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pytorch/benchmarks
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Mingzhe Li e829d4fba9 [op-bench] fix jit mode (#45774) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45774

Fix RuntimeError: No such operator operator_benchmark::_consume

Test Plan: waitforsandcastle

Reviewed By: ngimel

Differential Revision: D24064982

fbshipit-source-id: 13160b6d18569e659ca1ab0ca1d444ed9947260c
2020-10-05 09:29:41 -07:00
..
distributed/ddp
fastrnns Benchmarks: tweak PE config settings. (#45349) 2020-09-26 23:13:29 -07:00
framework_overhead_benchmark Remove py2 compatible future imports (#44735) 2020-09-16 12:55:57 -07:00
functional_autograd_benchmark Reland of benchmark code (#43428) 2020-08-24 13:27:26 -07:00
operator_benchmark [op-bench] fix jit mode (#45774) 2020-10-05 09:29:41 -07:00
overrides_benchmark Add __torch_function__ for methods (#37091) 2020-08-05 20:44:13 -07:00
profiler_benchmark Source code level attribution in profiler (#43898) 2020-09-30 00:57:35 -07:00
record_function_benchmark Fix D23995953 import. 2020-09-29 19:30:23 -07:00
serialization
static_runtime [StaticRuntime] Integrate Static Runtime into PyTorchPredictor (#45640) 2020-10-02 23:03:05 -07:00
tensorexpr [WIP][JIT] Add benchmarking support of NV Fuser with FP16 dtype support (#44101) 2020-09-15 15:10:49 -07:00
compare-fastrnn-results.py Benchmarks: add scripts for FastRNNs results comparison. (#44134) 2020-09-03 13:44:42 -07:00
compare.sh Benchmarks: add scripts for FastRNNs results comparison. (#44134) 2020-09-03 13:44:42 -07:00
README.md
upload_scribe.py Benchmarks: make fuser and executor configurable from command line. (#44291) 2020-09-09 11:59:35 -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 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