PyTorch CUDA error

Robotics & Edge Computing Jetson & Embedded Systems Jetson Orin Nano
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I am running computer vision inference on Jetson Orin Nano Super JetPack 6.2.1, using ultralytics and pytorch.

torch.cuda.is_available() returns true, but when it runs I get a runtime error: “CUDA error: no kernel image is available for execution on the device”

What and where is the correct version of the pytorch wheel I need to install?

Should I follow these:

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*** Please note that this reply is generated by LLM automatically ***

To resolve the issue you’re experiencing with running computer vision inference on Jetson Orin Nano Super with JetPack 6.2.1, using Ultralytics and PyTorch, where torch.cuda.is_available() returns True but you encounter a CUDA error indicating no kernel image is available for execution on the device, you need to ensure you’re using the correct version of the PyTorch wheel compatible with your setup.

Step 1: Determine the Correct PyTorch Version

First, you need to identify the correct PyTorch version that is compatible with your Jetson Orin Nano Super and JetPack 6.2.1. NVIDIA provides optimized PyTorch wheels for Jetson platforms, which can be found in the NVIDIA PyTorch for Jetson documentation.

Step 2: Install the Correct PyTorch Wheel

To install the correct PyTorch wheel, you can follow the instructions provided in the NVIDIA Docs for Installing PyTorch for Jetson Platform. Essentially, you would use a command similar to the one below, adjusting the version numbers as necessary to match your environment:

pip3 install --extra-index-url https://pypi.ngc.nvidia.com <pytorch_wheel_name>

Replace <pytorch_wheel_name> with the actual name of the PyTorch wheel you need, which can be found in the NVIDIA documentation or by searching the NGC (NVIDIA GPU Cloud) PyTorch repository.

Step 3: Verify CUDA Version Compatibility

Ensure that your CUDA version is compatible with the PyTorch wheel you’re installing. JetPack 6.2.1 comes with CUDA 11.4, so you should verify that the PyTorch wheel you choose is compatible with this CUDA version.

Step 4: Test PyTorch with CUDA

After installing the correct PyTorch wheel, test PyTorch with CUDA to ensure it’s working correctly. You can do this by running a simple PyTorch script that utilizes CUDA, such as:

import torch

# Check if CUDA is available
print(torch.cuda.is_available())

# If available, move the tensor to the GPU
if torch.cuda.is_available():
    device = torch.device("cuda:0")
    tensor = torch.randn(10, device=device)
    print(tensor)

Additional Tips

  • NVIDIA Optimized Frameworks: While the question mentions NVIDIA Optimized Frameworks like Kaldi, NVCaffe, PyTorch, and TensorFlow, the focus here is on resolving the PyTorch issue. However, exploring these frameworks can provide additional insights and tools for optimizing deep learning tasks on Jetson platforms.
  • DLProf and TFTRT: These tools are useful for profiling and optimizing TensorFlow models. If you’re working with TensorFlow, these might be worth exploring to improve performance.

By following these steps and ensuring you’re using the correct PyTorch wheel version compatible with your Jetson Orin Nano Super and JetPack 6.2.1, you should be able to resolve the CUDA error and successfully run computer vision inference tasks.

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Hi,

Please try the PyTorch packages below:

Thanks.

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