Memasang GPU ke cluster

Dataproc memberikan kemampuan bagi unit pemrosesan grafis (GPU) untuk dihubungkan ke node Compute Engine master dan pekerja di cluster Dataproc. Anda dapat menggunakan GPU ini untuk mempercepat beban kerja tertentu pada instance Anda, seperti machine learning dan pemrosesan data.

Untuk mengetahui informasi selengkapnya tentang hal yang dapat Anda lakukan dengan GPU dan jenis hardware GPU yang tersedia, baca GPU di Compute Engine.

Tidak ada biaya harga Dataproc tambahan yang ditambahkan ke biaya Compute Engine untuk GPU yang digunakan di cluster Dataproc.

Sebelum memulai

• GPU memerlukan driver dan software khusus. Item ini belum diinstal sebelumnya di cluster Dataproc.
• Baca harga GPU di Compute Engine untuk memahami biaya penggunaan GPU di instance Anda.
• Baca artikel pembatasan untuk instance dengan GPU guna mempelajari perbedaan fungsi instance ini dengan instance non-GPU.
• Periksa halaman kuota project Anda untuk memastikan bahwa Anda memiliki kuota GPU yang memadai (NVIDIA_K80_GPUS, NVIDIA_P100_GPUS, atau NVIDIA_V100_GPUS) yang tersedia di project Anda. Jika GPU tidak tercantum di halaman kuota atau Anda memerlukan kuota GPU tambahan, minta penambahan kuota.

Jenis GPU

Node Dataproc mendukung jenis GPU berikut. Anda harus menentukan jenis GPU saat menambahkan GPU ke cluster Dataproc.

• nvidia-tesla-l4 - NVIDIA® Tesla® L4
• nvidia-tesla-a100 - NVIDIA® Tesla® A100
• nvidia-tesla-k80 - NVIDIA® Tesla® K80
• nvidia-tesla-p100 - NVIDIA® Tesla® P100
• nvidia-tesla-v100 - NVIDIA® Tesla® V100
• nvidia-tesla-p4 - NVIDIA® Tesla® P4
• nvidia-tesla-t4 - NVIDIA® Tesla® T4
• nvidia-tesla-p100-vws - Workstation Virtual NVIDIA® Tesla® P100
• nvidia-tesla-p4-vws - Workstation Virtual NVIDIA® Tesla® P4
• nvidia-tesla-t4-vws - Workstation Virtual NVIDIA® Tesla® T4

Memasang GPU ke cluster

gcloud

Pasang GPU ke node pekerja master, primer, dan sekunder di cluster Dataproc saat membuat cluster menggunakan flag ‑‑master-accelerator, ‑‑worker-accelerator, dan ‑‑secondary-worker-accelerator. Flag ini menggunakan dua nilai berikut:

1. jenis GPU yang terpasang pada sebuah {i>node<i}, dan
2. jumlah GPU yang dipasang ke {i>node<i}.

Jenis GPU diperlukan, dan jumlah GPU bersifat opsional (defaultnya adalah 1 GPU).

Contoh:

gcloud dataproc clusters create cluster-name \
    --region=region \
    --master-accelerator type=nvidia-tesla-k80 \
    --worker-accelerator type=nvidia-tesla-k80,count=4 \
    --secondary-worker-accelerator type=nvidia-tesla-k80,count=4 \
    ... other flags

Untuk menggunakan GPU di cluster, Anda harus menginstal driver GPU.

REST API

Lampirkan GPU ke node pekerja master, primer, dan sekunder di cluster Dataproc dengan mengisi kolom acceleratorTypeUri dan acceleratorCount InstanceGroupConfig.AcceleratorConfig sebagai bagian dari permintaan API cluster.create.

Konsol

Klik PLATFORM CPU DAN GPU→GPU→TAMBAHKAN GPU di bagian node master dan worker panel Konfigurasi node di halaman Buat cluster di Konsol Google Cloud untuk menentukan jumlah GPU dan jenis GPU untuk node.

Menginstal driver GPU

Driver GPU diperlukan untuk menggunakan GPU apa pun yang terpasang ke node Dataproc. Anda dapat menginstal driver GPU dengan mengikuti petunjuk untuk tindakan inisialisasi ini, yang tercantum di bawah.

