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pytorch/benchmarks
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Bin Bao 2a5851735a Set torch.backends.cudnn.enabled to false when testing accuracy (#94363) 
Summary: It looks like setting torch.backends.cudnn.deterministic to
True is not enough for eliminating non-determinism when testing
benchmarks with --accuracy, so let's turn off cudnn completely.
With this change, mobilenet_v3_large does not show random failure on my
local environment. Also take this chance to clean up CI skip lists.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/94363
Approved by: https://github.com/ezyang
2023-02-09 23:43:13 +00:00
..
cpp
distributed [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00
dynamo Set torch.backends.cudnn.enabled to false when testing accuracy (#94363) 2023-02-09 23:43:13 +00:00
fastrnns [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00
framework_overhead_benchmark [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00
functional_autograd_benchmark
fuser
instruction_counts [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00
nested
operator_benchmark [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00
overrides_benchmark
profiler_benchmark [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00
record_function_benchmark [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00
serialization
sparse [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00
static_runtime
tensorexpr [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00
transformer [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00
compare-fastrnn-results.py
compare.sh
README.md
upload_scribe.py [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00

README.md

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