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https://github.com/hpcaitech/ColossalAI.git
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0fedef4f3c
* integrated parallel layers for ease of building models * integrated 2.5d layers * cleaned codes and unit tests * added log metric by step hook; updated imagenet benchmark; fixed some bugs * reworked initialization; cleaned codes Co-authored-by: BoxiangW <45734921+BoxiangW@users.noreply.github.com>
24 lines
310 B
Python
Executable File
24 lines
310 B
Python
Executable File
from colossalai.amp import AMP_TYPE
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LOG_NAME = 'cifar-simclr'
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EPOCH = 800
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BATCH_SIZE = 512
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NUM_EPOCHS = 51
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LEARNING_RATE = 0.03*BATCH_SIZE/256
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WEIGHT_DECAY = 0.0005
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MOMENTUM = 0.9
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fp16 = dict(
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mode=AMP_TYPE.TORCH,
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)
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dataset = dict(
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root='./dataset',
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)
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gradient_accumulation=1
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clip_grad_norm=1.0
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