#!/bin/bash
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# This script installs NVIDIA GPU drivers and collects GPU utilization metrics.

set -euxo pipefail

function remove_old_backports {
  # This script uses 'apt-get update' and is therefore potentially dependent on
  # backports repositories which have been archived.  In order to mitigate this
  # problem, we will remove any reference to backports repos older than oldstable

  # https://github.com/GoogleCloudDataproc/initialization-actions/issues/1157
  oldstable=$(curl -s https://deb.debian.org/debian/dists/oldstable/Release | awk '/^Codename/ {print $2}');
  stable=$(curl -s https://deb.debian.org/debian/dists/stable/Release | awk '/^Codename/ {print $2}');

  matched_files="$(grep -rsil '\-backports' /etc/apt/sources.list*)"
  if [[ -n "$matched_files" ]]; then
    for filename in "$matched_files"; do
      grep -e "$oldstable-backports" -e "$stable-backports" "$filename" || \
        sed -i -e 's/^.*-backports.*$//' "$filename"
    done
  fi
}

function compare_versions_lte {
  [ "$1" = "$(echo -e "$1\n$2" | sort -V | head -n1)" ]
}

function compare_versions_lt() {
  [ "$1" = "$2" ] && return 1 || compare_versions_lte $1 $2
}

function get_metadata_attribute() {
  local -r attribute_name=$1
  local -r default_value=$2
  /usr/share/google/get_metadata_value "attributes/${attribute_name}" || echo -n "${default_value}"
}

OS_NAME=$(lsb_release -is | tr '[:upper:]' '[:lower:]')
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
readonly OS_NAME

# node role
ROLE="$(/usr/share/google/get_metadata_value attributes/dataproc-role)"
readonly ROLE

# CUDA version and Driver version
# https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html
readonly -A DRIVER_FOR_CUDA=([10.1]="418.88"    [10.2]="440.64.00"
          [11.0]="450.51.06" [11.1]="455.45.01" [11.2]="460.73.01"
          [11.5]="495.29.05" [11.6]="510.47.03" [11.7]="515.65.01"
          [11.8]="520.56.06")
readonly -A CUDNN_FOR_CUDA=( [10.1]="7.6.4.38"  [10.2]="7.6.5.32"
          [11.0]="8.0.4.30"  [11.1]="8.0.5.39"  [11.2]="8.1.1.33"
          [11.5]="8.3.3.40"  [11.6]="8.4.1.50"  [11.7]="8.5.0.96"
          [11.8]="8.6.0.163")
readonly -A NCCL_FOR_CUDA=(  [10.1]="2.4.8"     [10.2]="2.5.6"
          [11.0]="2.7.8"     [11.1]="2.8.3"     [11.2]="2.8.3"
          [11.5]="2.11.4"    [11.6]="2.11.4"    [11.7]="2.12.12"
          [11.8]="2.15.5")
readonly -A CUDA_SUBVER=(    [10.1]="10.1.243"  [10.2]="10.2.89"
          [11.0]="11.0.3"    [11.1]="11.1.0"    [11.2]="11.2.2"
          [11.5]="11.5.2"    [11.6]="11.6.2"    [11.7]="11.7.1"
          [11.8]="11.8.0")

RUNTIME=$(get_metadata_attribute 'rapids-runtime' 'SPARK')
DEFAULT_CUDA_VERSION='11.2'
if [[ ${DATAPROC_IMAGE_VERSION} == 2.* ]] && [[ "${RUNTIME}" == "SPARK" ]]; then
  DEFAULT_CUDA_VERSION='11.5'
fi
readonly DEFAULT_CUDA_VERSION
readonly CUDA_VERSION=$(get_metadata_attribute 'cuda-version' "${DEFAULT_CUDA_VERSION}")
readonly DEFAULT_NVIDIA_DEBIAN_GPU_DRIVER_VERSION=${DRIVER_FOR_CUDA["${CUDA_VERSION}"]}
readonly NVIDIA_DEBIAN_GPU_DRIVER_VERSION=$(get_metadata_attribute 'gpu-driver-version' ${DEFAULT_NVIDIA_DEBIAN_GPU_DRIVER_VERSION})
readonly NVIDIA_DEBIAN_GPU_DRIVER_VERSION_PREFIX=${NVIDIA_DEBIAN_GPU_DRIVER_VERSION%%.*}
readonly DRIVER=${NVIDIA_DEBIAN_GPU_DRIVER_VERSION_PREFIX}
# As of Rocky 8.7, kernel 4.18.0-425 is unable to build older nvidia kernel drivers
if [[ "${OS_NAME}" == "rocky" &&  "${DRIVER}" < "510" ]]; then
  readonly ROCKY_BINARY_INSTALL="true"
fi

# Fail early for configurations known to be unsupported
function unsupported_error {
  echo "Unsupported kernel driver on ${distribution}: '${DRIVER}'"
  exit -1
}
if [[ "${OS_NAME}" == "rocky" ]]; then
  KERNEL_SUBVERSION=$(uname -r | awk -F- '{print $2}')
  if [[ "${DRIVER}" < "460" && "${DRIVER}" != "450"
     && "${KERNEL_SUBVERSION%%.*}" > "305" ]]; then
    unsupported_error
  fi
elif [[ "${OS_NAME}" == "debian" ]]; then
  KERNEL_VERSION=$(uname -r | awk -F- '{print $1}')
  if [[ "${DRIVER}" < "455"
     && $(echo "${KERNEL_VERSION%.*} > 5.7" | bc -l) == 1  ]]; then
    unsupported_error
  fi
fi

