Hello, there is a NGC Pytorch container which has support for both arm64 and amd64. This should support the devkit with dGPU. For determining which version of the container to pull, we can reference Frameworks Support Matrix - NVIDIA Docs and the dGPU base image for the Holoscan Docker image (in v0.6) nvcr.io/nvidia/tensorrt:22.03-py3, so you could try nvcr.io/nvidia/pytorch:22.03-py3.
Hi,
I dont have Clara AGX on hand so I could not try what my thoughts. If I want to include Pytorch with dGPU in the holoscan docker image, should I replace the base image for it from nvcr.io/nvidia/tensorrt:22.03-py3 to nvcr.io/nvidia/pytorch:22.03-py3. Or should I add a new a line of FROM nvcr.io/nvidia/pytorch:22.03-py3? If I add FROM nvcr.io/nvidia/pytorch:22.03-py3 in the dockerfile, should I copy something by COPY ***? I am new to docker but trying to learn about it. Thanks in advance for your help.
Only if you want to build the SDK from source, you could replace the base image in the Dockerfile from FROM nvcr.io/nvidia/tensorrt:22.03-py3 to FROM nvcr.io/nvidia/pytorch:22.03-py3. However, please see section “Need more control over the SDK?” and “Attention” on when building from source is appropriate. If you want to build from source, you could reference this Dockerfile modified for v0.6, then please see GitHub instructions after changing the Dockerfile.
If not building the SDK from source, you could run the PyTorch container and install Holoscan SDK inside of it via the deb package or pip install, described in here. Please see the Overview on the NGC page for the docker run command. Note that the option --rm specifies Automatically remove the container when it exits in docker run | Docker Documentation.