Refer to the Debian NVIDIA Installation Guide and Ubuntu NVIDIA Installation How-To for detailed instructions. Ensure that you have successfully installed and verified the drivers before proceeding to the next steps.
2. Install Docker on your system using the following commands:
Update package index: `apt update`
Install Docker using the docker.io package: `apt install docker.io`
Verify Docker’s status and version: `systemctl status docker; docker –version`
3. Set up NVIDIA Container Toolkit by executing these commands in sequence:
Download the GPG key: `curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey -o /tmp/nvidia-gpgkey`
Decompress and save it: `gpg –dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg /tmp/nvidia-gpgkey`
Download the NVIDIA container toolkit list file: `curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list -o /tmp/nvidia-list`
Modify the list file to include the signature: `sed ‘s#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g’ /tmp/nvidia-list > /etc/apt/sources.list.d/nvidia-container-toolkit.list`

Update the package database: `apt update`
4. Configure Docker to recognize and utilize NVIDIA GPUs by running this command: `nvidia-container-cli configure –runtime=docker`
5. Restart Docker daemon for changes to take effect: `systemctl restart docker`
6. Pull the specific NVIDIA CUDA image from Docker Hub using this command: `docker pull nvidia/cuda:12.2.0-base-ubuntu22.04`
7. Run the Docker container with GPU support by executing this command inside your terminal or shell: `docker run –gpus all -it nvidia/cuda:12.2.0-base-ubuntu22.04 bash`
8. Maintain your CUDA-enabled Docker environment by regularly checking for image updates, managing GPU resources judiciously when running multiple containers, backing up essential configurations, and actively engaging with the community for tips and best practices. Staying proactive will ensure optimal performance and security for your GPU-accelerated projects.
9. Troubleshoot any issues by revisiting setup steps to catch oversights, using tools like `nvidia-smi` inside your container to verify GPU accessibility, ensuring NVIDIA drivers are compatible with the CUDA version you’re deploying, and monitoring GPU usage to prevent bottlenecks.
10. Embracing the powerful synergy between NVIDIAs CUDA platform and Docker offers a robust and flexible environment for GPU-accelerated applications. By diligently following these steps and adhering to best practices, you’ll be well-prepared to harness the full potential of your GPU. As with any tech journey, continuous learning and engagement with the community will serve as valuable assets, ensuring that you remain at the forefront of GPU computing advancements.