nvidia-docker VS pyenv-installer

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

To install it make sure that all prerequisites are met. Then use the pyenv-installer project like this to get the tool installed:

I tried the uninstaller from https://github.com/pyenv/pyenv-installer and I'm getting this.

After you’ve installed the build dependencies, you’re ready to install pyenv itself. I recommend using the pyenv-installer project:

For python, it's recommended to use pyenv, which allows you to install several versions of python at the same time with simple commands like this: pyenv install 3.9.13

Please don't contribute worthless and irrelevant comments like this. As you doubtless well know, piping from curl into bash is something that a large subset of respected programmers think is reasonable, and another rather tedious subset do not. For example, the entire Rust community clearly has a consensus that it's reasonable: https://rustup.rs/ As does homebrew https://brew.sh/ and pyenv https://github.com/pyenv/pyenv-installer#install to name whatever came to my mind in 30s thought.

Since the debate has such large numbers on both sides, your individual opinion on it is neither interesting nor germane.

name: Test, build and release # whenever a branch or commit is pushed on: [push] jobs: # use pytest test: # used to ensure testing is done right env: DEVELOPMENT: '1' runs-on: ubuntu-latest # to avoid using old sqlite version container: image: debian:latest options: --user root steps: # check out repo - uses: actions/checkout@v2 # prevent from asking user for input - run: export DEBIAN_FRONTEND=noninteractive # install recommended tools for building Python - run: apt -q update - run: apt -q install make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev git sqlite3 -y - run: apt -q upgrade -y # install pyenv - run: curl -L https://github.com/pyenv/pyenv-installer/raw/master/bin/pyenv-installer | bash - run: exec $SHELL - run: ~/.pyenv/bin/pyenv update # install and set up required Python - run: ~/.pyenv/bin/pyenv install 3.10.2 - run: ~/.pyenv/bin/pyenv virtualenv 3.10.2 npbc - run: ~/.pyenv/bin/pyenv local 3.10.2/envs/npbc # print version info (debugging) - run: ~/.pyenv/shims/python -V - run: ~/.pyenv/shims/python -c "import sqlite3; print(sqlite3.version)" # install pip packages - run: ~/.pyenv/shims/pip install -r requirements.txt pytest # run test - run: ~/.pyenv/shims/pytest -vv

curl -L https://github.com/pyenv/pyenv-installer/raw/master/bin/pyenv-installer | bash

This uses the pyenv-installer project to install pyenv on your system.