NVIDIA Caffe Docker Container for Deep Learning

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Use examples when they help make things clearer.

I want to ensure that my GPU is fully utilized for accelerated tasks, but I’m not sure where to start.

You’re an ethical computer science research assistant. Your role is to explain complex ideas in easy-to-understand English. Use examples when they help make things clearer.

Sure, I can provide you with a step-by-step guide on how to set up NVIDIA CUDA Toolkit within a Docker container for Debian and Ubuntu systems:

1. Install the necessary packages for running Docker containers on your system (if not already installed). For Debian, run `sudo apt update` followed by `sudo apt install docker.io`. For Ubuntu, use `sudo apt update && sudo apt install docker.io -y`.

2. Update the APT package index: `sudo apt update`

3. Install Docker using the `docker.io` package: `sudo apt install docker.io`

4. Verify that Docker is installed and running by checking its status with `systemctl status docker`. You should see output similar to this:


# Install Docker using the `docker.io` package
sudo apt install docker.io -y

# Update the APT package index
sudo apt update

# Verify that Docker is installed and running by checking its status with `systemctl status docker`
# This command checks the status of the Docker service and displays information about it
systemctl status docker

# The following output shows that Docker is installed and running
# `docker.service` is the name of the Docker service
# `Docker Application Container Engine` is the description of the service
# `Loaded` indicates that the service is loaded and enabled
# `Active` indicates that the service is currently running
# `Main PID` is the process ID of the main Docker process
# `CGroup` is the control group where the service is running
# `Docs` is a link to the official Docker documentation
# `--containerd` is the path to the containerd socket used by Docker
# `fd://` is the default socket for Docker communication
# `/run/containerd/containerd.sock` is the location of the containerd socket
docker.service Docker Application Container Engine
   Loaded: loaded (/lib/systemd/system/docker.service; enabled; vendor preset: enabled)
   Active: active (running) since Thu 2021-08-19 15:43:37 UTC; 6 days ago
     Docs: https://docs.docker.com
 Main PID: 12345 (dockerd)
   CGroup: /system.slice/docker.service
           12345 /usr/bin/dockerd -H fd:// --containerd=/run/containerd/containerd.sock

5. Install the NVIDIA Container Toolkit by following these steps (adapted from the official documentation):

a. Download the GPG key: `curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey -o /tmp/nvidia-gpgkey`

b. Import the key using `sudo gpg –dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg /tmp/nvidia-gpgkey`. This will add a new GPG key to your system’s trusted list of keys, which is required for verifying the authenticity of packages from NVIDIA’s repository.

c. Download the package list: `curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list -o /tmp/nvidia-list`

d. 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`

e. Update the package database: `sudo apt update`. This will ensure that your system has up-to-date information about available packages from NVIDIA’s repository.

6. Install the necessary packages for running CUDA containers using Docker by executing this command: `sudo nvidia-container-cli configure –runtime=nvidia`

7. Restart the Docker daemon to apply changes made in step 6: `sudo systemctl restart docker`. This will ensure that your new configuration is applied and that CUDA containers can be run using NVIDIA’s runtime.

8. Pull a specific NVIDIA CUDA image from Docker Hub by running this command (adapted from the official documentation): `docker pull nvidia/cuda:12.0-base-ubuntu20.04`

Note that you can replace “12.0” with a different version number depending on your needs, and “ubuntu20.04” with the appropriate operating system for your use case (e.g., “centos7”).

9. Run a Docker container using GPU resources by executing this command: `docker run –gpus all -it nvidia/cuda:12.0-base-ubuntu20.04 bash`

This will start an interactive shell inside the container, which you can use to test your CUDA installation and perform other tasks as needed. Note that “–gpus all” is used here to ensure that all available GPUs are assigned to this container (if multiple GPUs are present on your system).

10. Verify that GPU resources are being utilized by running the `nvidia-smi` command inside the Docker container: `sudo nvidia-smi`. This will display information about your NVIDIA GPUs, including their current utilization and status.

That’s it! You should now have a fully functional CUDA environment running in a Docker container on Debian or Ubuntu systems.

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