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PyTorch 1.9.1 Bug Fix Release

September 22, 2021
6 min read
blog-PyTorch-v1.9.1.jpg

Introducing PyTorch 1.9.1

PyTorch is a widely used, open source deep learning platform used for easily writing neural network layers in Python enabling a seamless workflow from research to production. Based on Torch, PyTorch has become a powerful machine learning framework favored by esteemed researchers around the world, and now adopted fully by Facebook.

The latest update of PyTorch is a small bug fix release.

PyTorch 1.9.1 Release Notes

  • Improvements
  • Bug Fixes
  • Documentation

Improvements

  • Stop warning on .names() access in max_pool2d #60059
  • Remove Caffe2 thread-pool leak warning #60318
  • Add option to skip GitHub tag validation for torch.hub.load #62139
  • Use log.warning in torch.distributed.run to print OMP_NUM_THREADS warning #63953
  • TorchElastic: Pretty print the failure message captured by @record #64036
  • torch.distribtued.run to set nproc_per_node to 1 by default #61552
  • Remove experimental API warning from torch.distributed.elastic.utils.store #60807
  • Deprecate use_env in torch.distributed.run #59409
  • Better engineering changes for torch.distributed launcher #59152

Bug fixes

Distributed / TorchElastic

  • Make init_method=tcp:// compatible with torch.distributed.run #63910
  • Fix default parameters (number of restarts, log level, number of processes per node) that regressed with the transition from torch.distributed.launch and torch.distributed.run and clarify the documentation accordingly #61294

Hub

  • Fix HTTP/403 error when calling torch.hub.load for TorchVision models #62072

Misc

  • torch.mm to check input matrix sizes shapes #61394

Documentation

  • Fix broken link in elastic launch doc #62378
  • Fix typo in torch.distribtued.run warning message #61127

This release has 2 assets:

  • Source code (zip)
  • Source code (tar.gz)

Visit the release page to download them.


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Contact Exxact Today


Free Resources

Browse our whitepapers, e-books, case studies, and reference architecture.

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blog-PyTorch-v1.9.1.jpg
News

PyTorch 1.9.1 Bug Fix Release

September 22, 2021 6 min read

Introducing PyTorch 1.9.1

PyTorch is a widely used, open source deep learning platform used for easily writing neural network layers in Python enabling a seamless workflow from research to production. Based on Torch, PyTorch has become a powerful machine learning framework favored by esteemed researchers around the world, and now adopted fully by Facebook.

The latest update of PyTorch is a small bug fix release.

PyTorch 1.9.1 Release Notes

  • Improvements
  • Bug Fixes
  • Documentation

Improvements

  • Stop warning on .names() access in max_pool2d #60059
  • Remove Caffe2 thread-pool leak warning #60318
  • Add option to skip GitHub tag validation for torch.hub.load #62139
  • Use log.warning in torch.distributed.run to print OMP_NUM_THREADS warning #63953
  • TorchElastic: Pretty print the failure message captured by @record #64036
  • torch.distribtued.run to set nproc_per_node to 1 by default #61552
  • Remove experimental API warning from torch.distributed.elastic.utils.store #60807
  • Deprecate use_env in torch.distributed.run #59409
  • Better engineering changes for torch.distributed launcher #59152

Bug fixes

Distributed / TorchElastic

  • Make init_method=tcp:// compatible with torch.distributed.run #63910
  • Fix default parameters (number of restarts, log level, number of processes per node) that regressed with the transition from torch.distributed.launch and torch.distributed.run and clarify the documentation accordingly #61294

Hub

  • Fix HTTP/403 error when calling torch.hub.load for TorchVision models #62072

Misc

  • torch.mm to check input matrix sizes shapes #61394

Documentation

  • Fix broken link in elastic launch doc #62378
  • Fix typo in torch.distribtued.run warning message #61127

This release has 2 assets:

  • Source code (zip)
  • Source code (tar.gz)

Visit the release page to download them.


Have any questions?
Contact Exxact Today


Free Resources

Browse our whitepapers, e-books, case studies, and reference architecture.

Explore