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Daya Khudia fb06c9e61f qconv operator level benchmark (#22895) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22895

Adding op level benchmarking for qconv operator

Reviewed By: mingzhe09088

Differential Revision: D16274273

fbshipit-source-id: 6674753e38f6692f5e6d0db0cac90c5fbf358147
2019-08-05 09:39:16 -07:00
..
fastrnns Fix spelling errors (#21665) 2019-06-13 15:21:55 -07:00
framework_overhead_benchmark Added running via throughput benchmark options. (#23077) 2019-07-22 11:27:55 -07:00
operator_benchmark qconv operator level benchmark (#22895) 2019-08-05 09:39:16 -07:00
README.md Move fast rnn benchmark to pytorch/pytorch 2019-03-27 14:46:09 -07:00

README.md

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 supercede 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