GPUs Clustern hinzufügen

Dataproc bietet die Möglichkeit, Grafikprozessoren (Graphics Processing Units, GPUs) zu den Master- und Worker-Knoten für Google Compute Engine in einem Dataproc-Cluster hinzuzufügen. Sie können diese GPUs verwenden, um bestimmte Arbeitslasten wie maschinelles Lernen und Datenverarbeitung in Ihren Instanzen zu beschleunigen.

Weitere Informationen zu den Vorteilen von GPUs und den unterschiedlichen Arten von GPU-Hardware finden Sie unter GPUs in Compute Engine.

Hinweis

  • GPUs erfordern spezielle Treiber und Software. Diese Elemente sind auf Dataproc-Clustern nicht vorinstalliert.
  • Lesen Sie mehr über die GPU-Preisgestaltung für Compute Engine, um zu erfahren, mit welchen Kosten die Nutzung von GPUs in Ihren Instanzen verbunden ist.
  • Lesen Sie mehr über die Einschränkungen für Instanzen mit GPUs, um zu erfahren, wie sich diese Instanzen von Nicht-GPU-Instanzen unterscheiden.
  • Prüfen Sie auf der Seite "Kontingente" für Ihr Projekt, ob ausreichend GPU-Kontingente (NVIDIA_K80_GPUS, NVIDIA_P100_GPUS oder NVIDIA_V100_GPUS) vorhanden sind. Wenn auf der Seite "Kontingente" keine GPUs aufgeführt sind oder Sie zusätzliche GPU-Kontingente benötigen, können Sie eine Erhöhung des Kontingents beantragen.

GPU-Typen

Dataproc-Knoten unterstützen die folgenden GPU-Typen. Sie müssen den GPU-Typ angeben, wenn Sie Ihrem Dataproc-Cluster GPUs hinzufügen.

  • 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 – NVIDIA® Tesla® P100 Virtual Workstations
  • nvidia-tesla-p4-vws – NVIDIA® Tesla® P4 Virtual Workstations
  • nvidia-tesla-t4-vws – NVIDIA® Tesla® T4 Virtual Workstations

GPUs Clustern hinzufügen

gcloud

Fügen Sie dem Master-, den primären und den sekundären Worker-Knoten in einem Dataproc-Cluster GPUs hinzu, wenn Sie den Cluster mit den Flags ‑‑master-accelerator, ‑‑worker-accelerator und ‑‑secondary-worker-accelerator erstellen. Diese Flags verwenden zwei Werte:

  1. den GPU-Typ, der dem Knoten hinzugefügt werden soll
  2. die Anzahl der GPUs, die dem Knoten hinzugefügt werden sollen

Der GPU-Typ ist erforderlich, die Anzahl der GPUs ist optional (der Standardwert ist 1 GPU).

Example:

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

Damit Sie GPUs in Ihrem Cluster verwenden können, müssen Sie GPU-Treiber installieren.

REST API

Fügen Sie dem Haupt-, den primären und den Worker-Knoten auf Abruf in einem Dataproc-Cluster GPUs hinzu, indem Sie die Felder InstanceGroupConfig.AcceleratorConfig acceleratorTypeUri und acceleratorCount als Teil der cluster.create-API-Anfrage ausfüllen.

Console

Klicken Sie in der Konsole auf der Seite Cluster erstellen auf der Seite Cluster erstellen in den Abschnitten „Master- und Worker-Knoten > GPU hinzufügen“ → „GPU“ → „GPU hinzufügen“ und geben Sie die Anzahl der GPUs und den GPU-Typ für die Knoten an.

GPU-Treiber installieren

Zum Verwenden von GPUs, die Dataproc-Knoten hinzugefügt wurden, sind GPU-Treiber erforderlich. Zum Installieren von GPU-Treibern folgen Sie den Anleitungen für diese Initialisierungsaktion, die unten aufgeführt ist.

