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Mingzhe Li 516ea33f6a add PT maxpool and avgpool ops to the benchmark suite (#21200) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21200

This diff adds MaxPool1d/2d/3d and AvgPool1d/2d/3d to the benchmark suite.

Reviewed By: hl475

Differential Revision: D15541980

fbshipit-source-id: 394d136ee94a16ee24285939323ca5fe317e99d3
2019-05-31 19:35:29 -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
add PT maxpool and avgpool ops to the benchmark suite (#21200)
2019-05-31 19:35:29 -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