Fairy-Stockfish is a chess variant engine derived from Stockfish designed for the support of fairy chess variants and easy extensibility with more games. It can play various regional, historical, and modern chess variants as well as games with user-defined rules. For compatibility with graphical user interfaces it supports the UCI, UCCI, USI, UCI-cyclone, and CECP/XBoard protocols.
The goal of the project is to create an engine supporting a large variety of chess-like games, equipped with the powerful search of Stockfish. Despite its generality the playing strength is on a very high level in almost all supported variants. Due to its multi-protocol support Fairy-Stockfish works with almost any chess variant GUI.
You can download the Windows executable or Linux binary from the latest release or compile the program from source. The program comes without a graphical user interface, so you perhaps want to use it together with a compatible GUI, or play against it online at pychess, lishogi, xichess, or lichess. Read more about how to use Fairy-Stockfish in the wiki.
Optional NNUE evaluation parameter files to improve playing strength for selected variants can be obtained via patreon, also see the variant NNUE overview.
When NNUE evaluation is used, the
EvalFile parameter needs to be adjusted accordingly as explained in the wiki.
If you like this project, please support its development via patreon or paypal, by contributing CPU time to the framework for testing of code improvements, or by contributing to the code or documentation. An introduction to the code base can be found in the wiki.
Regional and historical games
- Xiangqi, Manchu, Minixiangqi, Supply chess
- Shogi, Shogi variants
- Makruk, ASEAN, Makpong, Ai-Wok
- Ouk Chatrang, Kar Ouk
- Shatar, Jeson Mor
- Shatranj, Courier
- Capablanca, Janus, Modern, Chancellor, Embassy, Gothic, Capablanca random chess
- Grand, Shako, Centaur, Tencubed, Opulent
- Chess960, Placement/Pre-Chess
- Crazyhouse, Loop, Chessgi, Pocket Knight, Capablanca-Crazyhouse
- Bughouse, Koedem
- Seirawan, Seirawan-Crazyhouse
- Amazon, Chigorin, Almost chess
- Hoppel-Poppel, New Zealand
- Antichess, Giveaway, Suicide, Losers, Codrus
- Extinction, Kinglet, Three Kings, Coregal
- King of the Hill, Racing Kings
- Three-check, Five-check
- Los Alamos, Gardner’s Minichess
- Horde, Maharajah and the Sepoys
- Knightmate, Nightrider, Grasshopper
- Minishogi, EuroShogi, Judkins shogi
- Kyoto shogi, Microshogi
- Dobutsu shogi, Goro goro shogi
- Tori shogi
- Yari shogi
- Okisaki shogi
- Sho shogi
Besides the C++ engine, this project also includes bindings for other programming languages in order to be able to use it as a library for chess variants. They support move, SAN, and FEN generation, as well as checking of game end conditions for all variants supported by Fairy-Stockfish. Since the bindings are using the C++ code, they are very performant compared to libraries directly written in the respective target language.
A port of Fairy-Stockfish to WebAssembly is maintained at https://github.com/ianfab/stockfish.wasm.
Stockfish is a free, powerful UCI chess engine derived from Glaurung 2.1. Stockfish 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 Stockfish with it.
The Stockfish engine features two evaluation functions for chess, the classical evaluation based on handcrafted terms, and the NNUE evaluation based on efficiently updatable neural networks. The classical evaluation runs efficiently on almost all CPU architectures, while the NNUE evaluation benefits from the vector intrinsics available on most CPUs (sse2, avx2, neon, or similar).
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.
The UCI protocol and available options
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.
Stockfish implements most commands as described in the UCI protocol
For users, the following UCI options, which can typically be set via a GUI, are available in Stockfish:
The number of CPU threads used for searching a position. For best performance, set this equal to the number of CPU cores available.
The size of the hash table in MB. It is recommended to set Hash after setting Threads.
Clear the hash table.
Let Stockfish ponder its next move while the opponent is thinking.
Output the N best lines (principal variations, PVs) when searching. Leave at 1 for best performance.
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.
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.
An option handled by your GUI.
An option handled by your GUI. If true, Stockfish will play Chess960.
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).
Enable weaker play aiming for an Elo rating as set by UCI_Elo. This option overrides Skill Level.
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.
Lower the Skill Level in order to make Stockfish 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.
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 “:”.
It is recommended to store .rtbw files on an SSD. There is no loss in storing the .rtbz files on a regular HD. 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.
Disable to let fifty-move rule draws detected by Syzygy tablebase probes count as wins or losses. This is useful for ICCF correspondence games.