DEFAULT_NCCL_VERSION=${NCCL_FOR_CUDA["${CUDA_VERSION}"]}
if [[ "${OS_NAME}" == "rocky" ]] \
   && (compare_versions_lte "${DEFAULT_NCCL_VERSION}" "2.8.4") ; then
  DEFAULT_NCCL_VERSION="2.8.4"
fi
readonly DEFAULT_NCCL_VERSION
readonly NCCL_VERSION=$(get_metadata_attribute 'nccl-version' ${DEFAULT_NCCL_VERSION})

# Parameters for NVIDIA-provided Debian GPU driver
DEFAULT_NVIDIA_DEBIAN_GPU_DRIVER_URL="https://download.nvidia.com/XFree86/Linux-x86_64/${NVIDIA_DEBIAN_GPU_DRIVER_VERSION}/NVIDIA-Linux-x86_64-${NVIDIA_DEBIAN_GPU_DRIVER_VERSION}.run"
if [[ "$(curl -s -I ${DEFAULT_NVIDIA_DEBIAN_GPU_DRIVER_URL} | head -1 | awk '{print $2}')" != "200" ]]; then
  DEFAULT_NVIDIA_DEBIAN_GPU_DRIVER_URL="https://download.nvidia.com/XFree86/Linux-x86_64/${NVIDIA_DEBIAN_GPU_DRIVER_VERSION%.*}/NVIDIA-Linux-x86_64-${NVIDIA_DEBIAN_GPU_DRIVER_VERSION%.*}.run"
fi
readonly DEFAULT_NVIDIA_DEBIAN_GPU_DRIVER_URL

NVIDIA_DEBIAN_GPU_DRIVER_URL=$(get_metadata_attribute 'gpu-driver-url' "${DEFAULT_NVIDIA_DEBIAN_GPU_DRIVER_URL}")
readonly NVIDIA_DEBIAN_GPU_DRIVER_URL

readonly NVIDIA_BASE_DL_URL='https://developer.download.nvidia.com/compute'

# Parameters for NVIDIA-provided NCCL library
readonly DEFAULT_NCCL_REPO_URL="${NVIDIA_BASE_DL_URL}/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb"
NCCL_REPO_URL=$(get_metadata_attribute 'nccl-repo-url' "${DEFAULT_NCCL_REPO_URL}")
readonly NCCL_REPO_URL
readonly NCCL_REPO_KEY="${NVIDIA_BASE_DL_URL}/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub"

readonly -A DEFAULT_NVIDIA_DEBIAN_CUDA_URLS=(
  [10.1]="${NVIDIA_BASE_DL_URL}/cuda/10.1/Prod/local_installers/cuda_10.1.243_418.87.00_linux.run"
  [10.2]="${NVIDIA_BASE_DL_URL}/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run"
  [11.0]="${NVIDIA_BASE_DL_URL}/cuda/11.0.3/local_installers/cuda_11.0.3_450.51.06_linux.run"
  [11.1]="${NVIDIA_BASE_DL_URL}/cuda/11.1.0/local_installers/cuda_11.1.0_455.23.05_linux.run"
  [11.2]="${NVIDIA_BASE_DL_URL}/cuda/11.2.2/local_installers/cuda_11.2.2_460.32.03_linux.run"
  [11.5]="${NVIDIA_BASE_DL_URL}/cuda/11.5.2/local_installers/cuda_11.5.2_495.29.05_linux.run"
  [11.6]="${NVIDIA_BASE_DL_URL}/cuda/11.6.2/local_installers/cuda_11.6.2_510.47.03_linux.run"
  [11.7]="${NVIDIA_BASE_DL_URL}/cuda/11.7.1/local_installers/cuda_11.7.1_515.65.01_linux.run"
  [11.8]="${NVIDIA_BASE_DL_URL}/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run")
readonly DEFAULT_NVIDIA_DEBIAN_CUDA_URL=${DEFAULT_NVIDIA_DEBIAN_CUDA_URLS["${CUDA_VERSION}"]}
NVIDIA_DEBIAN_CUDA_URL=$(get_metadata_attribute 'cuda-url' "${DEFAULT_NVIDIA_DEBIAN_CUDA_URL}")
readonly NVIDIA_DEBIAN_CUDA_URL

# Parameters for NVIDIA-provided Ubuntu GPU driver
readonly NVIDIA_UBUNTU_REPO_URL="${NVIDIA_BASE_DL_URL}/cuda/repos/ubuntu1804/x86_64"
readonly NVIDIA_UBUNTU_REPO_KEY_PACKAGE="${NVIDIA_UBUNTU_REPO_URL}/cuda-keyring_1.0-1_all.deb"
readonly NVIDIA_UBUNTU_REPO_CUDA_PIN="${NVIDIA_UBUNTU_REPO_URL}/cuda-ubuntu1804.pin"

# Parameter for NVIDIA-provided Rocky Linux GPU driver
readonly NVIDIA_ROCKY_REPO_URL="${NVIDIA_BASE_DL_URL}/cuda/repos/rhel8/x86_64/cuda-rhel8.repo"

