- * #### 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 Stockfish 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 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][nodchip-link] 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][pytorch-link], while data generation tools
-are available in [a dedicated branch][tools-link].
-
-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 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.
-
-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