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Based on the provided context, it appears that you are experiencing issues with installing and importing PyTorch on your Jetson AGX Orin device with JetPack 6.2.1.
Firstly, let’s break down the software requirements for Jetson Platform Services 2.0:
- JetPack 6.2/Jetson Linux 36.4.3 (or JetPack 6.1/Jetson Linux 36.4.0)
You have mentioned that you have installed the following packages:
nvidia-cudnn-frontend-1:1.4.0-1+cuda11.4 ( CUDA 11.4)
tinycudnn-2.0.0-cp310-cp310-linux_aarch64.whl (TinyCUDNN 2.0.0)
torchvision-0.2.3.0-cp310-cp310-linux_aarch64.whl (TorchVision 0.2.3.0)
nvidia-cutlass-4.0.0-py3-none-any.whl (Cutlass 4.0.0)
torch-2.8.0-cp310-cp310-linux_aarch64.whl (PyTorch 2.8.0)
triton-3.4.0-cp310-cp310-linux_aarch64.whl (Triton 3.4.0)
pycuda-2025.1.1-cp310-cp310-linux_aarch64.whl (PyCUDA 2025.1.1)
torchaudio-2.8.0-cp310-cp310-linux_aarch64.whl (TorchAudio 2.8.0)
xformers-0.0.3.3a00641d20250830-cp39-abi3-linux_aarch64.whl (XFormers 0.0.3.3a00641d20250830)
However, you are still experiencing issues with importing PyTorch, specifically with the error message:
ImportError: libcudso.0: cannot open shared object file: No such file or directory
This error message suggests that the CUDA libraries are not properly installed or configured on your system.
To resolve this issue, you may need to install additional packages or configure your system to recognize the CUDA libraries. Here are a few potential solutions:
- Install the CUDA toolkit: You can try installing the CUDA toolkit using the following command:
sudo apt-get install cuda-toolkit-11-4
This will install the CUDA 11.4 toolkit, which may resolve the issue.
- Configure the CUDA libraries: You can try configuring the CUDA libraries by setting the
LD_LIBRARY_PATH environment variable to include the CUDA library directory. For example:
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
This will tell your system to look for the CUDA libraries in the /usr/local/cuda/lib64 directory.
- Verify the CUDA installation: You can try verifying the CUDA installation by running the following command:
nvidia-smi
This will display information about the NVIDIA GPU and CUDA installation on your system.
If none of these solutions work, you may need to provide more information about your system configuration and the exact error messages you are experiencing.
In comparison to other topics in the provided context, it appears that there are several other issues related to Jetson AGX Orin and CUDA installation, such as:
- “Error with “Nvidia Container Runtime with Docker Integration” on AGX Orin with JP6.2”
- “Unable to install cuda-toolkit-12-6 inside 24.04 container”
- “JetPack 6.0 and 6.1 failing install of nvidia container runtime with docker integration”
These issues may be related to the CUDA installation and configuration on the Jetson AGX Orin device. However, without more information, it is difficult to determine the exact cause of the issue and provide a comprehensive solution.
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