# Parameters for NVIDIA-provided CUDNN library
DEFAULT_CUDNN_VERSION=${CUDNN_FOR_CUDA["${CUDA_VERSION}"]}
if [[ "${OS_NAME}" == "rocky" ]] \
   && (compare_versions_lte "${DEFAULT_CUDNN_VERSION}" "8.0.5.39") ; then
  DEFAULT_CUDNN_VERSION="8.0.5.39"
fi
readonly DEFAULT_CUDNN_VERSION
readonly CUDNN_VERSION=$(get_metadata_attribute 'cudnn-version' "${DEFAULT_CUDNN_VERSION}")
CUDNN_TARBALL="cudnn-${CUDA_VERSION}-linux-x64-v${CUDNN_VERSION}.tgz"
CUDNN_TARBALL_URL="${NVIDIA_BASE_DL_URL}/redist/cudnn/v${CUDNN_VERSION%.*}/${CUDNN_TARBALL}"
if ( compare_versions_lte "8.3.1.22" "${CUDNN_VERSION}" ); then
  CUDNN_TARBALL="cudnn-linux-x86_64-${CUDNN_VERSION}_cuda${CUDA_VERSION%.*}-archive.tar.xz"
  if ( compare_versions_lte "${CUDNN_VERSION}" "8.4.1.50" ); then
    CUDNN_TARBALL="cudnn-linux-x86_64-${CUDNN_VERSION}_cuda${CUDA_VERSION}-archive.tar.xz"
  fi
  CUDNN_TARBALL_URL="${NVIDIA_BASE_DL_URL}/redist/cudnn/v${CUDNN_VERSION%.*}/local_installers/${CUDA_VERSION}/${CUDNN_TARBALL}"
fi
readonly CUDNN_TARBALL
readonly CUDNN_TARBALL_URL

# Whether to install NVIDIA-provided or OS-provided GPU driver
GPU_DRIVER_PROVIDER=$(get_metadata_attribute 'gpu-driver-provider' 'NVIDIA')
readonly GPU_DRIVER_PROVIDER

# Stackdriver GPU agent parameters
readonly GPU_AGENT_REPO_URL='https://raw.githubusercontent.com/GoogleCloudPlatform/ml-on-gcp/master/dlvm/gcp-gpu-utilization-metrics'
# Whether to install GPU monitoring agent that sends GPU metrics to Stackdriver
INSTALL_GPU_AGENT=$(get_metadata_attribute 'install-gpu-agent' 'false')
readonly INSTALL_GPU_AGENT

# Dataproc configurations
readonly HADOOP_CONF_DIR='/etc/hadoop/conf'
readonly HIVE_CONF_DIR='/etc/hive/conf'
readonly SPARK_CONF_DIR='/etc/spark/conf'

NVIDIA_SMI_PATH='/usr/bin'
MIG_MAJOR_CAPS=0
IS_MIG_ENABLED=0

function execute_with_retries() {
  local -r cmd=$1
  for ((i = 0; i < 10; i++)); do
    if eval "$cmd"; then
      return 0
    fi
    sleep 5
  done
  return 1
}

function install_nvidia_nccl() {
  local -r nccl_version="${NCCL_VERSION}-1+cuda${CUDA_VERSION}"

  if [[ ${OS_NAME} == rocky ]]; then
    execute_with_retries "dnf -y -q install libnccl-${nccl_version} libnccl-devel-${nccl_version} libnccl-static-${nccl_version}"
  elif [[ ${OS_NAME} == ubuntu ]] || [[ ${OS_NAME} == debian ]]; then
    curl -fsSL --retry-connrefused --retry 10 --retry-max-time 30 "${NCCL_REPO_KEY}" | apt-key add -

    local tmp_dir
    tmp_dir=$(mktemp -d -t gpu-init-action-nccl-XXXX)

    curl -fsSL --retry-connrefused --retry 10 --retry-max-time 30 \
      "${NCCL_REPO_URL}" -o "${tmp_dir}/nvidia-ml-repo.deb"
    dpkg -i "${tmp_dir}/nvidia-ml-repo.deb"

    execute_with_retries "apt-get update"

    execute_with_retries \
      "apt-get install -y --allow-unauthenticated libnccl2=${nccl_version} libnccl-dev=${nccl_version}"
  else
    echo "Unsupported OS: '${OS_NAME}'"
    exit 1
  fi
}

function install_nvidia_cudnn() {
  local major_version
  major_version="${CUDNN_VERSION%%.*}"
  local cudnn_pkg_version
  cudnn_pkg_version="${CUDNN_VERSION}-1+cuda${CUDA_VERSION}"

