mirror of
https://github.com/saymrwulf/pytorch.git
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10e2840ce3
update_hint_regression has been behaving, so I am setting 2% noise threshold for it. 1.5% for sum_floordiv_regression. I have one concern, with the way we do the regression detection. small or changes <threshold level will accumulate and eventually trigger failure. to avoid those would have to keep any eye on the dashboard and potentially refresh the expected result file regularly even when there is no faluires. . Pull Request resolved: https://github.com/pytorch/pytorch/pull/137548 Approved by: https://github.com/aorenste |
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| .. | ||
| distributed | ||
| dynamo | ||
| fastrnns | ||
| framework_overhead_benchmark | ||
| functional_autograd_benchmark | ||
| fuser | ||
| gpt_fast | ||
| inference | ||
| instruction_counts | ||
| nested | ||
| operator_benchmark | ||
| overrides_benchmark | ||
| profiler_benchmark | ||
| record_function_benchmark | ||
| serialization | ||
| sparse | ||
| static_runtime | ||
| tensorexpr | ||
| transformer | ||
| 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. Links are provided where descriptions exist: