manylinux-cuda

manylinux docker images featuring an installation of the NVIDIA CUDA compiler, runtime and development libraries, designed specifically for building Python wheels with a C++/CUDA backend.

Download Images

Obtain the docker images from Docker Hub for the following CUDA versions:

X86_64 Architecture

• manylinux_2_34 on X86_64 arch with CUDA 12.6 (see on Dockerhub)

  docker pull sameli/manylinux_2_34_x86_64_cuda_12.6
  

• manylinux_2_28 on X86_64 arch with CUDA 12.3 (see on Dockerhub)

  docker pull sameli/manylinux_2_28_x86_64_cuda_12.3
  

• manylinux2014 on X86_64 arch with CUDA 12.3 (see on Dockerhub)

  docker pull sameli/manylinux2014_x86_64_cuda_12.3
  

• manylinux2014 on X86_64 arch with CUDA 12.0 (see on Dockerhub)

  docker pull sameli/manylinux2014_x86_64_cuda_12.0
  

• manylinux2014 on X86_64 arch with CUDA 11.8 (see on Dockerhub)

  docker pull sameli/manylinux2014_x86_64_cuda_11.8
  

• manylinux2014 on X86_64 arch with CUDA 10.2 (see on Dockerhub)

  docker pull sameli/manylinux2014_x86_64_cuda_10.2
  

AARCH64 Architecture

• manylinux_2_28 on AARCH64 arch with CUDA 12.3 (see on Dockerhub)

  docker pull sameli/manylinux_2_28_x86_64_cuda_12.3
  

• manylinux2014 on AARCH64 arch with CUDA 12.3 (see on Dockerhub)

  docker pull sameli/manylinux2014_x86_64_cuda_12.3
  

Base of Images

The docker images were built based on the following images:

• manylinux_2_34 on X86_64 architecture is based on: quay.io/pypa/manylinux_2_34_x86_64
• manylinux_2_28 on X86_64 architecture is based on: quay.io/pypa/manylinux_2_28_x86_64
• manylinux_2_28 on AARCH64 architecture is based on: quay.io/pypa/manylinux_2_28_aarch64
• manylinux2014 on X86_64 architecture is based on: quay.io/pypa/manylinux2014_x86_64
• manylinux2014 on AARCH64 architecture is based on: quay.io/pypa/manylinux2014_aarch64

What is Included

To maintain a minimal Docker image size, only the essential compilers and libraries from CUDA Toolkit are included. These include:

• CUDA compiler: cuda-crt, cuda-cuobjdump, cuda-cuxxfilt, cuda-nvcc, cuda-nvprune, cuda-nvvm, cuda-cudart, cuda-nvrtc, cuda-opencl,
• CUDA libraries: libcublas, libcufft, libcufile, libcurand, libcusolver, libcusparse, libnpp, libnvjitlink, libnvjpeg
• CUDA development libraries: cuda-cccl, cuda-cudart-devel, cuda-driver-devel, cuda-nvrtc-devel, cuda-opencl-devel, cuda-profiler-api, libcublas-devel, libcufft-devel, libcufile-devel, libcurand-devel, libcusolver-devel, libcusparse-devel, libnpp-devel, libnvjitlink-devel, libnvjpeg-devel

If you need additional packages from CUDA toolkit to be included in the images, please feel free to create a GitHub issue.

NVIDIA Driver

The Docker images do not include the NVIDIA driver to prevent incompatibility issues with the host system's native driver when used at runtime.

For users who might need specific components of the NVIDIA driver, such as libcuda.so, to compile their code, there are two options:

1. Use the Host's Native Driver: Add the --gpus all flag to your docker run command to enable the container to utilize the host’s GPU and driver (see Use Host's GPU for details). This is the recommended approach as it avoids compatibility issues between the container's and host's drivers.

2. Install the Driver in the Container: If necessary, the driver can be installed within the container using the following commands, based on your image's base distribution:

   • For manylinux_2 images:

     dnf -y install epel-release
     dnf -y module install nvidia-driver:latest-dkms
     

   • For manylinux2014 images:

     yum install nvidia-driver-latest-dkms
     

   Note, however, that this step should generally be avoided unless strictly required, as it may lead to compatibility issues between the driver versions in the container and on the host system. If possible, it is recommended to rely on the host system's driver installation when running containers that require GPU access.

Environment Variables

The following environment variables are defined:

• PATH=/usr/local/cuda/bin:${PATH}
• LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH}
• CUDA_HOME=/usr/local/cuda
• CUDA_ROOT=/usr/local/cuda
• CUDA_PATH=/usr/local/cuda
• CUDADIR=/usr/local/cuda

Run Containers

Run containers in interactive mode by:

docker run -it sameli/manylinux_2_28_x86_64_cuda_12.3

Check CUDA Version

The nvcc executable is available on the PATH. To check the CUDA version, execute:

docker run -t sameli/manylinux_2_28_x86_64_cuda_12.3 nvcc --version

The output of the above command is:

Copyright (c) 2005-2022 NVIDIA Corporation
Built on Mon_Oct_24_19:12:58_PDT_2022
Cuda compilation tools, release 12.0, V12.0.76
Build cuda_12.3.r12.0/compiler.31968024_0

Use Host's GPU

The primary purpose of these Docker images is to build code, such as Python wheels, using the manylinux standard. While this process does not require access to the host's GPU, you might want to use them at runtime on the host's GPU, particularly for testing purposes.

To access host's GPU device from the container, install NVIDIA Container Toolkit as follows.

1. Add the package to the repository:

   distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
   curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
   curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
   

2. Install nvidia-contaner-toolkit by:

   sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
   

3. Restart docker to be able to use it:

   sudo systemctl restart docker
   

To use host's GPU, add --gpus all to any of the docker commands given before, such as:

docker run --gpus all -it sameli/manylinux_2_28_x86_64_cuda_12.3

To check the host's NVIDIA driver version, CUDA runtime library version, and list of available GPU devices, run nvida-smi command, such as by:

docker run --gpus all sameli/manylinux_2_28_x86_64_cuda_12.3 nvidia-smi

Troubleshooting

No Space Left on Device

When running the docker containers in GitHub action, you may encounter this error:

no space left on device.

To resolve this, try clearing the GitHub's runner cache before executing the docker container:

- name: Clear Cache
  run: rm -rf /opt/hostedtoolcache

Driver Conflict

If you run the container with --gpus all to access the host's GPU, conflicts may arise if you also install an NVIDIA driver within the container. This typically does not cause problems until you attempt to use the driver, such as by commands like nvidia-smi inside the container, which can lead to errors due to driver conflicts. To resolve this, ensure you use only one driver source. You can either rely solely on the host's driver by not installing a separate driver in the container, or refrain from using the host's GPU if you intend to install a driver in the container.

Other CUDA Versions

To request a docker image for a specific CUDA version or architecture, feel free to create a GitHub issue.

License