  if [[ ${OS_NAME} == rocky ]]; then
    if [[ ${major_version} == 8 ]]; then
      execute_with_retries "dnf -y -q install libcudnn8-${cudnn_pkg_version} libcudnn8-devel-${cudnn_pkg_version}"
    else
      echo "Unsupported CUDNN version: '${CUDNN_VERSION}'"
      exit 1
    fi
  elif [[ ${OS_NAME} == ubuntu ]]; then
    local -a packages
    packages=(
      "libcudnn${major_version}=${cudnn_pkg_version}"
      "libcudnn${major_version}-dev=${cudnn_pkg_version}")
    execute_with_retries \
      "apt-get install -y --no-install-recommends ${packages[*]}"
  elif [[ ${OS_NAME} == debian ]]; then
    local tmp_dir
    tmp_dir=$(mktemp -d -t gpu-init-action-cudnn-XXXX)

    curl -fSsL --retry-connrefused --retry 10 --retry-max-time 30 \
      "${CUDNN_TARBALL_URL}" -o "${tmp_dir}/${CUDNN_TARBALL}"

    if ( compare_versions_lte "${CUDNN_VERSION}" "8.3.0.98" ); then
      tar -xzf "${tmp_dir}/${CUDNN_TARBALL}" -C /usr/local
    else
      ln -sf /usr/local/cuda/targets/x86_64-linux/lib /usr/local/cuda/lib
      tar -h --no-same-owner --strip-components=1 \
        -xJf "${tmp_dir}/${CUDNN_TARBALL}" -C /usr/local/cuda
    fi

    cat <<'EOF' >>/etc/profile.d/cudnn.sh
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH}
EOF
  else
    echo "Unsupported OS: '${OS_NAME}'"
    exit 1
  fi

  ldconfig

  echo "NVIDIA cuDNN successfully installed for ${OS_NAME}."
}

# Install NVIDIA GPU driver provided by NVIDIA
function install_nvidia_gpu_driver() {
  if [[ ${OS_NAME} == debian ]]; then
    curl -fsSL --retry-connrefused --retry 10 --retry-max-time 30 \
      "${NVIDIA_UBUNTU_REPO_KEY_PACKAGE}" -o /tmp/cuda-keyring.deb
    dpkg -i "/tmp/cuda-keyring.deb"

    curl -fsSL --retry-connrefused --retry 10 --retry-max-time 30 \
      "${NVIDIA_DEBIAN_GPU_DRIVER_URL}" -o driver.run
    bash "./driver.run" --silent --install-libglvnd

    curl -fsSL --retry-connrefused --retry 10 --retry-max-time 30 \
      "${NVIDIA_DEBIAN_CUDA_URL}" -o cuda.run
    bash "./cuda.run" --silent --toolkit --no-opengl-libs
  elif [[ ${OS_NAME} == ubuntu ]]; then
    curl -fsSL --retry-connrefused --retry 10 --retry-max-time 30 \
      "${NVIDIA_UBUNTU_REPO_KEY_PACKAGE}" -o /tmp/cuda-keyring.deb
    dpkg -i "/tmp/cuda-keyring.deb"
    curl -fsSL --retry-connrefused --retry 10 --retry-max-time 30 \
      "${NVIDIA_UBUNTU_REPO_CUDA_PIN}" -o /etc/apt/preferences.d/cuda-repository-pin-600

    add-apt-repository "deb ${NVIDIA_UBUNTU_REPO_URL} /"
    execute_with_retries "apt-get update"

    if [[ -n "${CUDA_VERSION}" ]]; then
      local -r cuda_package=cuda-toolkit-${CUDA_VERSION//./-}
    else
      local -r cuda_package=cuda-toolkit
    fi
    # Without --no-install-recommends this takes a very long time.
    execute_with_retries "apt-get install -y -q --no-install-recommends cuda-drivers-${NVIDIA_DEBIAN_GPU_DRIVER_VERSION_PREFIX}"
    execute_with_retries "apt-get install -y -q --no-install-recommends ${cuda_package}"
  elif [[ ${OS_NAME} == rocky ]]; then
    execute_with_retries "dnf config-manager --add-repo ${NVIDIA_ROCKY_REPO_URL}"
    execute_with_retries "dnf clean all"

    if [[ "${ROCKY_BINARY_INSTALL}" == "true" ]]; then
      execute_with_retries "dnf -y -q module install nvidia-driver"
    else
      execute_with_retries "dnf -y -q module install nvidia-driver:${NVIDIA_DEBIAN_GPU_DRIVER_VERSION_PREFIX}-dkms"
    fi
    NVIDIA_ROCKY_GPU_DRIVER_VERSION="$(ls -d /usr/src/nvidia-* | awk -F"nvidia-" '{print $2}')"
    execute_with_retries "dkms build nvidia/${NVIDIA_ROCKY_GPU_DRIVER_VERSION}"
    execute_with_retries "dkms install nvidia/${NVIDIA_ROCKY_GPU_DRIVER_VERSION}"
    modprobe nvidia
    execute_with_retries "dnf -y -q install cuda-${CUDA_VERSION//./-}"
  else
    echo "Unsupported OS: '${OS_NAME}'"
    exit 1
  fi
  ldconfig
  echo "NVIDIA GPU driver provided by NVIDIA was installed successfully"
}

