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Mingzhe Li e886450863 report p50 time instead of avg (#28722) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28722

as title

Test Plan:
```buck run mode/opt caffe2/benchmarks/operator_benchmark:benchmark_all_test -- --operator sigmoid

# ----------------------------------------
# PyTorch/Caffe2 Operator Micro-benchmarks
# ----------------------------------------
# Tag : short

# Benchmarking PyTorch: sigmoid
iters: 200, 462.6029555220157
iters: 400, 441.04792759753764
iters: 800, 441.81562116136774
iters: 1600, 440.79964311094955
iters: 3200, 436.3108493271284
iters: 6400, 440.87966314691585
iters: 12800, 452.29464218209614
# Mode: Eager
# Name: sigmoid_M512_N512
# Input: M: 512, N: 512
Forward Execution Time (us) : 441.048

Reviewed By: hl475

Differential Revision: D18149525

fbshipit-source-id: 5fe70a35b790ee7ad3ff57c0cb0b1c29cb609b83
2019-10-25 17:22:27 -07:00
..
fastrnns Ignore F401 in all __init__.py without putting noqa (#25823) 2019-10-23 15:28:13 -07:00
framework_overhead_benchmark Added running via throughput benchmark options. (#23077) 2019-07-22 11:27:55 -07:00
operator_benchmark report p50 time instead of avg (#28722) 2019-10-25 17:22:27 -07:00
README.md

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