
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 inmax_pool2d
#60059 - Remove Caffe2 thread-pool leak warning #60318
- Add option to skip GitHub tag validation for
torch.hub.load
#62139 - Use
log.warning
intorch.distributed.run
to print OMP_NUM_THREADS warning #63953 - TorchElastic: Pretty print the failure message captured by @record #64036
torch.distribtued.run
to setnproc_per_node
to 1 by default #61552- Remove experimental API warning from
torch.distributed.elastic.utils.store
#60807 - Deprecate
use_env
intorch.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
andtorch.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

PyTorch 1.9.1 Bug Fix Release
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 inmax_pool2d
#60059 - Remove Caffe2 thread-pool leak warning #60318
- Add option to skip GitHub tag validation for
torch.hub.load
#62139 - Use
log.warning
intorch.distributed.run
to print OMP_NUM_THREADS warning #63953 - TorchElastic: Pretty print the failure message captured by @record #64036
torch.distribtued.run
to setnproc_per_node
to 1 by default #61552- Remove experimental API warning from
torch.distributed.elastic.utils.store
#60807 - Deprecate
use_env
intorch.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
andtorch.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