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pytorch/benchmarks
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Kimish Patel cda9e995e2 Benchmark repeat op. (#20016) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20016

PT's repeat op benchmark

Reviewed By: zheng-xq

Differential Revision: D15166941

fbshipit-source-id: b1ed7af790460456210b60bfb4e44a08657e9612
2019-05-20 07:34:54 -07:00
..
fastrnns
try to make at::cat in mm_tree_reduction operate on contig tensors (#18816)
2019-04-24 23:44:25 -07:00
operator_benchmark
Benchmark repeat op. (#20016)
2019-05-20 07:34:54 -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