Hello, I’m a newbie on Nvidia. Should have just gotten an x86. Anyways, have a Jetson Orin Nano (aarch64) and I managed to download and successfully install Pytorch: install https://developer.download.nvidia.cn/compute/redist/jp/v60dp/pytorch/torch-2.3.0a0+ebedce2.nv24.02-cp310-cp310-linux_aarc…

OK, great that you got it working. And I forgot to mention, even if you don’t want to use the containers, mine have all the wheels they build under /opt. So you can just pull those out of the container and install them outside if you want. When working on projects, I just mount a local folder int…

Thanks Dusty. I’ve only done Docker a few times. I’d have to install it. But, once installed and running the Container, do I have to do anything special regarding where I run my model? Right now, I run train.py in /home/martin/yolo5. I will try your 2nd easier way first I think.

Ah I stand corrected. I had that PIP wheel 2.2.0 already - that’s the one I was trying to use and it recognizes CUDA. Problem is, of course, torchvision. I guess I’ll roll with the Container instead. I’m just not used to working with Containers.

Actually, maybe I will try and just install torchvision from source and see what happens. Inside a conda env.

You’re a genius!!! Installing from source worked. I installed torchvision 0.17 and now I see GPU Mem being hit. I am ridiculously stoked! Big thanks. Now I just don’t understand this “training” thing. lol. Once its done, I’m not really sure what the next step is… lol!

Help with Pytorch, torchvision on Jetpack 6

Robotics & Edge Computing Jetson Systems Jetson Orin Nano

dusty_nv March 4, 2024, 5:10pm 2

@martin225 sorry for your troubles, the easier way is to just use l4t-pytorch container which already includes PyTorch, torchvision, torchaudio, ect already compiled/installed with CUDA support:

The 2nd easier way is to build torchvision from source, from the Installation section of this thread:

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Help with Pytorch, torchvision on Jetpack 6

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