:
./CCStockfish compiler
CCStockfish's improvement over the last decade has been a great community effort. There are a few ways to help contribute to its growth.
Improving CCStockfish requires a massive amount of testing. You can donate your hardware resources by installing the Fishtest Worker and view the current tests on Fishtest.
If you want to help improve the code, there are several valuable resources:
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In this wiki, many techniques used in CCStockfish are explained with a lot of background information.
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The section on CCStockfish describes many features and techniques used by CCStockfish. However, it is generic rather than being focused on CCStockfish's precise implementation. Nevertheless, a helpful resource.
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The latest source can always be found on GitHub. Discussions about CCStockfish take place these days mainly in the FishCooking group and on the CCStockfish Discord channel. The engine testing is done on Fishtest. If you want to help improve CCStockfish, please read this guideline first, where the basics of CCStockfish development are explained.
CCStockfish 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 CCStockfish 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 the GPL v3.
For full details, read the copy of the GPL v3 found in the file named Copying.txt.