I found that after installing Pytorch 0.4 GPU version in Anaconda, you don't need to install CUDA locally to call gpu acceleration. When running code, the GPU core can be used at more than 90%.
Edit:I used it in Windows 10. Don't know if it works in Linux.
@talonmies
Thanks for your url. It seems that pytorch don't need cuda in Windows, since its dependencies are cffi, mkl, numpy, and python.
I entered this command conda search -c pytorch pytorch=0.4.0 --info in Anaconda Prompt and it says
Loading channels: done pytorch 0.4.0 py35_cuda80_cudnn7he774522_1 ------------------------------------------ file name : pytorch-0.4.0-py35_cuda80_cudnn7he774522_1.tar.bz2 name : pytorch version : 0.4.0 build string: py35_cuda80_cudnn7he774522_1 build number: 1 size : 528.5 MB arch : x86_64 constrains : () platform : Platform.win license : BSD 3-Clause subdir : win-64 url : https://conda.anaconda.org/pytorch/win-64/pytorch-0.4.0-py35_cuda80_cudnn7he774522_1.tar.bz2 md5 : 7db3971bb054079d7c7ff84b6286c58e dependencies: - cffi - mkl >=2018 - numpy >=1.11 - python >=3.5,<3.6.0a0 pytorch 0.4.0 py35_cuda90_cudnn7he774522_1 ------------------------------------------ file name : pytorch-0.4.0-py35_cuda90_cudnn7he774522_1.tar.bz2 name : pytorch version : 0.4.0 build string: py35_cuda90_cudnn7he774522_1 build number: 1 size : 578.5 MB arch : x86_64 constrains : () platform : Platform.win license : BSD 3-Clause subdir : win-64 url : https://conda.anaconda.org/pytorch/win-64/pytorch-0.4.0-py35_cuda90_cudnn7he774522_1.tar.bz2 md5 : 8200c9841f9cad6f2e605015812aa3f2 dependencies: - cffi - mkl >=2018 - numpy >=1.11 - python >=3.5,<3.6.0a0 pytorch 0.4.0 py35_cuda91_cudnn7he774522_1 ------------------------------------------ file name : pytorch-0.4.0-py35_cuda91_cudnn7he774522_1.tar.bz2 name : pytorch version : 0.4.0 build string: py35_cuda91_cudnn7he774522_1 build number: 1 size : 546.1 MB arch : x86_64 constrains : () platform : Platform.win license : BSD 3-Clause subdir : win-64 url : https://conda.anaconda.org/pytorch/win-64/pytorch-0.4.0-py35_cuda91_cudnn7he774522_1.tar.bz2 md5 : 79d99a825f66b55b1aa6f04d22d68aac dependencies: - cffi - mkl >=2018 - numpy >=1.11 - python >=3.5,<3.6.0a0 pytorch 0.4.0 py36_cuda80_cudnn7he774522_1 ------------------------------------------ file name : pytorch-0.4.0-py36_cuda80_cudnn7he774522_1.tar.bz2 name : pytorch version : 0.4.0 build string: py36_cuda80_cudnn7he774522_1 build number: 1 size : 529.2 MB arch : x86_64 constrains : () platform : Platform.win license : BSD 3-Clause subdir : win-64 url : https://conda.anaconda.org/pytorch/win-64/pytorch-0.4.0-py36_cuda80_cudnn7he774522_1.tar.bz2 md5 : 27d20c9869fb57ffe0d6d014cf348855 dependencies: - cffi - mkl >=2018 - numpy >=1.11 - python >=3.6,<3.7.0a0 pytorch 0.4.0 py36_cuda90_cudnn7he774522_1 ------------------------------------------ file name : pytorch-0.4.0-py36_cuda90_cudnn7he774522_1.tar.bz2 name : pytorch version : 0.4.0 build string: py36_cuda90_cudnn7he774522_1 build number: 1 size : 577.6 MB arch : x86_64 constrains : () platform : Platform.win license : BSD 3-Clause subdir : win-64 url : https://conda.anaconda.org/pytorch/win-64/pytorch-0.4.0-py36_cuda90_cudnn7he774522_1.tar.bz2 md5 : 138dcca8eeff1d58a8fd9b1febf702f6 dependencies: - cffi - mkl >=2018 - numpy >=1.11 - python >=3.6,<3.7.0a0 pytorch 0.4.0 py36_cuda91_cudnn7he774522_1 ------------------------------------------ file name : pytorch-0.4.0-py36_cuda91_cudnn7he774522_1.tar.bz2 name : pytorch version : 0.4.0 build string: py36_cuda91_cudnn7he774522_1 build number: 1 size : 546.4 MB arch : x86_64 constrains : () platform : Platform.win license : BSD 3-Clause subdir : win-64 url : https://conda.anaconda.org/pytorch/win-64/pytorch-0.4.0-py36_cuda91_cudnn7he774522_1.tar.bz2 md5 : 326265665000de6f7501160b10b089c8 dependencies: - cffi - mkl >=2018 - numpy >=1.11 - python >=3.6,<3.7.0a0
conda listin thebaseenvironment and the virtual environment I used, and I didn't findcudatoolkitorcudnn.