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Halaman ini memberikan informasi latar belakang tentang cara kerja GPU dengan Dataflow, termasuk informasi tentang prasyarat dan jenis GPU yang didukung.
Dengan menggunakan GPU dalam tugas Dataflow, Anda dapat mempercepat beberapa tugas pemrosesan data. GPU dapat melakukan komputasi tertentu lebih cepat daripada CPU. Komputasi ini biasanya berupa aljabar linear atau numerik,
yang sering digunakan dalam kasus penggunaan pemrosesan gambar dan machine learning. Tingkat peningkatan performa bervariasi menurut kasus penggunaan, jenis komputasi, dan jumlah data yang diproses.
Prasyarat untuk menggunakan GPU di Dataflow
Untuk menggunakan GPU dengan tugas Dataflow, Anda harus menggunakan Runner v2.
Dataflow menjalankan kode pengguna di VM pekerja di dalam container Docker.
VM pekerja ini menjalankan Container-Optimized OS.
Agar tugas Dataflow dapat menggunakan GPU, Anda memerlukan prasyarat berikut:
Driver GPU diinstal pada VM pekerja dan dapat diakses oleh container Docker. Untuk mengetahui informasi selengkapnya, lihat
Menginstal driver GPU.
Tugas yang menggunakan GPU dikenai biaya seperti yang ditentukan di
halaman harga Dataflow.
Ketersediaan
Jenis GPU berikut didukung dengan Dataflow:
Jenis GPU
String worker_accelerator
NVIDIAยฎ L4
nvidia-l4
NVIDIAยฎ A100 40 GB
nvidia-tesla-a100
NVIDIAยฎ A100 80 GB
nvidia-a100-80gb
NVIDIAยฎ Teslaยฎ T4
nvidia-tesla-t4
NVIDIAยฎ Teslaยฎ P4
nvidia-tesla-p4
NVIDIAยฎ Teslaยฎ V100
nvidia-tesla-v100
NVIDIAยฎ Teslaยฎ P100
nvidia-tesla-p100
NVIDIAยฎ H100
nvidia-h100-80gb
NVIDIAยฎ H100 Mega
nvidia-h100-mega-80gb
Untuk mengetahui informasi selengkapnya tentang setiap jenis GPU, termasuk data performa, lihat Platform GPU Compute Engine.
Untuk mengetahui informasi tentang region dan zona yang tersedia untuk GPU, lihat Ketersediaan region dan zona GPU dalam dokumentasi Compute Engine.
Workload yang direkomendasikan
Tabel berikut memberikan rekomendasi jenis GPU yang akan digunakan untuk berbagai workload. Contoh dalam tabel hanyalah saran, dan Anda perlu melakukan pengujian di lingkungan Anda sendiri untuk menentukan jenis GPU yang sesuai untuk workload Anda.
Untuk mengetahui informasi yang lebih mendetail tentang ukuran memori GPU, ketersediaan fitur, dan
jenis beban kerja ideal untuk berbagai model GPU, lihat
Diagram perbandingan umum
di halaman platform GPU.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-09-04 UTC."],[[["\u003cp\u003eDataflow jobs using GPUs can accelerate data processing, especially for numeric or linear algebra computations like those in image processing and machine learning.\u003c/p\u003e\n"],["\u003cp\u003eUsing GPUs in Dataflow requires Dataflow Runner v2 and incurs charges detailed on the Dataflow pricing page.\u003c/p\u003e\n"],["\u003cp\u003ePrerequisites for GPU usage include having GPU drivers installed on worker VMs and GPU libraries installed in the custom container image.\u003c/p\u003e\n"],["\u003cp\u003eDataflow supports several NVIDIA GPU types, including L4, A100 (40 GB and 80 GB), Tesla T4, P4, V100, and P100, each suited for different workload sizes and types.\u003c/p\u003e\n"],["\u003cp\u003eThe boot disk size for GPU containers should be increased to at least 50 gigabytes to prevent running out of disk space, due to the large nature of these containers.\u003c/p\u003e\n"]]],[],null,["# Dataflow support for GPUs\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\nThis page provides background information on how GPUs work with\nDataflow, including information about prerequisites and supported\nGPU types.\n\nUsing GPUs in Dataflow jobs lets you accelerate\nsome data processing tasks. GPUs can perform certain computations faster\nthan CPUs. These computations are usually numeric or linear algebra,\noften used in image processing and machine learning use cases. The\nextent of performance improvement varies by the use case, type of computation,\nand amount of data processed.\n\nPrerequisites for using GPUs in Dataflow\n----------------------------------------\n\n\n- To use GPUs with your Dataflow job, you must use Runner v2.\n- Dataflow runs user code in worker VMs inside a Docker container. These worker VMs run [Container-Optimized OS](/container-optimized-os/docs). For Dataflow jobs to use GPUs, you need the following prerequisites:\n - GPU drivers are installed on worker VMs and accessible to the Docker container. For more information, see [Install GPU drivers](/dataflow/docs/gpu/use-gpus#drivers).\n - GPU libraries required by your pipeline, such as [NVIDIA CUDA-X libraries](https://developer.nvidia.com/gpu-accelerated-libraries) or the [NVIDIA CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit), are installed in the custom container image. For more information, see [Configure your container image](/dataflow/docs/gpu/use-gpus#container-image).\n- Because GPU containers are typically large, to avoid [running out of disk space](/dataflow/docs/guides/common-errors#no-space-left), increase the default [boot disk size](/dataflow/docs/reference/pipeline-options#worker-level_options) to 50 gigabytes or more.\n\n\u003cbr /\u003e\n\nPricing\n-------\n\nJobs using GPUs incur charges as specified in the Dataflow\n[pricing page](/dataflow/pricing).\n\nAvailability\n------------\n\nThe following GPU types are supported with Dataflow:\n\nFor more information about each GPU type, including performance data, see\n[Compute Engine GPU platforms](/compute/docs/gpus).\n\nFor information about available regions and zones for GPUs, see\n[GPU regions and zones availability](/compute/docs/gpus/gpu-regions-zones)\nin the Compute Engine documentation.\n\n### Recommended workloads\n\nThe following table provides recommendations for which type of GPU to use for\ndifferent workloads. The examples in the table are suggestions only, and you\nneed to test in your own environment to determine the appropriate GPU type for\nyour workload.\n\nFor more detailed information about GPU memory size, feature availability, and\nideal workload types for different GPU models, see the\n[General comparison chart](/compute/docs/gpus#general_comparison_chart)\non the GPU platforms page.\n\nWhat's next\n-----------\n\n- See an example of a [developer workflow for building pipelines that use GPUs](/dataflow/docs/gpu/develop-with-gpus).\n- Learn how to [run an Apache Beam pipeline on Dataflow with GPUs](/dataflow/docs/gpu/use-gpus).\n- Work through [Processing Landsat satellite images with GPUs](/dataflow/docs/samples/satellite-images-gpus)."]]
