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android / platform / external / pytorch / 6a14fcb9223d27d4be1d19d24e535707f76a3e01 / . / benchmarks
tree: 288ccd92096cff6ffffb66212a0b146addbe1b71 [path history] [tgz]
  1. cpp/
  2. distributed/
  3. fastrnns/
  4. framework_overhead_benchmark/
  5. functional_autograd_benchmark/
  6. fuser/
  7. instruction_counts/
  8. operator_benchmark/
  9. overrides_benchmark/
  10. profiler_benchmark/
  11. record_function_benchmark/
  12. serialization/
  13. sparse/
  14. static_runtime/
  15. tensorexpr/
  16. compare-fastrnn-results.py
  17. compare.sh
  18. README.md
  19. 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