![]() The following NEW packages will be INSTALLED:Ĭudatoolkit pkgs/main/linux-64::cudatoolkit-10.0.130-0 The following packages will be downloaded: The reason why you want to choose different CUDA versions for the binaries is e.g., for graphics card do you know if there is any possibility to conda install torchvision -c pytorch without having to install CUDA Toolkit? This is my output when I try to install torchvision (ignore the $USERPATH): Collecting package metadata: doneĮnvironment location: /$USERPATH/anaconda3/envs/maskrcnn_benchmark ![]() You don’t have to choose your system’s CUDA version it’s only used if you install PyTorch from source. Personally, I add the following to my scripts if _available():Īnd for the training Variables: if _available(): If you have the CUDA version, and a supported graphics card, you would e.g., call model.cuda() in your code to enable training using CUDA. If you never use cuda, I would just install the CPU version, because it’s smaller. Might be useful if you have an older card that doesn’t support CUDA 9.0 via its drivers, yet.Ĭould I simply always install the version with most recent cuda (9.1 currently) and be happy? Originally I thought I have to choose the cuda version I have installed on my system, but since this is not the case, why do I have to choose at all?īut why would I want to e.g. In that case, could I simply always install the version with most recent cuda (9.1 currently) and be happy? Over the cuda 9.0 version there? And if it always works, even on a CPU if I just make sure not to use cuda So for each cuda version or none i get a different binary. conda install pytorch-cpu torchvision -c pytorch # noneĪnd for pip and python 3.6 on Linux between:.conda install pytorch torchvision cuda91 -c pytorch # 9.1.conda install pytorch torchvision cuda90 -c pytorch # 9.0.conda install pytorch torchvision -c pytorch # 8.0.For example for conda and python 3.6 on Linux it is the choice between these: Sorry, but I still do not get it: when I install from binaries, without installing CUDA and cuDNN myself, I still can choose different CUDA versions from the download screen.
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