b8fc24e092 | ||
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.. | ||
cpp | ||
distributed | ||
dynamo | ||
fastrnns | ||
framework_overhead_benchmark | ||
functional_autograd_benchmark | ||
fuser | ||
instruction_counts | ||
nested | ||
operator_benchmark | ||
overrides_benchmark | ||
profiler_benchmark | ||
record_function_benchmark | ||
serialization | ||
sparse | ||
static_runtime | ||
tensorexpr | ||
transformer | ||
README.md | ||
compare-fastrnn-results.py | ||
compare.sh | ||
upload_scribe.py |
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. Links are provided where descriptions exist: