nvidia-container-runtime
nvidia-container-runtime | container-images | |
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3 | 10 | |
1,089 | - | |
- | - | |
0.0 | - | |
over 1 year ago | - | |
Makefile | ||
Apache License 2.0 | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
nvidia-container-runtime
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Comparing 3 Docker container runtimes - Runc, gVisor and Kata Containers
Now you can even choose a runtime which creates a virtual machine or a container with a more secure isolation. Once there was a runtime for using an NVIDIA GPU called nvidia-container-runtime. That project is now deprecated and Docker has the "--gpus" option instead. Talking about GPUs is not the scope of this blogpost, but it is a good example of a special runtime that gave additional capabilities to containers.
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Can you add CUDA to a docker container?
Yes, you can, actually already exist images with Cuda installed https://hub.docker.com/r/nvidia/cuda . To be able to use the GPU device within the docker container you need to install `nvidia-container-runtime` https://github.com/NVIDIA/nvidia-container-runtime.
- "Unknown runtime specified nvidia" trying to configure Plex Docker container with GPU passthrough
container-images
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How to setup a free, self-hosted AI model for use with VS Code
Note you should select the NVIDIA Docker image that matches your CUDA driver version. Look in the unsupported list if your driver version is older.
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Accelerate Machine Learning Local Development and Test Workflows with Nvidia Docker
FROM tensorflow/tensorflow:1.15.5-gpu-py3 # Handle Nvidia public key update and update repositories for Ubuntu 18.x. #https://github.com/sangyun884/HR-VITON/issues/45 # reference: https://jdhao.github.io/2022/05/05/nvidia-apt-repo-public-key-error-fix/ RUN rm /etc/apt/sources.list.d/cuda.list RUN rm /etc/apt/sources.list.d/nvidia-ml.list RUN apt-key del 7fa2af80 # Additional reference: https://gitlab.com/nvidia/container-images/cuda/-/issues/158 RUN export this_distro="$(cat /etc/os-release | grep '^ID=' | awk -F'=' '{print $2}')" \ && export this_version="$(cat /etc/os-release | grep '^VERSION_ID=' | awk -F'=' '{print $2}' | sed 's/[^0-9]*//g')" \ && apt-key adv --fetch-keys "https://developer.download.nvidia.com/compute/cuda/repos/${this_distro}${this_version}/x86_64/3bf863cc.pub" \ && apt-key adv --fetch-keys "https://developer.download.nvidia.com/compute/machine-learning/repos/${this_distro}${this_version}/x86_64/7fa2af80.pub" # get the latest version of OpenCV RUN apt-get update && \ DEBIAN_FRONTEND=noninteractive \ apt-get install -y -qq \ wget git libopencv-dev RUN python -m pip install --upgrade pip && \ pip install matplotlib opencv-python==4.5.4.60 Pillow scipy \ azure-eventhub azure-eventhub-checkpointstoreblob-aio ipykernel WORKDIR /
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Run Playwright tests with hardware acceleration on a GPU-enabled EC2 instance with Docker support
As far as I can see, the way Google Chrome developers chose to support hardware acceleration under Linux is through Vulkan (here and here) According to Nvidia, there's no official support for Vulkan inside Docker. Although it seems that FAQ hasn't been updated because I was able to find a Docker container with Vulkan support here.
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CUDA 11.7 released with Ubuntu 22.04 support
Looking forward to the CUDA containers getting released!
- How to build ZED 2i Camera x ROS2 Foxy x Nvidia Jetson x Ubuntu 18.04 via Docker
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Running Nvidia drivers in Clear Linux or Flatcar?
That leaves Flatcar and Clear Linux - both of which happen to at least have documentation for installing/running Nvidia drivers and CUDA. Flatcar has this repository from Nvidia, and I've also found this project called forklift which will supposedly handle auto-updating the kernel modules for you. The Clear Linux docs also seem to include a method to auto-rebuild the modules with kernel upgrades, though it does say that the driver version needs to be updated manually, which honestly almost sounds preferable considering how finicky Nvidia drivers can be on Linux. Clear Linux also has several other tutorials/guides that appear to try and market it for things like machine learning, which leads me to believe that Nvidia gpus would hopefully work decently on it.
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Is it possible to install Nvidia drivers?
To add CUDA I plan on adding the stuff from this Docker script.
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Can you add CUDA to a docker container?
You can use the cuda dockerfile as reference: https://gitlab.com/nvidia/container-images/cuda/-/blob/master/Dockerfile
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KDE Development with Podman
However, getting Nvidia to work was much more complicated. Now, I am not a container expert, so a lot of it was because of my unfamiliarity with the technology. At first, I had to get nvidia-container-toolkit using CentOS package. The test containers given in the instructions here worked fine. However, I soon understood that nvidia-container-toolkit requires basing the image on nvidia official containers or going through this and figure out how to create a custom container. Most documentation online seemed to be about nvidia-docker or just covered the install portion of nvidia-container-toolkit. There was almost nothing available on how to create a custom image. After some digging around and copying and pasting (I still don't understand some of it), I was able to create a container with nvidia-smi, and other cuda commands working.
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Tensorflow build error
https://gitlab.com/nvidia/container-images/cuda/-/issues/109#note_503061879
What are some alternatives?
nvidia-docker - Build and run Docker containers leveraging NVIDIA GPUs
zed-docker - Docker images for the ZED SDK
diagnostics - Packages related to gathering, viewing, and analyzing diagnostics data from robots.
zed-ros2-wrapper - ROS 2 wrapper for the ZED SDK
IntelligentEdgeHOL - The IntelligentEdgeHOL walks through the process of deploying an Azure IoT Edge module to an Nvidia Jetson Nano device to allow for detection of objects in YouTube videos, RTSP streams, or an attached web cam
docker-cuda-demo
container-engine-accelerators - Collection of tools and examples for managing Accelerated workloads in Kubernetes Engine
coreos-assembler - Tooling container to assemble CoreOS-like systems
jetson-containers - Machine Learning Containers for NVIDIA Jetson and JetPack-L4T
HR-VITON - Official PyTorch implementation for the paper High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions (ECCV 2022).
flatcar-forklift - SystemD service to deploy always up-to-date kernel modules for Flatcar Container Linux