#cuda-go-devcontainer

Nvidia Jetson Orin Nano CUDA enabled Devcontainer along Go(lang) development

Check https://github.com/luisgizirian/cuda-image-builders

devcontainer.json

// For format details, see https://aka.ms/devcontainer.json.
// This implementation serves as basis. You can customize as needed.
{
	"name": "Jetson Orin Nano CUDA with Go",
	"dockerFile": "Dockerfile",
	
	// Features to add to the dev container. More info: https://containers.dev/features.
	"features": {
		"ghcr.io/devcontainers/features/go:1": {}
	},

	// Use 'forwardPorts' to make a list of ports inside the container available locally.
	// "forwardPorts": [],

	// Configure tool-specific properties.
	"customizations": {
			"vscode": {
            			"extensions": [
                			"golang.go"
            			]
			}
	},

	"remoteEnv": {
		"DISPLAY": "${localEnv:DISPLAY}"
	},

	"runArgs": [
		"--net=host",
		"--runtime",
		"nvidia"
	]
}

Dockerfile

# This implementation serves as basis. You can customize as needed.
# Use the official L4T base image from NVIDIA
FROM nvcr.io/nvidia/l4t-cuda:11.4.19-runtime

VOLUME /tmp/.X11-unix:/tmp/.X11-unix:rw

#
# Install any utils needed for execution
#
RUN apt-get update && apt-get install -y --no-install-recommends \
    sudo \
    && rm -rf /var/lib/apt/lists/* \
    && apt-get clean

#
# Install nvidia-cuda-dev for CUDA developer packages
# Use nvidia-cuda if need CUDA runtime only
#
RUN apt-get update && apt-get install -y --no-install-recommends \
    nvidia-cuda-dev \
    && rm -rf /var/lib/apt/lists/* \
    && apt-get clean

#Update libraries
RUN ldconfig 

#
# Setup environment variables
#
ENV CUDA_HOME="/usr/local/cuda"
ENV PATH="/usr/local/cuda/bin:$PATH"
ENV LD_LIBRARY_PATH="/usr/local/cuda/lib64:$LD_LIBRARY_PATH"