#!/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 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:]')
readonly OS_NAME

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

# Parameters for NVIDIA-provided Debian GPU driver
readonly DEFAULT_NVIDIA_DEBIAN_GPU_DRIVER_VERSION='460.73.01'
readonly DEFAULT_NVIDIA_DEBIAN_GPU_DRIVER_URL="https://download.nvidia.com/XFree86/Linux-x86_64/${DEFAULT_NVIDIA_DEBIAN_GPU_DRIVER_VERSION}/NVIDIA-Linux-x86_64-${DEFAULT_NVIDIA_DEBIAN_GPU_DRIVER_VERSION}.run"
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'

# CUDA Version
CUDA_VERSION=$(get_metadata_attribute 'cuda-version' '11.2')
readonly CUDA_VERSION

# 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 DEFAULT_NCCL_VERSION="2.8.3"
readonly DEFAULT_NCCL_VERSION_ROCKY="2.8.4"

if [[ ${OS_NAME} == rocky ]]; then
  NCCL_VERSION=$(get_metadata_attribute 'nccl-version' ${DEFAULT_NCCL_VERSION_ROCKY})
else
  NCCL_VERSION=$(get_metadata_attribute 'nccl-version' ${DEFAULT_NCCL_VERSION})
fi
readonly NCCL_VERSION

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]="${NVIDIA_BASE_DL_URL}/cuda/11.2.2/local_installers/cuda_11.2.2_460.32.03_linux.run")
readonly DEFAULT_NVIDIA_DEBIAN_CUDA_URL=${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
readonly CUDNN_VERSION=$(get_metadata_attribute 'cudnn-version' '')
readonly CUDNN_TARBALL="cudnn-${CUDA_VERSION}-linux-x64-v${CUDNN_VERSION}.tgz"
readonly CUDNN_TARBALL_URL="http://developer.download.nvidia.com/compute/redist/cudnn/v${CUDNN_VERSION%.*}/${CUDNN_TARBALL}"

# 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 ${packages[*]}"
  else
    local tmp_dir
    tmp_dir=$(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}"

    tar -xzf "${tmp_dir}/${CUDNN_TARBALL}" -C /usr/local

    cat <<'EOF' >>/etc/profile.d/cudnn.sh
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH}
EOF
  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-460"
    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 ${NVIDIA_ROCKY_REPO_URL}"
    execute_with_retries "dnf clean all"
    execute_with_retries "dnf -y -q module install nvidia-driver:460-dkms"
    execute_with_retries "dnf -y -q install cuda-${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 "${install_dir}"
  curl -fsSL --retry-connrefused --retry 10 --retry-max-time 30 \
    "${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-22.06/examples/MIG-Support/yarn-unpatched/scripts/nvidia-smi
  wget -P /usr/local/yarn-mig-scripts/ https://raw.githubusercontent.com/NVIDIA/spark-rapids-examples/branch-22.06/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

  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"
    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

Installieren des GPU-Treibers bestätigen

Nachdem Sie den GPU-Treiber auf den Dataproc-Knoten installiert haben, können Sie prüfen, ob der Treiber ordnungsgemäß funktioniert. Stellen Sie eine SSH-Verbindung zum Masterknoten Ihres Dataproc-Clusters her und führen Sie den folgenden Befehl aus:

nvidia-smi

Wenn der Treiber ordnungsgemäß funktioniert, umfasst die Ausgabe die Treiberversion und die GPU-Statistiken (siehe Installieren des GPU-Treibers bestätigen).

Spark-Konfiguration

Beim Senden von Jobs an Spark können Sie die folgende Spark-Konfiguration verwenden, um die erforderlichen Bibliotheken zu laden:

spark.executorEnv.LD_PRELOAD=libnvblas.so

Beispiel für GPU-Job

Sie können GPUs in Cloud Dataproc testen, indem Sie einen der folgenden Jobs ausführen:

  1. Führen Sie eines der Spark ML-Beispiele aus.
  2. Führen Sie das folgende Beispiel mit spark-shell aus, um eine Matrixberechnung durchzuführen:
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 * 5
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!")

Nächste Schritte