August 2021 Conda install may be working now, as according to @ComputerScientist in the comments below, conda install tensorflow-gpu==2.4.1 will give cudatoolkit-10.1.243 and cudnn-7.6.5 The following was written in Jan 2021 and is out of date Currently conda install tensorflow-gpu installs tensorflow v2.3.0 and does NOT install the conda cudnn or cudatoolkit packages. Installing them … Read more
Step 1 Move to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin Step 2 Rename file cusolver64_11.dll To cusolver64_10.dll cusolver64_10.dll
It is necessary to have both the model, and the data on the same device, either CPU or GPU, for the model to process data. Data on CPU and model on GPU, or vice-versa, will result in a Runtime error. You can set a variable device to cuda if it’s available, else it will be … Read more
On Ubuntu 20.04, you can simply install NVIDIAs cuda toolkit cuda: sudo apt-get update sudo apt install nvidia-cuda-toolkit There are also install advices for Windows. The packge is around 1GB and it took a while to install… Some minutes later you need to export PATH variables so that it can be found: Find Shared Object … Read more
Yes. The OpenMP 4 target constructs were designed to support a wide range of accelerators. Compiler support for NVIDIA GPUs is available from GCC 7+ (see 1 and 2, although the latter has not been updated to reflect OpenMP 4 GPU support), Clang (see 3,4,5), and Cray. Compiler support for Intel GPUs is available in … Read more
It looks like PyTorch’s caching allocator reserves some fixed amount of memory even if there are no tensors, and this allocation is triggered by the first CUDA memory access (torch.cuda.empty_cache() deletes unused tensor from the cache, but the cache itself still uses some memory). Even with a tiny 1-element tensor, after del and torch.cuda.empty_cache(), GPUtil.showUtilization(all=True) … Read more
Update 3: Since late 2020, torch-mlir project has come a long way and now supports all major Operating systems. Using torch-mlir you can now use your AMD, NVIDIA or Intel GPUs with the latest version of Pytorch. You can download the binaries for your OS from here. Update 2: Since October 21, 2021, You can … Read more
I’m not a CUDA expert, — I’ve been developing with the AMD Stream SDK, which AFAIK is roughly comparable. You can disable the Windows watchdog timer, but that is highly not recommended, for reasons that should be obvious. To disable it, you need to regedit HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\Watchdog\Display\DisableBugCheck, create a REG_DWORD and set it to 1. You … Read more
You can list all the available GPUs by doing: >>> import torch >>> available_gpus = [torch.cuda.device(i) for i in range(torch.cuda.device_count())] >>> available_gpus [<torch.cuda.device object at 0x7f2585882b50>]