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Update defaults and readme
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README.md

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@@ -54,11 +54,11 @@ python train.py --resume_from_checkpoint <path> ...
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python train.py --gpus 1 ...
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```
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## Feature set selection
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By default the trainer uses a factorized HalfKAv2 feature set (named "HalfKAv2^")
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By default the trainer uses a factorized HalfKAv2_hm feature set (named "HalfKAv2_hm^")
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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`
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The default is:
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```
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python train.py ... --features="HalfKAv2^"
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python train.py ... --features="HalfKAv2_hm^"
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```
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## Skipping certain fens in the training
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## Current recommended training invocation
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```
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python train.py --smart-fen-skipping --random-fen-skipping 3 --batch-size 16384 --threads 8 --num-workers 8 --gpus 1 trainingdata validationdata
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python train.py --smart-fen-skipping --random-fen-skipping 3 --batch-size 16384 --threads 2 --num-workers 2 --gpus 1 trainingdata validationdata
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```
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best nets have been trained with 16B d9-scored nets, training runs >200 epochs
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@@ -96,13 +96,13 @@ python serialize.py nn.nnue converted.pt
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Visualize a network from either a checkpoint (`.ckpt`), a serialized model (`.pt`)
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or a SF NNUE file (`.nnue`).
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```
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python visualize.py nn.nnue --features="HalfKAv2"
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python visualize.py nn.nnue --features="HalfKAv2_hm"
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```
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Visualize the difference between two networks from either a checkpoint (`.ckpt`), a serialized model (`.pt`)
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or a SF NNUE file (`.nnue`).
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```
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python visualize.py nn.nnue --features="HalfKAv2" --ref-model nn.cpkt --ref-features="HalfKAv2^"
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python visualize.py nn.nnue --features="HalfKAv2_hm" --ref-model nn.cpkt --ref-features="HalfKAv2_hm^"
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```
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# Logging

features.py

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@@ -42,7 +42,7 @@ def get_available_feature_blocks_names():
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return list(iter(_feature_blocks_by_name))
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def add_argparse_args(parser):
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_default_feature_set_name = 'HalfKAv2^'
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_default_feature_set_name = 'HalfKAv2_hm^'
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parser.add_argument("--features", dest='features', default=_default_feature_set_name, help="The feature set to use. Can be a union of feature blocks (for example P+HalfKP). \"^\" denotes a factorized block. Currently available feature blocks are: " + ', '.join(get_available_feature_blocks_names()))
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def _init():

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