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This article mainly introduces "how to solve pytorch-gpu installation failure". In daily operation, I believe many people have doubts about how to solve the problem of pytorch-gpu installation failure. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the doubts of "how to solve pytorch-gpu installation failure". Next, please follow the editor to study!

The gpu acceleration environment has been configured on the ubuntu18 system

The following problems occurred after installing pytorch-gpu:

Import torchx = torch.Tensor (2) x_cuda = x.cuda ()

X can be normal, when the x_cuda is defined, the command line is stuck and no longer continues to output

A few weeks later, the reason was finally found:

At the beginning, pytorch-gpu was version 1.0, but the configured version of cuda and cudnn was lower, and the higher version of pytorch was not supported. Later, we chose to install version 0.4.1 of pytorch to solve this problem.

The current environment is torch-0.4.1-cp36 + 384 graphics card driver + cuda8.0+cudnn6.0.

How hard it is to be able to smile.

Add: solve the problem of download error in conda installation pytorch-gpu version

After changing the source of Tsinghua University, the order of the official website is as follows:

Conda install pytorch=1.1.0 torchvision=0.3.0 cudatoolkit=9.0-c pytorch

However, HTTP error errors are often reported. Just delete-c pytorch.

At this point in conda install pytorch=1.1.0 torchvision=0.3.0 cudatoolkit=9.0, the study on "how to solve the failure of pytorch-gpu installation" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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