# Collects 'gpu_utilization' and 'gpu_memory_utilization' metrics
function install_gpu_agent() {
  if ! command -v pip; then
    execute_with_retries "apt-get install -y -q python-pip"
  fi
  local install_dir=/opt/gpu-utilization-agent
  mkdir -p "${install_dir}"
  curl -fsSL --retry-connrefused --retry 10 --retry-max-time 30 \
    "${GPU_AGENT_REPO_URL}/requirements.txt" -o "${install_dir}/requirements.txt"
  curl -fsSL --retry-connrefused --retry 10 --retry-max-time 30 \
    "${GPU_AGENT_REPO_URL}/report_gpu_metrics.py" -o "${install_dir}/report_gpu_metrics.py"
  pip install -r "${install_dir}/requirements.txt"

  # Generate GPU service.
  cat <<EOF >/lib/systemd/system/gpu-utilization-agent.service
[Unit]
Description=GPU Utilization Metric Agent

[Service]
Type=simple
PIDFile=/run/gpu_agent.pid
ExecStart=/bin/bash --login -c 'python "${install_dir}/report_gpu_metrics.py"'
User=root
Group=root
WorkingDirectory=/
Restart=always

[Install]
WantedBy=multi-user.target
EOF
  # Reload systemd manager configuration
  systemctl daemon-reload
  # Enable gpu-utilization-agent service
  systemctl --no-reload --now enable gpu-utilization-agent.service
}

function set_hadoop_property() {
  local -r config_file=$1
  local -r property=$2
  local -r value=$3
  bdconfig set_property \
    --configuration_file "${HADOOP_CONF_DIR}/${config_file}" \
    --name "${property}" --value "${value}" \
    --clobber
}

function configure_yarn() {
  if [[ ! -f ${HADOOP_CONF_DIR}/resource-types.xml ]]; then
    printf '<?xml version="1.0" ?>\n<configuration/>' >"${HADOOP_CONF_DIR}/resource-types.xml"
  fi
  set_hadoop_property 'resource-types.xml' 'yarn.resource-types' 'yarn.io/gpu'

  set_hadoop_property 'capacity-scheduler.xml' \
    'yarn.scheduler.capacity.resource-calculator' \
    'org.apache.hadoop.yarn.util.resource.DominantResourceCalculator'

  set_hadoop_property 'yarn-site.xml' 'yarn.resource-types' 'yarn.io/gpu'
}

# This configuration should be applied only if GPU is attached to the node
function configure_yarn_nodemanager() {
  set_hadoop_property 'yarn-site.xml' 'yarn.nodemanager.resource-plugins' 'yarn.io/gpu'
  set_hadoop_property 'yarn-site.xml' \
    'yarn.nodemanager.resource-plugins.gpu.allowed-gpu-devices' 'auto'
  set_hadoop_property 'yarn-site.xml' \
    'yarn.nodemanager.resource-plugins.gpu.path-to-discovery-executables' $NVIDIA_SMI_PATH
  set_hadoop_property 'yarn-site.xml' \
    'yarn.nodemanager.linux-container-executor.cgroups.mount' 'true'
  set_hadoop_property 'yarn-site.xml' \
    'yarn.nodemanager.linux-container-executor.cgroups.mount-path' '/sys/fs/cgroup'
  set_hadoop_property 'yarn-site.xml' \
    'yarn.nodemanager.linux-container-executor.cgroups.hierarchy' 'yarn'
  set_hadoop_property 'yarn-site.xml' \
    'yarn.nodemanager.container-executor.class' \
    'org.apache.hadoop.yarn.server.nodemanager.LinuxContainerExecutor'
  set_hadoop_property 'yarn-site.xml' 'yarn.nodemanager.linux-container-executor.group' 'yarn'

  # Fix local dirs access permissions
  local yarn_local_dirs=()
  readarray -d ',' yarn_local_dirs < <(bdconfig get_property_value \
    --configuration_file "${HADOOP_CONF_DIR}/yarn-site.xml" \
    --name "yarn.nodemanager.local-dirs" 2>/dev/null | tr -d '\n')
  chown yarn:yarn -R "${yarn_local_dirs[@]/,/}"
}

function configure_gpu_exclusive_mode() {
  # check if running spark 3, if not, enable GPU exclusive mode
  local spark_version
  spark_version=$(spark-submit --version 2>&1 | sed -n 's/.*version[[:blank:]]\+\([0-9]\+\.[0-9]\).*/\1/p' | head -n1)
  if [[ ${spark_version} != 3.* ]]; then
    # include exclusive mode on GPU
    nvidia-smi -c EXCLUSIVE_PROCESS
  fi
}

