nvidia-docker for your GPU application development
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We just finished installing DGX-1. Yayy!!
NVIDIA suggests the use of nvidia-docker
to develop and prototype GPU application on DGX-1. The reason is that many popular deep learning frameworks such as torch, mxnet, tensorflow, theano, caffe, CNTK, and DIGITS use specific version of NVIDIA driver, libraries, and configurations. Docker container provides hardware and software encapsulation, allowing multiple containers run on the same systems using their own specific configurations. nvidia-docker is a thin wrapper around docker command which enables portability in GPU-based images that use NVIDIA GPUs by providing driver agnostic CUDA images.
This post shows basic commands to get started using nvidia-docker for GPU application development. You need to have NVIDIA driver, Docker, and nvidia-docker
installed in your system. Check these links to install docker and nvidia-docker:
- Docker installation
- nvidia-docker installation
nvidia-docker
wrapper only modifies docker run
and create
commands. The other commands are just pass-through to the docker-cli. To list all docker images in your system:
Use nvidia-docker run
to run a container. The format is
nvidia-docker
run <docker-options> <image_name> command
Here are some examples:
In these examples, -it
is docker option to set an interactive connection and assign a terminal inside the new container, --rm
flag tells docker to automatically clean up and remove the container when it exits. Docker images which start with compute.nvidia.com/nvidia/
are official docker images from NVIDIA.
--rm
flag is useful for running short-term foreground processes. You can omit this option, if you want to keep a container when it exits for debugging and development. It is always advisable to name your container when you keep your container using --name
. To start your container again, use nvidia-docker start
.
If you write your python codes in jupyter notebook for your development, you can specify port mapping using -p
flag. You can also mount a directory in your host machine to the container using -v
flag. -w
flag indicates the location where the command will be executed
References:
- NVIDIA Docker: GPU Server Application Deployment Made Easy
- nvidia-docker wiki
- nvidia-docker for your GPU application development
- k8s调用gpu,nvidia-docker使用gpu
- docker︱在nvidia-docker中使用tensorflow-gpu/jupyter
- Nvidia-Docker安装使用 -- 可使用GPU的Docker容器
- 在Docker下使用Nvidia GPU进行计算
- docker使用cuda gpu的时候缺少nvidia-uvm
- 在Docker下使用Nvidia GPU进行计算
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