opelly/Stockfish
1
0
Fork 0
mirror of https://github.com/opelly27/Stockfish.git synced 2026-05-20 10:57:43 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge remote-tracking branch 'remotes/nodchip/master' into trainer

Browse Source
This commit is contained in:
noobpwnftw
2020-09-19 15:41:30 +08:00
parent 26f63fe741 61bc8d12d3
commit 411adab149
57 changed files with 9136 additions and 1469 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+26 -1
View File
@@ -7,6 +7,31 @@
## Overview
Stockfish NNUE is a port of a shogi neural network named NNUE (efficiently updateable neural network backwards) to Stockfish 11. To learn more about the Stockfish chess engine, look [here](stockfish.md) for an overview and [here](https://github.com/official-stockfish/Stockfish) for the official repository.
## Building
To compile:
```
make -jN ARCH=... build
```
To compile with Profile Guided Optimizations. Requires that the computer that is used for compilation supports the selected `ARCH`.
```
make -jN ARCH=... profile-build
```
`N` is the number of threads to use for compilation.
`ARCH` is one of:
`x86-64-vnni512`, `x86-64-vnni256`, `x86-64-avx512`, `x86-64-bmi2`, `x86-64-avx2`,
`x86-64-sse41-popcnt`, `x86-64-modern`, `x86-64-ssse3`, `x86-64-sse3-popcnt`,
`x86-64`, `x86-32-sse41-popcnt`, `x86-32-sse2`, `x86-32`, `ppc-64`, `ppc-32,
armv7`, `armv7-neon`, `armv8`, `apple-silicon`, `general-64`, `general-32`.
`ARCH` needs to be chosen based based on the instruction set of the CPU that will run stockfish. `x86-64-modern` will produce a binary that works on most common processors, but other options may increase performance for specific hardware.
Additional options:
- `blas=[yes/no]` - whether to use an external BLAS library. Default is `no`. Using an external BLAS library may have a significantly improve learning performance and by default expects openBLAS to be installed.
## Training Guide
### Generating Training Data
To generate training data from the classic eval, use the gensfen command with the setting "Use NNUE" set to "false". The given example is generation in its simplest form. There are more commands.
@@ -45,7 +70,7 @@ Nets get saved in the "evalsave" folder.
- lambda is the amount of weight it puts to eval of learning data vs win/draw/loss results. 1 puts all weight on eval, lambda 0 puts all weight on WDL results.
### Reinforcement Learning
If you would like to do some reinforcement learning on your original network, you must first generate training data using the learn binaries with the setting `Use NNUE` set to true. Make sure that your previously trained network is in the eval folder. Use the commands specified above. Make sure `SkipLoadingEval` is set to false so that the data generated is using the neural net's eval by typing the command `setoption name SkipLoadingEval value false` before typing the `isready` command. You should aim to generate less positions than the first run, around 1/10 of the number of positions generated in the first run. The depth should be higher as well. You should also do the same for validation data, with the depth being higher than the last run.
If you would like to do some reinforcement learning on your original network, you must first generate training data using the learn binaries with the setting `Use NNUE` set to `pure`. Make sure that your previously trained network is in the eval folder. Use the commands specified above. Make sure `SkipLoadingEval` is set to false so that the data generated is using the neural net's eval by typing the command `setoption name SkipLoadingEval value false` before typing the `isready` command. You should aim to generate less positions than the first run, around 1/10 of the number of positions generated in the first run. The depth should be higher as well. You should also do the same for validation data, with the depth being higher than the last run.
After you have generated the training data, you must move it into your training data folder and delete the older data so that the binary does not accidentally train on the same data again. Do the same for the validation data and name it to val-1.bin to make it less confusing. Make sure the evalsave folder is empty. Then, using the same binary, type in the training commands shown above. Do __NOT__ set `SkipLoadingEval` to true, it must be false or you will get a completely new network, instead of a network trained with reinforcement learning. You should also set eval_save_interval to a number that is lower than the amount of positions in your training data, perhaps also 1/10 of the original value. The validation file should be set to the new validation data, not the old data.