function fetch_mig_scripts() {
  mkdir -p /usr/local/yarn-mig-scripts
  sudo chmod 755 /usr/local/yarn-mig-scripts
  wget -P /usr/local/yarn-mig-scripts/ https://raw.githubusercontent.com/NVIDIA/spark-rapids-examples/branch-22.10/examples/MIG-Support/yarn-unpatched/scripts/nvidia-smi
  wget -P /usr/local/yarn-mig-scripts/ https://raw.githubusercontent.com/NVIDIA/spark-rapids-examples/branch-22.10/examples/MIG-Support/yarn-unpatched/scripts/mig2gpu.sh
  sudo chmod 755 /usr/local/yarn-mig-scripts/*
}

function configure_gpu_script() {
  # Download GPU discovery script
  local -r spark_gpu_script_dir='/usr/lib/spark/scripts/gpu'
  mkdir -p ${spark_gpu_script_dir}
  # need to update the getGpusResources.sh script to look for MIG devices since if multiple GPUs nvidia-smi still
  # lists those because we only disable the specific GIs via CGROUPs. Here we just create it based off of:
  # https://raw.githubusercontent.com/apache/spark/master/examples/src/main/scripts/getGpusResources.sh
  echo '
#!/usr/bin/env bash

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
NUM_MIG_DEVICES=$(nvidia-smi -L | grep MIG | wc -l)
ADDRS=$(nvidia-smi --query-gpu=index --format=csv,noheader | sed -e '\'':a'\'' -e '\''N'\'' -e'\''$!ba'\'' -e '\''s/\n/","/g'\'')
if [ $NUM_MIG_DEVICES -gt 0 ]; then
  MIG_INDEX=$(( $NUM_MIG_DEVICES - 1 ))
  ADDRS=$(seq -s '\''","'\'' 0 $MIG_INDEX)
fi
echo {\"name\": \"gpu\", \"addresses\":[\"$ADDRS\"]}
' > ${spark_gpu_script_dir}/getGpusResources.sh

  chmod a+rwx -R ${spark_gpu_script_dir}
}

function configure_gpu_isolation() {
  # enable GPU isolation
  sed -i "s/yarn\.nodemanager\.linux\-container\-executor\.group\=.*$/yarn\.nodemanager\.linux\-container\-executor\.group\=yarn/g" "${HADOOP_CONF_DIR}/container-executor.cfg"
  if [[ $IS_MIG_ENABLED -ne 0 ]]; then
    # configure the container-executor.cfg to have major caps
    printf '\n[gpu]\nmodule.enabled=true\ngpu.major-device-number=%s\n\n[cgroups]\nroot=/sys/fs/cgroup\nyarn-hierarchy=yarn\n' $MIG_MAJOR_CAPS >> "${HADOOP_CONF_DIR}/container-executor.cfg"
    printf 'export MIG_AS_GPU_ENABLED=1\n' >> "${HADOOP_CONF_DIR}/yarn-env.sh"
    printf 'export ENABLE_MIG_GPUS_FOR_CGROUPS=1\n' >> "${HADOOP_CONF_DIR}/yarn-env.sh"
  else
    printf '\n[gpu]\nmodule.enabled=true\n[cgroups]\nroot=/sys/fs/cgroup\nyarn-hierarchy=yarn\n' >> "${HADOOP_CONF_DIR}/container-executor.cfg"
  fi

  # Configure a systemd unit to ensure that permissions are set on restart
  cat >/etc/systemd/system/dataproc-cgroup-device-permissions.service<<EOF
[Unit]
Description=Set permissions to allow YARN to access device directories

[Service]
ExecStart=/bin/bash -c "chmod a+rwx -R /sys/fs/cgroup/cpu,cpuacct; chmod a+rwx -R /sys/fs/cgroup/devices"

[Install]
WantedBy=multi-user.target
EOF

  systemctl enable dataproc-cgroup-device-permissions
  systemctl start dataproc-cgroup-device-permissions
}

function main() {
  if [[ ${OS_NAME} != debian ]] && [[ ${OS_NAME} != ubuntu ]] && [[ ${OS_NAME} != rocky ]]; then
    echo "Unsupported OS: '${OS_NAME}'"
    exit 1
  fi

  remove_old_backports

  if [[ ${OS_NAME} == debian ]] || [[ ${OS_NAME} == ubuntu ]]; then
    export DEBIAN_FRONTEND=noninteractive
    execute_with_retries "apt-get update"
    execute_with_retries "apt-get install -y -q pciutils"
  elif [[ ${OS_NAME} == rocky ]] ; then
    execute_with_retries "dnf -y -q update"
    execute_with_retries "dnf -y -q install pciutils"
    execute_with_retries "dnf -y -q install kernel-devel-$(uname -r)"
    execute_with_retries "dnf -y -q install gcc"
  fi

