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Google Cloud TPU adalah akselerator AI yang dirancang khusus dan dibuat oleh Google yang dioptimalkan untuk melatih dan menggunakan model AI berskala besar. Cloud TPU dirancang untuk menskalakan berbagai workload AI secara hemat biaya dan memberikan fleksibilitas untuk mempercepat workload inferensi pada framework AI, termasuk PyTorch, JAX, dan TensorFlow. Untuk mengetahui detail selengkapnya tentang TPU, lihat Pengantar
Google Cloud TPU.
Prasyarat untuk menggunakan TPU di Dataflow
Project Google Cloud Anda harus disetujui untuk menggunakan penawaran GA ini.
Batasan
Penawaran ini tunduk pada batasan berikut:
Hanya akselerator TPU host tunggal yang didukung: Penawaran TPU Dataflow hanya mendukung konfigurasi TPU host tunggal di mana setiap pekerja Dataflow mengelola satu atau beberapa perangkat TPU yang tidak saling terhubung dengan TPU yang dikelola oleh pekerja lain.
Hanya kumpulan pekerja TPU homogen yang didukung: Fitur seperti
Dataflow right fitting dan Dataflow Prime
tidak mendukung beban kerja TPU.
Harga
Tugas Dataflow yang menggunakan TPU ditagih untuk jam-chip TPU pekerja yang digunakan dan tidak ditagih untuk CPU dan memori pekerja. Untuk mengetahui informasi selengkapnya, lihat halaman harga Dataflow.
Ketersediaan
Akselerator TPU dan region pemrosesan berikut tersedia.
Akselerator TPU yang didukung
Kombinasi akselerator TPU yang didukung diidentifikasi oleh tuple (jenis TPU, topologi TPU).
Jenis TPU mengacu pada model perangkat TPU.
Topologi TPU mengacu pada jumlah dan pengaturan fisik chip
TPU dalam slice.
Untuk mengonfigurasi jenis dan topologi TPU untuk pekerja Dataflow, gunakan opsi pipeline worker_accelerator yang diformat sebagai type:TPU_TYPE;topology:TPU_TOPOLOGY.
Konfigurasi TPU berikut didukung dengan Dataflow:
Jenis TPU
Topologi
Wajib worker_machine_type
tpu-v5-lite-podslice
1x1
ct5lp-hightpu-1t
tpu-v5-lite-podslice
2x2
ct5lp-hightpu-4t
tpu-v5-lite-podslice
2x4
ct5lp-hightpu-8t
tpu-v6e-slice
1x1
ct6e-standard-1t
tpu-v6e-slice
2x2
ct6e-standard-4t
tpu-v6e-slice
2x4
ct6e-standard-8t
tpu-v5p-slice
2x2x1
ct5p-hightpu-4t
Region
Untuk mengetahui informasi tentang region dan zona yang tersedia untuk TPU, lihat Region dan zona TPU di dokumentasi Cloud TPU.
[[["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."],[],[],null,["| **Note:** The Dataflow TPU offering is generally available with an allowlist. To get access to this feature, reach out to your account team.\n\nGoogle Cloud TPUs are custom-designed AI accelerators created by Google that are\noptimized for training and using of large AI models. They are designed to\nscale cost-efficiently for a wide range of AI workloads and provide versatility\nto accelerate inference workloads on AI frameworks, including PyTorch, JAX, and\nTensorFlow. For more details about TPUs, see [Introduction to\nGoogle Cloud TPU](/tpu/docs/intro-to-tpu).\n\nPrerequisites for using TPUs in Dataflow\n\n- Your Google Cloud projects must be approved to use this GA offering.\n\nLimitations\n\nThis offering is subject to the following limitations:\n\n- **Only single-host TPU accelerators are supported**: The Dataflow TPU offering supports only single-host TPU configurations where each Dataflow worker manages one or many TPU devices that are not interconnected with TPUs managed by other workers.\n- **Only homogenous TPU worker pools are supported**: Features like Dataflow right fitting and Dataflow Prime don't support TPU workloads.\n\nPricing\n\nDataflow jobs that use TPUs are billed for worker TPU chip-hours\nconsumed and are not billed for worker CPU and memory. For more information, see\nthe Dataflow [pricing page](/dataflow/pricing).\n\nAvailability\n\nThe following TPU accelerators and processing regions are available.\n\nSupported TPU accelerators\n\nThe supported TPU accelerator combinations are identified by the tuple (TPU\ntype, TPU topology).\n\n- **TPU type** refers to the model of the TPU device.\n- **TPU topology** refers to the number and physical arrangement of the TPU chips in a slice.\n\nTo configure the type and topology of TPUs for Dataflow workers,\nuse the [`worker_accelerator` pipeline\noption](/dataflow/docs/reference/service-options) formatted as\n`type:TPU_TYPE;topology:TPU_TOPOLOGY`.\n\nThe following TPU configurations are supported with Dataflow:\n\n| TPU type | Topology | Required `worker_machine_type` |\n|----------------------|----------|--------------------------------|\n| tpu-v5-lite-podslice | 1x1 | ct5lp-hightpu-1t |\n| tpu-v5-lite-podslice | 2x2 | ct5lp-hightpu-4t |\n| tpu-v5-lite-podslice | 2x4 | ct5lp-hightpu-8t |\n| tpu-v6e-slice | 1x1 | ct6e-standard-1t |\n| tpu-v6e-slice | 2x2 | ct6e-standard-4t |\n| tpu-v6e-slice | 2x4 | ct6e-standard-8t |\n| tpu-v5p-slice | 2x2x1 | ct5p-hightpu-4t |\n\nRegions\n\nFor information about available regions and zones for TPUs, see [TPU regions and\nzones](/tpu/docs/regions-zones) in the Cloud TPU documentation.\n\nWhat's next\n\n- Learn how to [run an Apache Beam pipeline on Dataflow with\n TPUs](/dataflow/docs/tpu/use-tpus).\n- Learn how to [troubleshoot your Dataflow TPU\n job](/dataflow/docs/tpu/troubleshoot-tpus)."]]
