Container images

Using CUDA images

Description

CUDA images come in three flavors and are available through the NVIDIA public hub repository.

• base : starting from CUDA 9.0, contains the bare minimum (libcudart) to deploy a pre~built CUDA application. Use this image if you want to manually select which CUDA packages you want to install.

• runtime : extends the base image by adding all the shared libraries from the CUDA toolkit. Use this image if you have a pre-built application using multiple CUDA libraries.

• devel : extends the runtime image by adding the compiler toolchain, the debugging tools, the headers and the static libraries. Use this image to compile a CUDA application from sources.

Requirements

Running a CUDA container requires a machine with at least one CUDA-capable GPU and a driver compatible with the CUDA toolkit version you are using. The machine running the CUDA container only requires the NVIDIA driver , the CUDA toolkit doesn’t have to be installed.

NVIDIA drivers are backward-compatible with CUDA toolkits versions

CUDA toolkit version	Driver version	GPU architecture
6.5	>= 340.29	>= 2.0 (Fermi)
7.0	>= 346.46	>= 2.0 (Fermi)
7.5	>= 352.39	>= 2.0 (Fermi)
8.0	== 361.93 or >= 375.51	== 6.0 (P100)
8.0	>= 367.48	>= 2.0 (Fermi)
9.0	>= 384.81	>= 3.0 (Kepler)
9.1	>= 387.26	>= 3.0 (Kepler)
9.2	>= 396.26	>= 3.0 (Kepler)
10.0	>= 384.111, < 385.00	Tesla GPUs
10.0	>= 410.48	>= 3.0 (Kepler)
10.1	>= 384.111, < 385.00	Tesla GPUs
10.1	>=410.72, < 411.00	Tesla GPUs
10.1	>= 418.39	>= 3.0 (Kepler)

Examples

# Running an interactive CUDA session isolating the first GPU
docker run -ti --rm --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=0 nvidia/cuda

# Querying the CUDA 7.5 compiler version
docker run --rm --runtime=nvidia nvidia/cuda:7.5-devel nvcc --version

Tags available

Check the DockerHub Container images are available through the NVIDIA Docker Hub repository. The Dockerfiles are available on GitLab

Current supported distributions include:

• [DEPRECATED] Ubuntu 14.04 LTS

• Ubuntu 16.04 LTS

• Ubuntu 18.04 LTS

• CentOS 6

• CentOS 7

For more information about a specific image please refer to its section:

• Driver (Experimental)

• NVIDIA Caffe (Deprecated see using NGC images)

• DIGITS (Deprecated see using NGC images)

Using NGC images

From the official product page :

Featuring a comprehensive catalog of containers, including NVIDIA optimized deep learning frameworks, third-party managed HPC applications, and NVIDIA HPC visualization tools.

Optimized container images for deep learning are available on the NVIDIA container registry: https://www.nvidia.com/en-us/gpu-cloud/deep-learning-containers/

For any NGC related issue, please use the devtalk forums.

Using non-CUDA images

Setting --gpus all will enable GPU support for any container image:

docker run --gpus all  --rm debian:stretch nvidia-smi

Writing Dockerfiles

If the environment variables are set inside the Dockerfile, you don’t need to set them on the docker run command-line.

For instance, if you are creating your own custom CUDA container, you should use the following:

ENV NVIDIA_VISIBLE_DEVICES all
ENV NVIDIA_DRIVER_CAPABILITIES compute,utility

These environment variables are already set in our official images pushed to Docker Hub.

For a Dockerfile using the NVIDIA Video Codec SDK, you should use:

ENV NVIDIA_VISIBLE_DEVICES all
ENV NVIDIA_DRIVER_CAPABILITIES compute,video,utility