Using GPUs for Cloudera Data Science Workbench Workloads

Perform this step on all nodes with GPU hardware installed on them.

1. Install the kernel-devel package.

   sudo yum install -y kernel-devel-`uname -r`

   If the previous command fails to find a matching version of the kernel-devel package, list all the kernel/kernel-devel versions that are available from the RHEL/CentOS package repositories, and pick the desired version to install.

   You can use a bash script as demonstrated here to do this:

   if ! yum install kernel-devel-`uname -r`; then 
     yum install -y kernel kernel-devel; retValue=$?
     if [ $retValue -eq 0]; then echo "Reboot is required since new version of kernel was installed"; fi
   fi

2. If you upgraded to a new kernel version in the previous step, run the following command to reboot.

   sudo reboot

3. Install the Development tools package.

   sudo yum groupinstall -y "Development tools"

Perform this step on all nodes with GPU hardware installed on them.

Cloudera Data Science Workbench does not ship with any of the NVIDIA drivers needed to enable GPUs for general purpose processing. System administrators are expected to install the version of the drivers that are compatible with the CUDA libraries that will be consumed on each node. Use the NVIDIA UNIX Driver archive to find out which driver is compatible with your GPU cards.

Use the NVIDIA UNIX Driver archive to find out which driver is compatible with your GPU card and operating system. Make sure the driver you select is also compatible with the nvidia-docker plugin we will be installing in the next step. See nvidia-docker installation prerequisites.

To download and install the NVIDIA driver, make sure you follow the instructions on the respective driver's download page.. It is crucial that you download the correct version.

wget http://us.download.nvidia.com/.../NVIDIA-Linux-x86_64-<driver_version>.run
export NVIDIA_DRIVER_VERSION=<driver_version>
chmod 755 ./NVIDIA-Linux-x86_64-$NVIDIA_DRIVER_VERSION.run
./NVIDIA-Linux-x86_64-$NVIDIA_DRIVER_VERSION.run -asq

/usr/bin/nvidia-smi

Perform this step on all nodes with GPU hardware installed on them.

To enable Docker containers to use the GPUs, the previously installed NVIDIA driver libraries must be consolidated in a single directory named after the <driver_version>, and mounted into the containers. This is done using the nvidia-docker package, which is a thin wrapper around the Docker CLI and a Docker plugin.

Minimum Required Cloudera Manager Role: Cluster Administrator

Test whether Cloudera Data Science Workbench can detect GPUs

Once Cloudera Data Science Workbench has successfully restarted, if NVIDIA drivers have been installed on the Cloudera Data Science Workbench hosts, Cloudera Data Science Workbench will now be able to detect the GPUs available on its hosts.

Additionally, the output of this command will also indicate that there are nodes with GPUs present.

cdsw status

The base engine image (docker.repository.cloudera.com/cdsw/engine:4) that ships with Cloudera Data Science Workbench 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 compatible versions of NVIDIA drivers 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/cdsw/engine:4

RUN NVIDIA_GPGKEY_SUM=d1be581509378368edeec8c1eb2958702feedf3bc3d17011adbf24efacce4ab5 && \
    NVIDIA_GPGKEY_FPR=ae09fe4bbd223a84b2ccfce3f60f4b3d7fa2af80 && \
    apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub && \
    apt-key adv --export --no-emit-version -a $NVIDIA_GPGKEY_FPR | tail -n +5 > cudasign.pub && \
    echo "$NVIDIA_GPGKEY_SUM  cudasign.pub" | sha256sum -c --strict - && rm cudasign.pub && \
    echo "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64 /" > /etc/apt/sources.list.d/cuda.list

ENV CUDA_VERSION 9.1.85
LABEL com.nvidia.cuda.version="${CUDA_VERSION}"

ENV CUDA_PKG_VERSION 9-1=$CUDA_VERSION-1
RUN apt-get update && apt-get install -y --no-install-recommends \
        cuda-cudart-$CUDA_PKG_VERSION && \
    ln -s cuda-9.1 /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

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 7.1.2.21
LABEL com.nvidia.cudnn.version="${CUDNN_VERSION}"

RUN apt-get update && apt-get install -y --no-install-recommends \
            libcudnn7=$CUDNN_VERSION-1+cuda9.1 && \
    apt-mark hold libcudnn7 && \
    rm -rf /var/lib/apt/lists/*

docker build --network host -t <company-registry>/cdsw-cuda:4 . -f cuda.Dockerfile

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

docker push <company-registry>/cdsw-cuda:4