Merge pull request official-stockfish#265 from Sopel97/trainreadme

vondele · web-flow · commit cfbc9751063e · 2023-09-22T07:42:29.000+02:00
Update readme on training.
diff --git a/README.md b/README.md
@@ -37,80 +37,25 @@ Linux/Mac:
 sh compile_data_loader.bat
 ```
 
-# Train a network
-
-```
-source env/bin/activate
-python train.py --gpus 1 train_data.bin val_data.bin
-```
-
-## Resuming from a checkpoint
-```
-python train.py --gpus 1 --resume_from_checkpoint <path> ...
-```
-
-## Feature set selection
-By default the trainer uses a factorized HalfKAv2_hm feature set (named "HalfKAv2_hm^")
-If you wish to change the feature set used then you can use the `--features=NAME` option. For the list of available features see `--help`
-The default is:
-```
-python train.py ... --features="HalfKAv2_hm^"
-```
-
-## Skipping certain fens in the training
-
-`--smart-fen-skipping` currently skips over moves where the king is in check, or where the bestMove is a capture (typical of non-quiet positions).
-`--random-fen-skipping N` skip N fens on average before using one. Uses fewer fens per game, useful with large data sets.
-
-## Current recommended training invocation
-
-```
-python train.py --smart-fen-skipping --random-fen-skipping 3 --batch-size 16384 --threads 2 --num-workers 2 --gpus 1 trainingdata validationdata
-```
-best nets have been trained with 16B d9-scored nets, training runs >200 epochs
-
+# Network training and management
 
+Hard way: [wiki](https://github.com/official-stockfish/nnue-pytorch/wiki/Basic-training-procedure-(train.py))
 
-# Export a network
-
-Using either a checkpoint (`.ckpt`) or serialized model (`.pt`),
-you can export to SF NNUE format.  This will convert `last.ckpt`
-to `nn.nnue`, which you can load directly in SF.
-```
-python serialize.py last.ckpt nn.nnue
-```
-
-# Import a network
-
-Import an existing SF NNUE network to the pytorch network format.
-```
-python serialize.py nn.nnue converted.pt
-```
-
-# Visualize a network
-
-Visualize a network from either a checkpoint (`.ckpt`), a serialized model (`.pt`)
-or a SF NNUE file (`.nnue`).
-```
-python visualize.py nn.nnue --features="HalfKAv2_hm"
-```
-
-Visualize the difference between two networks from either a checkpoint (`.ckpt`), a serialized model (`.pt`)
-or a SF NNUE file (`.nnue`).
-```
-python visualize.py nn.nnue  --features="HalfKAv2_hm" --ref-model nn.cpkt --ref-features="HalfKAv2_hm^"
-```
+Easier way: [wiki](https://github.com/official-stockfish/nnue-pytorch/wiki/Basic-training-procedure-(easy_train.py))
 
 # Logging
 
+TODO: Move to wiki. Add setup for easy_train.py
+
 ```
-pip install tensorboard
 tensorboard --logdir=logs
 ```
 Then, go to http://localhost:6006/
 
 # Automatically run matches to determine the best net generated by a (running) training
 
+TODO: Move to wiki
+
 ```
 python run_games.py --concurrency 16 --stockfish_exe ./stockfish.master --c_chess_exe ./c-chess-cli --ordo_exe ./ordo --book_file_name ./noob_3moves.epd run96
 ```
