Google Cloud

AI 基礎架構:網路技術

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Google Cloud

AI 基礎架構:網路技術

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Gain insight into a topic and learn the fundamentals.
Intermediate level
Some related experience required
4 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level
Some related experience required
4 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • 介紹 AI 管道每個階段的網路需求和相關的 Google Cloud 產品。

  • 區分不同 GPU 和 TPU 機型的網路特性。

  • 介紹資料擷取作業、AI 訓練和生成式 AI 推論適用的網路選項

  • 瞭解 AI 網路最佳做法。

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Recently updated!

April 2026

Assessments

4 assignments

Taught in Chinese (Traditional)

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There are 6 modules in this course

這個單元會概略說明課程內容,並列出學習目標。

What's included

1 plugin

本單元詳細說明 AI 工作負載不同於傳統網頁應用程式的特殊網路要求,包括介紹每個管道階段 (從擷取到推論) 的特定頻寬和延遲需求,並分析 Google Cloud 的 A3 和 A4 GPU 機型為盡可能提高「有效處理量」而採用的「通道一致」(rail-aligned) 網路架構。

What's included

1 assignment3 plugins

本模組詳細說明將大量資料集有效遷移到雲端的策略,包括介紹如何運用 Cross-Cloud Network 和 Cloud Interconnect 來建立高頻寬管道,並概述啟用巨型封包 (MTU) 等設定方面的最佳做法,來減少通訊協定負擔並盡可能提升處理量。

What's included

1 assignment2 plugins

本單元詳細說明低延遲網路在分散式模型訓練中的關鍵作用,包括介紹遠端直接記憶體存取 (RDMA) 對梯度同步作業的必要性、Google Titanium 卸載架構在釋放 CPU 資源方面的優勢,以及既能擴充叢集又不出現瓶頸的拓撲選擇。

What's included

1 assignment3 plugins

本單元詳細說明生成式 AI 推論特有的網路難題 (例如流量飆升和長時間連線),包括介紹如何運用 GKE 推論閘道和「佇列深度」路由,盡可能縮短第一個詞元生成時間,同時也探討了網路可靠性和 Identity and Access Management (IAM) 的最佳做法。

What's included

1 assignment5 plugins

學員用單元 PDF 的連結

What's included

1 reading

Instructor

Google Cloud Training
Google Cloud
2,139 Courses4,138,208 learners

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Google Cloud

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