- Chess titans wikipedia how to#
- Chess titans wikipedia code#
Release of Ethereal7.78 by Andrew Grant, CCC, September 04, 2016.Ethereal - Official Release by Andrew Grant, CCRL Discussion Board, June 25, 2016.Ethereal - Official Release by Andrew Grant, CCC, June 25, 2016.Ethereal Bitboard 6.26 Chess Engine.a star is born! by AA Ross, CCC, May 14, 2016.
Chess titans wikipedia how to#
Re: How to speed up my engine by Andrew Grant, CCC, May 03, 2016
Re: How to speed up my engine by Günther Simon, CCC, May 03, 2016. Chess titans wikipedia code#
Syzygy Bases using Fathom's probing code (9.65), superseded by 7-men Pyrrhic (12.50). Automated Tuning by Logistic Regression, AdaGrad (12.50). Reverse Futility Pruning (Static Null Move Pruning). It rivals speeds of GPU based trainers, by leveraging massive SMP and efficient implementations. It is fairly flexible and trying things like HalfKA, changing layer sizes, adding layers, changing activation functions, or adding more factorizers is only a few minutes of effort in the code. This toolkit was used in training the Halogen networks as well. It includes some AVX2 and even AVX512 code for use in updating the network parameters. The trainer itself is a fully original work, written in C and making use of all 104 threads. Finally, training the actual Network can take a few days, with many stops and starts to drop the learning rate and find a global optima. From there, processing that data down into a list of FENs and then into the format used by Ethereal's NNTrainer takes another 12 hours or so. For example, with a Batch Size of 16384, only about 50% of the 43,850 parameters are used on average.ĭata generation for a given network takes about 3 weeks, completed on a 104 core machine. The networks are trained using a modified form of the Adam optimizer, which allows better performance for datasets with extremely sparse input fields. In practice, each Net was trained somewhere between 2 and 4 billion positions total, evaluated by Ethereal / Ethereal NNUE. If I eventually add support for AVX (not avx2) machines, it will be a significant gain as AVX does not have 256-bit vector support for integer types in a meaningful way.ĭuring training the Network actually has 43850 input parameters, using a few factorization of the board to aid in training without having tens of billions of positions. This approach allows us to never have to pack the data downwards, saving many operations, and also lets us take a slightly more expensive approach to the later layers in exchange for massively increased precision. Instead, the network goes like this: int16_t => int16_t => (int32_t -> float_t) => float_t => float_t. Firstly, not all weights are quantized to int8 / int16 for the input layer. This is the textbook approach, but with some changes. Andrew Grant on his NNUE approach :Įthereal is using the HalfKP paradigm, with a 40960x256 -> 512x32x32x1 Network. The NNUEs are not derived from, trained on, nor duplicated from the works of the Stockfish team. 
One for standard chess, and the secondary network trained exclusively for Chess960. The program comes with two NNUEs for evaluation, Ethereal 13 (NNUE), the commercial Ethereal version for AVX2 systems was released on June 04, 2021.
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