docker-install VS nvidia-docker

curl -fsSL https://get.docker.com -o get-docker.sh sudo sh get-docker.sh # Install docker compose sudo curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose # Apply executable permissions to the binary sudo chmod +x /usr/local/bin/docker-compose # Run Project docker-compose up -d

root@ubuntu-16gb-hel1-1:~# curl -fsSL https://get.docker.com | sh - # Executing docker install script, commit: e5543d473431b782227f8908005543bb4389b8de + sh -c apt-get update -qq >/dev/null + sh -c DEBIAN_FRONTEND=noninteractive apt-get install -y -qq apt-transport-https ca-certificates curl >/dev/null + sh -c install -m 0755 -d /etc/apt/keyrings + sh -c curl -fsSL "https://download.docker.com/linux/ubuntu/gpg" | gpg --dearmor --yes -o /etc/apt/keyrings/docker.gpg + sh -c chmod a+r /etc/apt/keyrings/docker.gpg + sh -c echo "deb [arch=arm64 signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu jammy stable" > /etc/apt/sources.list.d/docker.list + sh -c apt-get update -qq >/dev/null + sh -c DEBIAN_FRONTEND=noninteractive apt-get install -y -qq docker-ce docker-ce-cli containerd.io docker-compose-plugin docker-ce-rootless-extras docker-buildx-plugin >/dev/null + sh -c docker version Client: Docker Engine - Community Version: 26.0.0 API version: 1.45 Go version: go1.21.8 Git commit: 2ae903e Built: Wed Mar 20 15:18:14 2024 OS/Arch: linux/arm64 Context: default Server: Docker Engine - Community Engine: Version: 26.0.0 API version: 1.45 (minimum version 1.24) Go version: go1.21.8 Git commit: 8b79278 Built: Wed Mar 20 15:18:14 2024 OS/Arch: linux/arm64 Experimental: false containerd: Version: 1.6.28 GitCommit: ae07eda36dd25f8a1b98dfbf587313b99c0190bb runc: Version: 1.1.12 GitCommit: v1.1.12-0-g51d5e94 docker-init: Version: 0.19.0 GitCommit: de40ad0 ================================================================================ To run Docker as a non-privileged user, consider setting up the Docker daemon in rootless mode for your user: dockerd-rootless-setuptool.sh install Visit https://docs.docker.com/go/rootless/ to learn about rootless mode. To run the Docker daemon as a fully privileged service, but granting non-root users access, refer to https://docs.docker.com/go/daemon-access/ WARNING: Access to the remote API on a privileged Docker daemon is equivalent to root access on the host. Refer to the 'Docker daemon attack surface' documentation for details: https://docs.docker.com/go/attack-surface/ ================================================================================ root@ubuntu-16gb-hel1-1:~# docker ps -a CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES

curl | sh is a bad practice, but lack of "curl https://get.docker.com | sh" scenario is certainly bad for Podman adoption.

sudo apt-get purge docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin docker-ce-rootless-extras sudo rm -rf /var/lib/docker sudo rm -rf /var/lib/containerd Install Docker curl -fsSL https://get.docker.com -o get-docker.sh sudo sh ./get-docker.sh

For Linux (using curl): curl -fsSL https://get.docker.com -o get-docker.sh && sh get-docker.sh

Install Docker (curl -sSL https://get.docker.com/ | sudo sh)

On WSL2, you just install Docker the same way as you would do on Linux. `sudo apt install docker.io` (from Debian/Ubuntu repo) or `curl https://get.docker.com | sh` (from Docker Inc). If you are using different WSL distribution, the standard way to install docker should work just fine. (e.g. `sudo pacman -S docker` works as expected on ArchWSL)

curl https://get.docker.com | bash -E

After all the resources are provisioned, I need to install Docker and pull a specific image. I will install Docker like this: curl -sSL https://get.docker.com | sh.

# 1. download the script # # $ curl -fsSL https://get.docker.com -o install-docker.sh # # 2. verify the script's content # # $ cat install-docker.sh # # 3. run the script with --dry-run to verify the steps it executes # # $ sh install-docker.sh --dry-run # # 4. run the script either as root, or using sudo to perform the installation. # # $ sudo sh install-docker.sh #

Nvidia Docker on GitHub

Here's where I found discussion regarding this https://github.com/NVIDIA/nvidia-docker/issues/1671

I found the Faq, looks like Windows isn't supported which might indicate why I had this problem earlier. I might need to dual boot my machine if it won't work with WSL which I don't see mentioned in either page. WSL Cuda instructions found this I'll give it a try.

Arc has linux kernel support from 6.0, been using an A770 with tdarr for a few months. Super solid and no issues like the nvidia docker toolkit just losing the GPU. The workaround doesn't hold for long - https://github.com/NVIDIA/nvidia-docker/issues/1730 It is one of the reasons I went with Intel over waiting for a low end 40 series. The other was I that basically stole it for $199. So far Plex is the only thing that doesn't work with Arc and all the HW transcoding falls on the iGPU. Knowing how they prioritize things nobody wants, Arc support and AV1 transcoding will be added when 16th gen Intel CPUs are released.

