I recently bought a Jetson Orin Nano and installed the latest JetPack image (L4T 36.4.4).
For the past three days, I’ve been trying to get PyTorch working with GPU support, but every method I tried has failed:
NVIDIA prebuilt wheel: Installed, but it only uses the CPU. CUDA is not detected.
Manual build: Attempted twice, but the board restarted unexpectedly during the build.
Docker: The NVIDIA container image cannot be found (image not found error).
I’m not very experienced and I’ve already spent a lot of time on this. I just need a working and easy-to-follow method to run PyTorch with GPU on Jetson Orin Nano.
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I understand your frustration in getting PyTorch working on your Jetson Orin Nano. I’ll provide a comprehensive guide to help you resolve the issues you’re facing.
Issue 1: PyTorch installed but only uses CPU, CUDA not detected
To resolve this issue, you need to ensure that CUDA is properly installed and configured on your Jetson Orin Nano. Here are the steps to follow:
Verify that CUDA is installed by running the command nvcc --version in the terminal. If CUDA is not installed, you can install it using the sudo apt-get install nvidia-cuda-toolkit command.
Update the PATH environment variable to include the CUDA installation directory. You can do this by adding the following line to your ~/.bashrc file: export PATH=/usr/local/cuda/bin:$PATH
Restart the terminal or run source ~/.bashrc to apply the changes.
Install PyTorch using the pip3 install torch torchvision command. Make sure to install the correct version of PyTorch that is compatible with your CUDA version.
Verify that PyTorch is using CUDA by running the following code:
import torch
print(torch.cuda.is_available())
If CUDA is detected, you should see True printed.
Issue 2: Board restarted unexpectedly during build
This issue is likely caused by the board running out of memory or overheating during the build process. To resolve this issue, you can try the following:
Increase the swap space on your board by running the command sudo fallocate -l 4G /swapfile and then sudo mkswap /swapfile and finally sudo swapon /swapfile.
Reduce the number of parallel jobs during the build process by setting the JOBS environment variable to a lower value, for example, export JOBS=2.
Monitor the board’s temperature and memory usage during the build process using tools like htop or nvidia-smi.
Issue 3: NVIDIA container image cannot be found
This issue is likely caused by the NVIDIA container image not being properly configured or installed. To resolve this issue, you can try the following:
Verify that the NVIDIA container image is installed by running the command docker images and checking if the NVIDIA image is listed.
Pull the latest NVIDIA container image using the command docker pull nvcr.io/nvidia/pytorch:latest.
Verify that the NVIDIA container image is properly configured by running the command docker run --rm --gpus all nvcr.io/nvidia/pytorch:latest nvidia-smi.
Additional Tips
Make sure that your Jetson Orin Nano is properly configured and updated by running the command sudo apt-get update and sudo apt-get upgrade.
Verify that the PyTorch version you are installing is compatible with your CUDA version.
If you are still facing issues, try reinstalling PyTorch and CUDA, and then restart the board.
I hope this comprehensive guide helps you resolve the issues you’re facing in getting PyTorch working on your Jetson Orin Nano. If you have any further questions or concerns, feel free to ask.
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