1# PyTorch Benchmarks 2 3This folder contains scripts that produce reproducible timings of various PyTorch features. 4 5It also provides mechanisms to compare PyTorch with other frameworks. 6 7## Setup environment 8Make sure you're on a machine with CUDA, torchvision, and pytorch installed. Install in the following order: 9``` 10# Install torchvision. It comes with the pytorch stable release binary 11conda install pytorch torchvision -c pytorch 12 13# Install the latest pytorch master from source. 14# It should supersede the installation from the release binary. 15cd $PYTORCH_HOME 16python setup.py build develop 17 18# Check the pytorch installation version 19python -c "import torch; print(torch.__version__)" 20``` 21 22## Benchmark List 23 24Please refer to each subfolder to discover each benchmark suite. Links are provided where descriptions exist: 25 26* [Fast RNNs](fastrnns/README.md) 27* [Dynamo](dynamo/README.md) 28* [Functional autograd](functional_autograd_benchmark/README.md) 29* [Instruction counts](instruction_counts/README.md) 30* [Operator](operator_benchmark/README.md) 31* [Overrides](overrides_benchmark/README.md) 32* [Sparse](sparse/README.md) 33* [Tensor expression](tensorexpr/HowToRun.md) 34