Limit Syzygy tablebase probing to positions with at most this many pieces left (including kings and pawns).
A positive value for contempt favors middle game positions and avoids draws, effective for the classical evaluation only.
By default, contempt is set to prefer the side to move. Set this option to “White” or “Black” to analyse with contempt for that side, or “Off” to disable contempt.
Assume a time delay of x ms due to network and GUI overheads. This is useful to avoid losses on time in those cases.
Lower values will make Stockfish take less time in games, higher values will make it think longer.
Tells the engine to use nodes searched instead of wall time to account for elapsed time. Useful for engine testing.
Debug Log File
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:
bench ttSize threads limit fenFile limitType evalType
Performs a standard benchmark using various options. The signature or standard node count is obtained using all defaults.
bench 16 1 13 default depth mixed.
Give information about the compiler and environment used for building a binary.
Display the current position, with ascii art and fen.
Return the evaluation of the current position.
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.
Flips the side to move.
Generating Training Data
To generate training data from the classic eval, use the generate_training_data command with the setting “Use NNUE” set to “false”. The given example is generation in its simplest form. There are more commands.
uci setoption name PruneAtShallowDepth value false setoption name Use NNUE value false setoption name Threads value X setoption name Hash value Y setoption name SyzygyPath value path isready generate_training_data depth A count B keep_draws 1 eval_limit 32000
Ais the searched depth per move, or how far the engine looks forward. This value is an integer.
Bis the amount of positions generated. This value is also an integer.
Specify how many threads and how much memory you would like to use with the
y values. The option SyzygyPath is not necessary, but if you would like to use it, you must first have Syzygy endgame tablebases on your computer, which you can find here. You will need to have a torrent client to download these tablebases, as that is probably the fastest way to obtain them. The
path is the path to the folder containing those tablebases. It does not have to be surrounded in quotes.
This will create a file named “training_data.binpack” in the same folder as the binary containing the generated training data. Once generation is done, you can rename the file to something like “1billiondepth12.binpack” to remember the depth and quantity of the positions and move it to a folder named “trainingdata” in the same directory as the binaries.
You will also need validation data that is used for loss calculation and accuracy computation. Validation data is generated in the same way as training data, but generally at most 1 million positions should be used as there’s no need for more and it would just slow the learning process down. It may also be better to slightly increase the depth for validation data. After generation you can rename the validation data file to “val.binpack” and drop it in a folder named “validationdata” in the same directory to make it easier.
Training data formats.
Currently there are 3 training data formats. Two of them are supported directly.
.bin– the original training data format. Uses 40 bytes per entry. Is supported directly by the
.plain– a human readable training data format. This one is not supported directly by the
generate_training_datacommand. It should not be used for data exchange because it’s less compact than other formats. It is mostly useful for inspection of the data.
.binpack– a compact binary training data format that exploits positions chains to further reduce size. It uses on average between 2 to 3 bytes per entry when generating data with
generate_training_data. It is supported directly by
generate_training_datacommand. It is currently the default for the
generate_training_datacommand. A more in depth description can be found here
Conversion between formats.
There is a builting converted that support all 3 formats described above. Any of them can be converted to any other. For more information and usage guide see here.
A note on classical evaluation versus NNUE evaluation
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 Stockfish 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 provides additional tools to train and develop the NNUE networks. 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).
the NNUE evaluation depends on the Stockfish binary and the network parameter file (see the EvalFile UCI option). Not every parameter file is compatible with a given Stockfish 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.
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 format
nn-[SHA256 first 12 digits].nnue(for instance,
nn-c157e0a5755b.nnue). This file can be downloaded from
[filename] as needed.
What to expect from the Syzygy tablebases?
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.
Stockfish 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. Stockfish attempts to use large pages when available and will fall back to regular memory allocation when this is not the case.
Support on Linux
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.
Support on Windows
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 Stockfish from getting large pages: a fresh session is better in this regard.
Compiling Stockfish yourself from the sources
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.
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 Stockfish version and which compiler you used to create your executable. This information can be found by typing the following command in a console:
Understanding the code base and participating in the project
Stockfish’s improvement over the last decade has been a great community effort. There are a few ways to help contribute to its growth.
Improving the code
If you want to help improve the code, there are several valuable resources:
In this 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 being focused on Stockfish’s precise implementation. Nevertheless, a helpful resource.
The latest source can always be found on GitHub. Discussions about Stockfish take place these days mainly in the FishCooking group and on the Stockfish Discord channel. 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.
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 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.
For full details, read the copy of the GPL v3 found in the file named Copying.txt.