I use ubuntu with wsl2 under windows 11. I want to learn how to get GPU-accelerated computing working. My ASUS laptop has a NVIDIA graphics card as well as a built in Intel. Please ask me any needed questions and then tell me the steps I need to get this working? SOFTWARE DEVELOPER Thanks for sharing that information. Can you please confirm if you have already installed NVIDIA drivers on your Windows 11 system? If not, you'll need to install the appropriate NVIDIA drivers for your GPU in order to enable GPU acceleration. Additionally, what is your end goal is with GPU accelerated computing? yes I have the NVIDIA control panel installed. SOFTWARE DEVELOPER Great. To enable GPU acceleration in WSL2, you'll need to follow these steps: First, confirm you are running Windows 11 build 20145 or later, and have WSL2 installed and configured to use Ubuntu. Download the NVIDIA Linux driver for your GPU from the official NVIDIA Linux download page https://www.nvidia.com/Download/index.aspx. Install the latest NVIDIA driver, including the NVIDIA Docker plugin. You can follow instructions on the NVIDIA Docker plugin GitHub page (https://github.com/NVIDIA/nvidia-docker). Install the CUDA toolkit and cuDNN library. Once you've installed the NVIDIA driver successfully, you can download the CUDA toolkit and cuDNN library (if applicable) from the official NVIDIA CUDA download page https://developer.nvidia.com/cuda-downloads. Install the NVIDIA Container Toolkit. NVIDIA Container Toolkit is a nifty package that provides a set of tools for GPU-accelerated container deployments. You can find installation instructions and a getting started guide on the NVIDIA Container Toolkit GitHub page https://github.com/NVIDIA/nvidia-docker. Test the installation by running a TensorFlow or PyTorch script that uses the GPU for accelerated computing. Check if the GPU is recognized and being used in the output of the script. Once you've successfully followed these steps, you should have GPU-accelerated computing environments in WSL2. Hope that helps! Let me know if you have any other questions. yes to question #1. On #2, do I download the Game Ready driver or the Studio Driver. I don't play games as I'm a software devevloper but want accelerated graphics for development or data science purposes. SOFTWARE DEVELOPER Great to hear that you have WSL2 set up on your system. Regarding your question, you should download the Studio Driver version, as it's optimized for professional workloads, including data science and software development. The Game Ready version is optimized for gaming and might not always provide the best performance for non-gaming workloads. Once you have downloaded the Studio Driver for your GPU, follow the instructions provided by the driver installer to complete the installation process. Let me know if you have any further questions! Would you clarify #3? Is the NVIDIA Docker plugin included with the NVIDIA driver install? Or is it a separate install. I have Docker for Windows installed and working in WSL2. SOFTWARE DEVELOPER Sure, I'd be happy to clarify. The NVIDIA Docker plugin is an additional package that needs to be installed separately from the NVIDIA drivers. The plugin provides a convenient way for creating and running GPU-accelerated Docker containers in your system. To install the NVIDIA Docker plugin, follow the instructions on the NVIDIA Docker plugin GitHub page (https://github.com/NVIDIA/nvidia-docker). Note that the plugin is only compatible with Docker CE (Community Edition) or Docker EE (Enterprise Edition) on Linux-based systems, and does not work on Docker for Windows. Once you've installed the plugin, you can use it to run GPU-accelerated containers with Docker. It's worth noting that the NVIDIA Docker plugin requires access to your host system's NVIDIA GPU, so you'll need to ensure that your GPU has been correctly installed and recognized by your system. Hope that answers your question. Let me know if you have any additional questions!

Im trying to determine how to troubleshoot & resolve the HW transcoding, but based on my testing Im assuming its some change to the NVIDIA toolkit https://github.com/NVIDIA/nvidia-docker

NVIDIA Container Toolkit

So I am in the initial planning stages of setting up a new unraid server. Looking at picking up an SC846 24bay 4u chassis. I've got a Gigabyte Aorus mb with an AMD 5950x, 32gb of ddr4 (adding more as needed) and an nvidia 3070ti. I plan on getting an LSI 8i for the drives and leaves room for expansion server plans. My goal is to have plex setup via docker and utilize the gpu transcoding to offload the cpu work. I also want to setup vms or a vm server to essentially also have a "gaming server" mainly for me and the kids. This means down the road I would be adding another GPU to split up with other users. Im trying to allow for a max of 4 people while also still allowing plex to transcode as needed. Now I know there's other ways to do this but I dont feel like splitting this up into multiple systems unless I have to. So really just trying to see if this might be possible. My worry is that in order to make the gpu available to the plex docker I have to setup an nvidia container. https://github.com/NVIDIA/nvidia-docker