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github.com/NUS-HPC-AI-Lab/InfoBatch
/ types & classes
Types & classes
16 in github.com/NUS-HPC-AI-Lab/InfoBatch
⨍
Functions
105
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Types & classes
16
↓ 5 callers
Class
ResNet
research/model.py:71
↓ 5 callers
Class
ResNet
examples/model.py:71
↓ 3 callers
Class
Lars
r"""Implements the LARS optimizer from `"Large batch training of convolutional networks" <https://arxiv.org/pdf/1708.03888.pdf>`_. Args:
research/lars.py:32
↓ 3 callers
Class
Lars
r"""Implements the LARS optimizer from `"Large batch training of convolutional networks" <https://arxiv.org/pdf/1708.03888.pdf>`_. Args:
examples/lars.py:32
↓ 2 callers
Class
InfoBatch
research/infobatch.py:5
↓ 2 callers
Class
InfoBatchSampler
research/infobatch.py:71
↓ 1 callers
Class
DistributedIBSampler
Wrapper over `Sampler` for distributed training. Allows you to use any sampler in distributed mode. It is especially useful in conjunctio
infobatch/infobatch.py:203
↓ 1 callers
Class
IBSampler
infobatch/infobatch.py:167
↓ 1 callers
Class
Lamb
r"""Implements Lamb algorithm. It has been proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_. Arguments
research/lamb.py:24
↓ 1 callers
Class
Lamb
r"""Implements Lamb algorithm. It has been proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_. Arguments
examples/lamb.py:24
Class
BasicBlock
research/model.py:12
Class
BasicBlock
examples/model.py:12
Class
Bottleneck
research/model.py:40
Class
Bottleneck
examples/model.py:40
Class
DatasetFromSampler
infobatch/infobatch.py:215
Class
InfoBatch
InfoBatch aims to achieve lossless training speed up by randomly prunes a portion of less informative samples based on the loss distribution
infobatch/infobatch.py:57