Felix Abecassis edited this page Oct 3, 2018 · 17 revisions


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.


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 >= 384.130, < 385.00 Tesla GPUs
10.0 >= 410.48 >= 3.0 (Kepler)


# 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

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