34 lines
1.1 KiB
Markdown
34 lines
1.1 KiB
Markdown
# PyTorch Benchmarks
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This folder contains scripts that produce reproducible timings of various PyTorch features.
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It also provides mechanisms to compare PyTorch with other frameworks.
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## Setup environment
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Make sure you're on a machine with CUDA, torchvision, and pytorch installed. Install in the following order:
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```
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# Install torchvision. It comes with the pytorch stable release binary
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pip3 install torch torchvision
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# Install the latest pytorch master from source.
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# It should supersede the installation from the release binary.
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cd $PYTORCH_HOME
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python setup.py build develop
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# Check the pytorch installation version
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python -c "import torch; print(torch.__version__)"
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```
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## Benchmark List
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Please refer to each subfolder to discover each benchmark suite. Links are provided where descriptions exist:
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* [Fast RNNs](fastrnns/README.md)
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* [Dynamo](dynamo/README.md)
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* [Functional autograd](functional_autograd_benchmark/README.md)
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* [Instruction counts](instruction_counts/README.md)
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* [Operator](operator_benchmark/README.md)
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* [Overrides](overrides_benchmark/README.md)
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* [Sparse](sparse/README.md)
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* [Tensor expression](tensorexpr/HowToRun.md)
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