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android / platform / external / pytorch / fe8558b7aa4ce55d06893c48d5cb00b7a7eb7dae / . / benchmarks
tree: 06165577802373b754d605809c1c5c330fd9a315 [path history] [tgz]
  1. distributed/
  2. dynamo/
  3. fastrnns/
  4. framework_overhead_benchmark/
  5. functional_autograd_benchmark/
  6. fuser/
  7. gpt_fast/
  8. inference/
  9. instruction_counts/
  10. nested/
  11. operator_benchmark/
  12. overrides_benchmark/
  13. profiler_benchmark/
  14. record_function_benchmark/
  15. serialization/
  16. sparse/
  17. static_runtime/
  18. tensorexpr/
  19. transformer/
  20. compare-fastrnn-results.py
  21. compare.sh
  22. README.md
  23. upload_scribe.py
benchmarks/README.md

PyTorch Benchmarks

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. Links are provided where descriptions exist: