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Auf dieser Seite wird erläutert, wie Sie Ihre Dataflow-Pipeline mit dem NVIDIA® L4-GPU-Typ ausführen.
Der L4-GPU-Typ ist nützlich, um Inferenz-Pipelines für maschinelles Lernen auszuführen.
Voraussetzungen
Verwenden Sie das Apache Beam SDK in der Version 2.46 oder höher. Apache Beam 2.50 oder höher wird empfohlen.
Sie benötigen ein L4-GPU-Kontingent (NVIDIA_L4_GPUS) in der Region, in der Ihr Job ausgeführt wird.
Weitere Informationen finden Sie unter GPU-Kontingente.
Der L4-GPU-Typ ist nur mit dem G2-beschleunigungsoptimierten Maschinentyp verfügbar.
Weitere Informationen finden Sie unter G2-Maschinenserie.
Pipelines, die den L4-GPU-Typ verwenden, unterliegen den G2-Standardeinschränkungen.
Der NVIDIA L4-GPU-Typ verwendet die NVIDIA-Treiberversion 525.0 oder höher und das CUDA-Toolkit Version 12.0 oder höher. Jeder Code, den Sie in Ihrer Pipeline verwenden, muss mit der NVIDIA-Treiberversion und der CUDA-Toolkitversion kompatibel sein. Wenn Sie beispielsweise PyTorch verwenden, müssen Sie die PyTorch-Version 23.01 oder höher verwenden.
Pipelines mit dem NVIDIA® L4-GPU-Typ ausführen
Wenn Sie den NVIDIA L4-GPU-Typ verwenden möchten, müssen Sie die folgenden Pipelineoptionen und Dienstoptionen in Ihren Pipelinecode einfügen.
GPU_COUNT: die Anzahl der zu verwendenden GPUs. Jeder G2-Maschinentyp hat eine feste Anzahl von NVIDIA L4-GPUs. Die richtige Anzahl von GPUs für Ihren Maschinentyp finden Sie in der Spalte GPU-Anzahl in der Tabelle G2-Standardmaschinentypen.
Das folgende Dockerfile-Beispiel enthält kompatible Abhängigkeiten für eine Pipeline, die den NVIDIA L4-GPU-Typ verwendet.
RUN apt-get -y update
RUN apt-get install [system packages]
# Install the SDK.
RUN pip install --no-cache-dir apache-beam[gcp]==2.51.0
# Install the machine learning dependencies.
RUN pip install --no-cache-dir tensorflow[and-cuda]
RUN pip install xgboost
RUN pip install transformers accelerate
(etc…..)
# Verify that the image doesn't have conflicting dependencies.
RUN pip check
# Copy files from official SDK image, including the script and dependencies.
COPY --from=apache/beam_python3.10_sdk:2.51.0 /opt/apache/beam /opt/apache/beam
# Set the entrypoint to Apache Beam SDK launcher.
ENTRYPOINT ["/opt/apache/beam/boot"]
[[["Leicht verständlich","easyToUnderstand","thumb-up"],["Mein Problem wurde gelöst","solvedMyProblem","thumb-up"],["Sonstiges","otherUp","thumb-up"]],[["Schwer verständlich","hardToUnderstand","thumb-down"],["Informationen oder Beispielcode falsch","incorrectInformationOrSampleCode","thumb-down"],["Benötigte Informationen/Beispiele nicht gefunden","missingTheInformationSamplesINeed","thumb-down"],["Problem mit der Übersetzung","translationIssue","thumb-down"],["Sonstiges","otherDown","thumb-down"]],["Zuletzt aktualisiert: 2025-08-18 (UTC)."],[[["\u003cp\u003eDataflow jobs using GPUs, like the NVIDIA L4, will incur charges as specified on the Dataflow pricing page and must use Dataflow Runner v2.\u003c/p\u003e\n"],["\u003cp\u003eTo utilize the NVIDIA L4 GPU type, Dataflow pipelines must use Apache Beam SDK version 2.46 or later, with 2.50 or later being recommended, and have the necessary L4 GPU quota.\u003c/p\u003e\n"],["\u003cp\u003eThe L4 GPU type is exclusive to the G2 accelerator-optimized machine type, and pipelines using it are subject to G2 standard limitations, which requires specification of the machine type and GPU count in pipeline options.\u003c/p\u003e\n"],["\u003cp\u003ePipelines using the NVIDIA L4 GPU type must be compatible with NVIDIA driver version 525.0 or later and CUDA toolkit version 12.0 or later, which can be ensured by using a custom container to manage dependencies.\u003c/p\u003e\n"],["\u003cp\u003eThe NVIDIA L4 GPU type is ideal for running machine learning inference pipelines on Dataflow, and a dockerfile is provided for users to implement it.\u003c/p\u003e\n"]]],[],null,["# Use the NVIDIA® L4 GPU type\n\n\u003cbr /\u003e\n\n| **Note:** The following considerations apply to this GA offering:\n|\n| - Jobs that use GPUs incur charges as specified in the Dataflow [pricing page](/dataflow/pricing).\n| - To use GPUs, your Dataflow job must use [Dataflow Runner v2](/dataflow/docs/runner-v2).\n\n\u003cbr /\u003e\n\nThe page explains how to run your Dataflow pipeline with the NVIDIA® L4 GPU type.\nThe L4 GPU type is useful for running machine learning inference pipelines.\n\nRequirements\n------------\n\n- Use the Apache Beam SDK version 2.46 or later. Apache Beam 2.50 or later is recommended.\n- You need L4 GPU quota (`NVIDIA_L4_GPUS`) in the region that your job runs in. For more information, see [GPU quotas](/compute/resource-usage#gpu_quota).\n- The L4 GPU type is available only with the G2 accelerator-optimized machine type. For more information, see [The G2 machine series](/compute/docs/accelerator-optimized-machines#g2-vms). Pipelines that use the L4 GPU type are subject to the [G2 standard limitations](/compute/docs/accelerator-optimized-machines#g2_standard_limitations).\n- The NVIDIA L4 GPU type uses the NVIDIA driver version 525.0 or later and the [CUDA toolkit](https://developer.nvidia.com/cuda-toolkit) version 12.0 or later. Any code that you use in your pipeline must be compatible with the NVIDIA driver version and CUDA toolkit version. For example, if you use PyTorch, you need to use PyTorch version 23.01 or later.\n\nRun pipelines with the NVIDIA® L4 GPU type\n------------------------------------------\n\nTo use the NVIDIA L4 GPU type, you need to include the following\n[pipeline options](/dataflow/docs/reference/pipeline-options) and\n[service options](/dataflow/docs/reference/service-options)\nin your pipeline code. \n\n### Java\n\n --workerMachineType=\u003cvar translate=\"no\"\u003eG2_MACHINE_TYPE\u003c/var\u003e\n --dataflowServiceOptions=\"worker_accelerator=type:nvidia-l4;count:\u003cvar translate=\"no\"\u003eGPU_COUNT\u003c/var\u003e;install-nvidia-driver\"\n\n### Python\n\n --machine_type=\u003cvar translate=\"no\"\u003eG2_MACHINE_TYPE\u003c/var\u003e\n --dataflow_service_options=\"worker_accelerator=type:nvidia-l4;count:\u003cvar translate=\"no\"\u003eGPU_COUNT\u003c/var\u003e;install-nvidia-driver\"\n\n### Go\n\n --worker_machine_type=\u003cvar translate=\"no\"\u003eG2_MACHINE_TYPE\u003c/var\u003e\n --dataflow_service_options=\"worker_accelerator=type:nvidia-l4;count:\u003cvar translate=\"no\"\u003eGPU_COUNT\u003c/var\u003e;install-nvidia-driver\"\n\nReplace the following values:\n\n- \u003cvar translate=\"no\"\u003eG2_MACHINE_TYPE\u003c/var\u003e: the [G2 machine type](/compute/docs/accelerator-optimized-machines#g2-standard-vms) to use\n- \u003cvar translate=\"no\"\u003eGPU_COUNT\u003c/var\u003e: The number of GPUs to use. Each G2 machine type has a fixed number of NVIDIA L4 GPUs. To find the correct number of GPUs for your machine type, see the **GPU count** column in the [G2 standard machine types](/compute/docs/accelerator-optimized-machines#g2-standard-vms) table.\n\nFor more information about running pipelines with\nGPUs, see [Run a pipeline with GPUs](/dataflow/docs/gpu/use-gpus).\n\nManage dependencies\n-------------------\n\nTo manage dependencies, use a custom container.\nFor more information, see\n[Use custom containers in Dataflow](/dataflow/docs/guides/using-custom-containers).\n\nThe following Dockerfile example contains compatible\ndependencies for a pipeline that uses the NVIDIA L4 GPU type. \n\n RUN apt-get -y update\n RUN apt-get install [system packages]\n\n # Install the SDK.\n RUN pip install --no-cache-dir apache-beam[gcp]==2.51.0\n # Install the machine learning dependencies.\n RUN pip install --no-cache-dir tensorflow[and-cuda]\n RUN pip install xgboost\n RUN pip install transformers accelerate\n (etc.....)\n # Verify that the image doesn't have conflicting dependencies.\n RUN pip check\n\n # Copy files from official SDK image, including the script and dependencies.\n COPY --from=apache/beam_python3.10_sdk:2.51.0 /opt/apache/beam /opt/apache/beam\n\n # Set the entrypoint to Apache Beam SDK launcher.\n ENTRYPOINT [\"/opt/apache/beam/boot\"]\n\nWhat's next\n-----------\n\n- Read about [best practices for working with Dataflow GPUs](/dataflow/docs/gpu/develop-with-gpus).\n- [Run a pipeline with GPUs](/dataflow/docs/gpu/use-gpus).\n- Learn more about [Dataflow ML](/dataflow/docs/machine-learning)."]]
