Custom CUDA-capable Engine Image

The base engine image (docker.repository.cloudera.com/CML/engine:<version>) that ships with Cloudera Machine Learning will need to be extended with CUDA libraries to make it possible to use GPUs in jobs and sessions.

The following sample Dockerfile illustrates an engine on top of which machine learning frameworks such as Tensorflow and PyTorch can be used. This Dockerfile uses a deep learning library from NVIDIA called NVIDIA CUDA Deep Neural Network (cuDNN). For detailed information about compatibility between NVIDIA driver versions and CUDA, refer the cuDNN installation guide (prerequisites).

Make sure you also check with the machine learning framework that you intend to use in order to know which version of cuDNN is needed. As an example, Tensorflow's NVIDIA hardware and software requirements for GPU support are listed in the Tensorflow documentation here. Additionally, the Tensorflow version compatibility matrix for CUDA and cuDNN is documented here..

The following sample Dockerfile uses NVIDIA's official Dockerfiles for CUDA and cuDNN images.

cuda.Dockerfile


    FROM docker.repository.cloudera.com/CML/engine:13
    
    RUN apt-get update && apt-get install -y --no-install-recommends \
    gnupg2 curl ca-certificates && \
    curl -fsSL https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub | apt-key add - && \
    echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 /" > /etc/apt/sources.list.d/cuda.list && \
    echo "deb https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 /" > /etc/apt/sources.list.d/nvidia-ml.list && \
    apt-get purge --autoremove -y curl && \
    rm -rf /var/lib/apt/lists/*
    
    ENV CUDA_VERSION 10.1.243
    LABEL com.nvidia.cuda.version="${CUDA_VERSION}"
    
    ENV CUDA_PKG_VERSION 10-1=$CUDA_VERSION-1
    RUN apt-get update && apt-get install -y --no-install-recommends \
    cuda-cudart-$CUDA_PKG_VERSION && \
    cuda-libraries-$CUDA_PKG_VERSION && \
    ln -s cuda-10.1 /usr/local/cuda && \
    rm -rf /var/lib/apt/lists/*
    
    RUN echo "/usr/local/cuda/lib64" >> /etc/ld.so.conf.d/cuda.conf && \
    ldconfig
    
    RUN echo "/usr/local/nvidia/lib" >> /etc/ld.so.conf.d/nvidia.conf && \
    echo "/usr/local/nvidia/lib64" >> /etc/ld.so.conf.d/nvidia.conf
    
    ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
    ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda-10.2/targets/x86_64-linux/lib/
    
    RUN echo "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64 /" > /etc/apt/sources.list.d/nvidia-ml.list
    
    ENV CUDNN_VERSION 7.6.5.32
    LABEL com.nvidia.cudnn.version="${CUDNN_VERSION}"
    
    RUN apt-get update && apt-get install -y --no-install-recommends \
    libcudnn7=$CUDNN_VERSION-1+cuda10.1 && \
    apt-mark hold libcudnn7 && \
    rm -rf /var/lib/apt/lists/*
    
   

Use the following example command to build the custom engine image using the cuda.Dockerfile command:

docker build --network host -t <company-registry>/CML-cuda:13 . -f cuda.Dockerfile

Push this new engine image to a public Docker registry so that it can be made available for Cloudera Machine Learning workloads. For example:

docker push <company-registry>/CML-cuda:13