mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-14 20:57:59 +00:00
96cedefd8e
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/50860 Since fairscale.nn.Pipe still uses 'balance' and 'devices' parameters, other frameworks like fairseq still use these parameters. As a result, the `convert_to_balance` method is a nice utility to use for migrating to PyTorch Pipe without changing a lot of code in other frameworks. In addition to this I've renamed the method to be more illustrative of what it does and also allowed an optional devices parameter. ghstack-source-id: 120430775 Test Plan: 1) waitforbuildbot 2) Tested with fairseq Reviewed By: SciPioneer Differential Revision: D25987273 fbshipit-source-id: dccd42cf1a74b08c876090d3a10a94911cc46dd8 |
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| .. | ||
| cpp/tensorexpr | ||
| distributed | ||
| fastrnns | ||
| framework_overhead_benchmark | ||
| functional_autograd_benchmark | ||
| operator_benchmark | ||
| overrides_benchmark | ||
| profiler_benchmark | ||
| record_function_benchmark | ||
| serialization | ||
| sparse | ||
| static_runtime | ||
| tensorexpr | ||
| compare-fastrnn-results.py | ||
| compare.sh | ||
| README.md | ||
| upload_scribe.py | ||
PyTorch Benchmarks
NOTE: This folder is currently work in progress.
This folder contains scripts that produce reproducible timings of various PyTorch features.
It also provides mechanisms to compare PyTorch with other frameworks.
Setup environment
Make sure you're on a machine with CUDA, torchvision, and pytorch installed. Install in the following order:
# Install torchvision. It comes with the pytorch stable release binary
conda install pytorch torchvision -c pytorch
# Install the latest pytorch master from source.
# It should supersede the installation from the release binary.
cd $PYTORCH_HOME
python setup.py build develop
# Check the pytorch installation version
python -c "import torch; print(torch.__version__)"
Benchmark List
Please refer to each subfolder to discover each benchmark suite