Installing TorchVision with:
pip install torchvision
leads to binary incompatibility of
torchvision 0.14.1 with
Runtime error: Couldn't load custom C++ ops
Is there a compatible version of TorchVision I can install with pip? Do I have to build it from source?
Yes, you would need to build
torchvision from source on your Jetson device.
Thank you. Do you have a link to reliable instructions how to build it?
You can find the instruction in the below topic:
Below are pre-built PyTorch pip wheel installers for Python on Jetson Nano, Jetson TX1/TX2, Jetson Xavier NX/AGX, and Jetson AGX Orin with JetPack 4.2 and newer.
Download one of the PyTorch binaries from below for your version of JetPack, and see the installation instructions to run on your Jetson. These pip wheels are built for ARM aarch64 architecture, so run these commands on your Jetson (not on a host PC). You can also use the pre-built
l4t-pytorch and l4t-ml container images and Dockerf…
These are PyTorch installers. I already installed a newer version from
https://developer.download.nvidia.cn/compute/redist/jp/v502/pytorch. What I’m looking for is
torchvision, which is a PyTorch add-on.
I tried to build
torchvision from source but run into this problem:
The detected CUDA version (12.0) mismatches the version that was used to compile
PyTorch (11.4). Please make sure to use the same CUDA versions.
PyTorch (11.4) works just fine with CUDA 12.0, even though it was built with an older version. Is there any other solution than reinstalling the original OS image with older CUDA version? Can I have two CUDA versions side by side?
Any chance Nvidia can provide PyTorch wheels built with CUDA 12.0?
What is the way to decode
nv22.12 sections of wheel name into CUDA version used to build it?
Do you upgrade the CUDA package from the website below?
If yes, you will need to build PyTorch from the source.
We only provide the prebuilt for the software that included in the JetPack.
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