1 as of now, pytorch which supports cuda 12.8 is not released yet. but unofficial support released nightly version of it. here are the commands to install it. so with this pytorch version you can use it on rtx 50XX. I've got 5080 and it works just fine.
I'm trying to install PyTorch with CUDA support on my Windows 11 machine, which has CUDA 12 installed and python 3.10. When I run nvcc --version, I get the following output: nvcc: NVIDIA (R) Cuda
I'm trying to do a basic install and import of Pytorch/Torchvision on Windows 10. I installed a Anaconda and created a new virtual environment named photo. I opened Anaconda prompt, activated the
0 -1 is a PyTorch alias for "infer this dimension given the others have all been specified" (i.e. the quotient of the original product by the new product). It is a convention taken from numpy.reshape(). Hence t.view(1,17) in the example would be equivalent to t.view(1,-1) or t.view(-1,17).
LibTorch version: 2.7.0 CUDA is available. GPU will be used. CUDA device count: 1 Current device name: NVIDIA GeForce RTX 5060 Ti Training Exception occurred: CUDA error: no kernel image is available for execution on the device CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH ...
The easiest way to check if PyTorch supports your compute capability is to install the desired version of PyTorch with CUDA support and run the following from a python interpreter
I don't understand what squeeze() and unsqueeze() do to a tensor, even after looking at the docs and related questions. I tried to understand it by exploring it myself in python. I first created a ...
