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Update 2021-10-26-accelerating-pytorch-with-cuda-graphs.md
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_posts/2021-10-26-accelerating-pytorch-with-cuda-graphs.md

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### API example
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PyTorch exposes graphs via a raw [`torch.cuda.CUDAGraph`](https://pytorch.org/docs/master/generated/torch.cuda.graph.html#torch.cuda.graph)class and two convenience wrappers, [`torch.cuda.graph`](https://pytorch.org/docs/master/generated/torch.cuda.CUDAGraph.html#torch.cuda.CUDAGraph) and [`torch.cuda.make_graphed_callables`](https://pytorch.org/docs/master/generated/torch.cuda.make_graphed_callables.html#torch.cuda.make_graphed_callables).
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PyTorch exposes graphs via a raw [`torch.cuda.CUDAGraph`](https://pytorch.org/docs/master/generated/torch.cuda.graph.html#torch.cuda.graph)class and two convenience wrappers, [`torch.cuda.graph`](https://pytorch.org/docs/master/generated/torch.cuda.graph.html#torch.cuda.graph) and [`torch.cuda.make_graphed_callables`](https://pytorch.org/docs/master/generated/torch.cuda.make_graphed_callables.html#torch.cuda.make_graphed_callables).
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```python
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N, D_in, H, D_out = 640, 4096, 2048, 1024
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[**Edward Yang**](mailto:ezyang@fb.com)
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*Research Engineer, Facebook*
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Edward studied CS at MIT and then Stanford before starting at Facebook. He is a part of the PyTorch core team and is one of the leading contributors to PyTorch.
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Edward studied CS at MIT and then Stanford before starting at Facebook. He is a part of the PyTorch core team and is one of the leading contributors to PyTorch.

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