CCStockFish

Overview

CCStockfish (中国鱼) 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).

Files

This distribution of CCStockfish 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 CCStockfish 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. 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:

• Threads

  The number of CPU threads used for searching a position. For best performance, set this equal to the number of CPU cores available.

• Hash

  The size of the hash table in MB. It is recommended to set Hash after setting Threads.

• Clear Hash

  Clear the hash table.

• Ponder

  Let CCStockfish ponder its next move while the opponent is thinking.

• MultiPV

  Output the N best lines (principal variations, PVs) when searching. Leave at 1 for best performance.

• Use NNUE

  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.

• EvalFile

  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.

• UCI_AnalyseMode

  An option handled by your GUI.

• UCI_Chess960

  An option handled by your GUI. If true, CCStockfish will play Chess960.

• UCI_ShowWDL

  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).

• UCI_LimitStrength

  Enable weaker play aiming for an Elo rating as set by UCI_Elo. This option overrides Skill Level.

• UCI_Elo

  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.

• Skill Level

  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.

• SyzygyPath

  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\dtz6

  It 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.

• SyzygyProbeDepth

  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.

• Syzygy50MoveRule

  Disable to let fifty-move rule draws detected by Syzygy tablebase probes count as wins or losses. This is useful for ICCF correspondence games.

• SyzygyProbeLimit

  Limit Syzygy tablebase probing to positions with at most this many pieces left (including kings and pawns).

• Move Overhead

  Assume a time delay of x ms due to network and GUI overheads. This is useful to avoid losses on time in those cases.

• Slow Mover

  Lower values will make CCStockfish take less time in games, higher values will make it think longer.

• nodestime

  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 of a version (standard node count) is obtained using all defaults. bench is currently bench 16 1 13 default depth mixed.

• compiler

  Give information about the compiler and environment used for building a binary.

• d

  Display the current position, with ascii art and fen.

• eval

  Return the evaluation of the current position.

• export_net [filename]

  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.

• flip

  Flips the side to move.

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 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:

1. 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.

2. 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 EvalFile UCI option, with the format nn-[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.

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 p