  # This configuration should be ran on all nodes
  # regardless if they have attached GPUs
  configure_yarn

  # Detect NVIDIA GPU
  if (lspci | grep -q NVIDIA); then
    # if this is called without the MIG script then the drivers are not installed
    if (/usr/bin/nvidia-smi --query-gpu=mig.mode.current --format=csv,noheader | uniq | wc -l); then
      NUM_MIG_GPUS=`/usr/bin/nvidia-smi --query-gpu=mig.mode.current --format=csv,noheader | uniq | wc -l`
      if [[ $NUM_MIG_GPUS -eq 1 ]]; then
        if (/usr/bin/nvidia-smi --query-gpu=mig.mode.current --format=csv,noheader | grep Enabled); then
          IS_MIG_ENABLED=1
          NVIDIA_SMI_PATH='/usr/local/yarn-mig-scripts/'
          MIG_MAJOR_CAPS=`grep nvidia-caps /proc/devices | cut -d ' ' -f 1`
          fetch_mig_scripts
        fi
      fi
    fi

    if [[ ${OS_NAME} == debian ]] || [[ ${OS_NAME} == ubuntu ]]; then
      execute_with_retries "apt-get install -y -q 'linux-headers-$(uname -r)'"
    fi

    # if mig is enabled drivers would have already been installed
    if [[ $IS_MIG_ENABLED -eq 0 ]]; then
      install_nvidia_gpu_driver
      if [[ -n ${CUDNN_VERSION} ]]; then
        install_nvidia_nccl
        install_nvidia_cudnn
      fi
      #Install GPU metrics collection in Stackdriver if needed
      if [[ ${INSTALL_GPU_AGENT} == true ]]; then
        install_gpu_agent
        echo 'GPU metrics agent successfully deployed.'
      else
        echo 'GPU metrics agent will not be installed.'
      fi
      configure_gpu_exclusive_mode
    fi

    configure_yarn_nodemanager
    configure_gpu_script
    configure_gpu_isolation
  elif [[ "${ROLE}" == "Master" ]]; then
    configure_yarn_nodemanager
    configure_gpu_script
  fi

  # Restart YARN services if they are running already
  if [[ $(systemctl show hadoop-yarn-resourcemanager.service -p SubState --value) == 'running' ]]; then
    systemctl restart hadoop-yarn-resourcemanager.service
  fi
  if [[ $(systemctl show hadoop-yarn-nodemanager.service -p SubState --value) == 'running' ]]; then
    systemctl restart hadoop-yarn-nodemanager.service
  fi
}

main

Memverifikasi penginstalan driver GPU

Setelah selesai menginstal driver GPU pada node Dataproc, Anda dapat memverifikasi bahwa driver berfungsi dengan benar. Jalankan SSH ke node master cluster Dataproc Anda dan jalankan perintah berikut:

nvidia-smi

Jika driver berfungsi dengan baik, output akan menampilkan versi driver dan statistik GPU (lihat Memverifikasi penginstalan driver GPU).

Catatan: Driver mungkin tidak berfungsi dengan benar setelah dimulai ulang VM setelah Upgrade Tanpa Pengawasan Linux. Solusi yang memungkinkan: Anda dapat menonaktifkan upgrade tanpa pengawasan atau mengecualikan update kernel dengan mengedit konfigurasi layanan upgrade yang tidak diawasi.

Konfigurasi Spark

Saat mengirimkan tugas ke Spark, Anda dapat menggunakan properti properti lingkungan runtime konfigurasi Spark spark.executorEnv dengan variabel lingkungan LD_PRELOAD untuk melakukan pramuat library yang diperlukan.

Contoh:

gcloud dataproc jobs submit spark --cluster=CLUSTER_NAME \
  --region=REGION \
  --class=org.apache.spark.examples.SparkPi \
  --jars=file:///usr/lib/spark/examples/jars/spark-examples.jar \
  --properties=spark.executorEnv.LD_PRELOAD=libnvblas.so,spark.task.resource.gpu.amount=1,spark.executor.resource.gpu.amount=1,spark.executor.resource.gpu.discoveryScript=/usr/lib/spark/scripts/gpu/getGpusResources.sh

Contoh tugas GPU

Anda dapat menguji GPU di Dataproc dengan menjalankan salah satu tugas berikut, yang akan berguna jika dijalankan dengan GPU:

1. Jalankan salah satu contoh Spark ML.
2. Jalankan contoh berikut dengan spark-shell untuk menjalankan komputasi matriks:

import org.apache.spark.mllib.linalg._
import org.apache.spark.mllib.linalg.distributed._
import java.util.Random

def makeRandomSquareBlockMatrix(rowsPerBlock: Int, nBlocks: Int): BlockMatrix = {
  val range = sc.parallelize(1 to nBlocks)
  val indices = range.cartesian(range)
  return new BlockMatrix(
      indices.map(
          ij => (ij, Matrices.rand(rowsPerBlock, rowsPerBlock, new Random()))),
      rowsPerBlock, rowsPerBlock, 0, 0)
}

val N = 1024 * 4
val n = 2
val mat1 = makeRandomSquareBlockMatrix(N, n)
val mat2 = makeRandomSquareBlockMatrix(N, n)
val mat3 = mat1.multiply(mat2)
mat3.blocks.persist.count
println("Processing complete!")

Langkah Berikutnya

• Compute Engine→Memasang GPU ke instance
• Compute Engine→GPU di Compute Engine