# What does torch.backends.cudnn.benchmark do?

**URL:** <https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936>\
**Category:** Uncategorized\
**Created:** [August 8, 2017, 4:21pm UTC](https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936 "2017-08-08T16:21:17Z")\
**Posts on this page:** 1\
**Showing post:** 27

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**Author:** ![Shubhankar](https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/s/b9bd4f/32.png) [@Shubhankar](https://discuss.pytorch.org/u/Shubhankar)\
**Post date:** [January 13, 2020, 7:30pm UTC](https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936/27 "2020-01-13T19:30:06Z")

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> [@How to get (print) convolution\_algorithm chosen by CUDNN autotune?](https://discuss.pytorch.org/t/how-to-get-print-convolution-algorithm-chosen-by-cudnn-autotune/66554):
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> How to get the convolution algorithm chosen by CUDNN autotune in pytorch and how to manually define it later? Is it possible to interact with CUDNN API from inside Pytorch. Following [CUDNN function](http://www.goldsborough.me/cuda/ml/cudnn/c++/2017/10/01/14-37-23-convolutions_with_cudnn/) returns the type of algorithm to be used defined by CUDNN\_CONVOLUTION\_FWD\_PREFER\_FASTEST: cudnnGetConvolutionForwardAlgorithm(cudnn, input\_descriptor, kernel\_descriptor, convoluti…

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