mirror of
https://github.com/saymrwulf/pytorch.git
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87f7fdfd5c
Summary: This is actually something I discovered a while ago with the wall of serotonin. It was really easy for large scale runs to get bottlenecked on disk access. I have a hack in the working files of that machine to use `/dev/shm`, but I figured I should formalize and actually make a respectable utility. I also added a param to tweak the run cadence and print when a CorePool is created; these are just to make the CI logs a bit nicer. (A printout each second on a 40 minute CI job is a bit much...) Pull Request resolved: https://github.com/pytorch/pytorch/pull/56711 Reviewed By: agolynski Differential Revision: D28392248 Pulled By: robieta fbshipit-source-id: b6aa7445c488d8e4ab9d4b31ab18df4e12783d8f |
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| .. | ||
| cpp | ||
| distributed | ||
| fastrnns | ||
| framework_overhead_benchmark | ||
| functional_autograd_benchmark | ||
| instruction_counts | ||
| operator_benchmark | ||
| overrides_benchmark | ||
| profiler_benchmark | ||
| record_function_benchmark | ||
| serialization | ||
| sparse | ||
| static_runtime | ||
| tensorexpr | ||
| compare-fastrnn-results.py | ||
| compare.sh | ||
| README.md | ||
| upload_scribe.py | ||
PyTorch Benchmarks
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