一、docker使用宿主机硬件设备的三种方式
- 使用--privileged=true选项，以特权模式开启容器
- 使用--device选项
- 使用容器卷挂载-v选项
二、docker使用gpu方式演变
docker使用宿主机的gpu设备，本质是把宿主机使用gpu时调用的设备文件全部挂载到docker上。
nvidia提供了三种方式的演变，如下是官网的一些介绍
来自 <>
NVIDIA designed NVIDIA-Docker in 2016 to enable portability in Docker images that leverage NVIDIA GPUs. It allowed driver agnostic CUDA images and provided a Docker command line wrapper that mounted the user mode components of the driver and the GPU device files into the container at launch. Over the lifecycle of NVIDIA-Docker, we realized the architecture lacked flexibility for a few reasons: Tight integration with Docker did not allow support of other container technologies such as LXC, CRI-O, and other runtimes in the future We wanted to leverage other tools in the Docker ecosystem – e.g. Compose (for managing applications that are composed of multiple containers) Support GPUs as a first-class resource in orchestrators such as Kubernetes and Swarm Improve container runtime support for GPUs – esp. automatic detection of user-level NVIDIA driver libraries, NVIDIA kernel modules, device ordering, compatibility checks and GPU features such as graphics, video acceleration As a result, the redesigned NVIDIA-Docker moved the core runtime support for GPUs into a library called libnvidia-container. The library relies on Linux kernel primitives and is agnostic relative to the higher container runtime layers. This allows easy extension of GPU support into different container runtimes such as Docker, LXC and CRI-O. The library includes a command-line utility and also provides an API for integration into other runtimes in the future. The library, tools, and the layers we built to integrate into various runtimes are collectively called the NVIDIA Container Runtime. Since 2015, Docker has been donating key components of its container platform, starting with the Open Containers Initiative (OCI) specification and an implementation of the specification of a lightweight container runtime called runc. In late 2016, Docker also donated containerd, a daemon which manages the container lifecycle and wraps OCI/runc. The containerd daemon handles transfer of images, execution of containers (with runc), storage, and network management. It is designed to be embedded into larger systems such as Docker. More information on the project is available on the official site. Figure 1 shows how the libnvidia-container integrates into Docker, specifically at the runc layer. We use a custom OCI prestart hook called nvidia-container-runtime-hook to runc in order to enable GPU containers in Docker (more information about hooks can be found in the OCI runtime spec). The addition of the prestart hook to runc requires us to register a new OCI compatible runtime with Docker (using the –runtime option). At container creation time, the prestart hook checks whether the container is GPU-enabled (using environment variables) and uses the container runtime library to expose the NVIDIA GPUs to the container. Figure 1.Integration of NVIDIA Container Runtime with Docker
1、nvidia-docker
nvidia-docker是在docker的基础上做了一层封装
通过 nvidia-docker-plugin把硬件设备在docker的启动命令上添加必要的参数。
Ubuntu distributions # Install nvidia-docker and nvidia-docker-plugin wget -P /tmp https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.0-rc/nvidia-docker_1.0.0.rc-1_amd64.deb sudo dpkg -i /tmp/nvidia-docker_1.0.0.rc-1_amd64.deb && rm /tmp/nvidia-docker*.deb # Test nvidia-smi nvidia-docker run --rm nvidia/cuda nvidia-smi Other distributions # Install nvidia-docker and nvidia-docker-plugin wget -P /tmp https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.0-rc/nvidia-docker_1.0.0.rc_amd64.tar.xz sudo tar --strip-components=1 -C /usr/bin -xvf /tmp/nvidia-docker_1.0.0.rc_amd64.tar.xz && rm /tmp/nvidia-docker*.tar.xz # Run nvidia-docker-plugin sudo -b nohup nvidia-docker-plugin > /tmp/nvidia-docker.log # Test nvidia-smi nvidia-docker run --rm nvidia/cuda nvidia-smi Standalone install # Install nvidia-docker and nvidia-docker-plugin wget -P /tmp https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.0-rc/nvidia-docker_1.0.0.rc_amd64.tar.xz sudo tar --strip-components=1 -C /usr/bin -xvf /tmp/nvidia-docker_1.0.0.rc_amd64.tar.xz && rm /tmp/nvidia-docker*.tar.xz # One-time setup sudo nvidia-docker volume setup # Test nvidia-smi nvidia-docker run --rm nvidia/cuda nvidia-smi
2、nvidia-docker2
sudo apt-get install nvidia-docker2 sudo apt-get install nvidia-container-runtime sudo dockerd --add-runtime=nvidia=/usr/bin/nvidia-container-runtime [...]
3、nvidia-container-toolkit
docker版本在19.03及以上后
nvidia-container-toolkit进行了进一步的封装，在参数里直接使用--gpus "device=0" 即可
总结
以上为个人经验，希望能给大家一个参考，也希望大家多多支持。