| # 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: |
| |
| * [Fast RNNs](fastrnns/README.md) |
| * [Dynamo](dynamo/README.md) |
| * [Functional autograd](functional_autograd_benchmark/README.md) |
| * [Instruction counts](instruction_counts/README.md) |
| * [Operator](operator_benchmark/README.md) |
| * [Overrides](overrides_benchmark/README.md) |
| * [Sparse](sparse/README.md) |
| * [Tensor expression](tensorexpr/HowToRun.md) |
