Assume that you’ve just flashed the jetson orin agx with the latest version of JP such that R36.4 is the current version. What image do I need to extend (e.g. FROM xx) in my Dockerfile to be able to utilize the gpu with pytorch? I have the nvidia container runtime installed and cuda 12.6.
Hi,
Here are some suggestions for the common issues:
1. Performance
Please run the below command before benchmarking deep learning use case:
$ sudo nvpmodel -m 0
$ sudo jetson_clocks
2. Installation
Installation guide of deep learning frameworks on Jetson:
- TensorFlow: Installing TensorFlow for Jetson Platform - NVIDIA Docs
- PyTorch: Installing PyTorch for Jetson Platform - NVIDIA Docs
We also have containers that have frameworks preinstalled:
Data Science, Machine Learning, AI, HPC Containers | NVIDIA NGC
3. Tutorial
Startup deep learning tutorial:
- Jetson-inference: Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson
- TensorRT sample: Jetson/L4T/TRT Customized Example - eLinux.org
4. Report issue
If these suggestions don’t help and you want to report an issue to us, please attach the model, command/step, and the customized app (if any) with us to reproduce locally.
Thanks!
Hi,
You can try nvcr.io/nvidia/pytorch:24.12-py3-igpu which already has PyTorch preinstalled.
Please make sure the container with igpu or l4t tag.
Thanks.
Maybe this is just semantics, but what I think you want to install is nvidia-container-toolkit instead of nvidia-container-runtime.
The nvidia-container-toolkit docs webpage has a very good overview, installation method and examples of running dockerized gpu containers on Jetson.
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