释放 TPU 栈的力量：全新开发者中心正式上线

英文标题：Unlocking the Power of the TPU Stack: Introducing our new Developer Hub

Google 正式推出 TPU 开发者中心，为模型构建者、优化工程师和开发者提供集中式教育资源，以最大化 Cloud TPU 性能。该中心提供代码优先的开源方案、深度文档，覆盖硬件架构、软件栈、并行优化及安全等全生命周期内容。无论初学者还是资深从业者，都能借助持续更新的技术指导，将模型从概念高效迁移至生产环境，降低 TPU 的使用门槛。

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中文

今天，我们激动地宣布 TPU 开发者中心正式上线——这是一个全新的教育资源平台，旨在赋能模型构建者、优化工程师和开发者，充分释放 Google Cloud TPU 的全部性能。随着 AI 开发领域的快速演进，该中心将成长为你获取高质量、可操作且及时更新的指导的中心化入口，确保你拥有在 TPU 基础设施及其配套软件栈上取得成功所需的工具。

TPU 中心将持续提供定期更新，帮助你找到来自 Google 各个团队的最新技技术内容。无论你是刚刚踏上旅程，还是希望榨取模型每一点性能的资深从业者，该中心都提供不断增长的教育资源，帮助你填补从概念到生产的鸿沟。

全新教育资源：在这里你将找到什么

我们的内容覆盖端到端的开发者生命周期，包括预训练、后训练和推理工作负载。该中心为项目的多个层面提供资源，从架构设计大规模训练集群到优化低延迟推理：

硬件架构与基础设施消费： 深入了解 TPU 硬件设计和基础架构。学习如何通过我们各种云基础设施消费模式（包括裸金属内核和特定的 Cloud TPU 服务产品）有效利用这些能力。我们提供清晰的指南，帮助你选择符合具体计算需求的基础设施层级。
软件栈能力： 了解 TPU 软件栈的各层，包括专用编译器技术和 XLA，确保你的模型运行在优化原语上。学习如何在几乎零迁移成本的情况下迁移并部署 PyTorch 到 TPU。这部分内容为已经在常用 ML 框架中工作的开发者简化了过渡过程。
追踪、调试与可观测性： 利用先进的遥测和 XProf 工具获取工作负载的细粒度可视性，帮助你精准定位性能瓶颈。我们的指南展示如何解读复杂的诊断数据，以缩短迭代周期。你将学会实时监控系统健康状态，确保模型在训练或推理全过程中始终保持峰值效率。
并行化与优化策略： 探索高级扩展技术，包括多芯片执行模型和联合优化方法——如 Pallas 内核——以攀登山峰般地提升模型性能和最大化效率。这些资源包含经过验证的并行管理方案，从基础配置到复杂的大规模分布式训练设置。我们还重点介绍了高级推理的优化策略，如 KV 缓存卸载。
网络与安全： 通过深入探讨网络基础和端到端安全最佳实践，为你的分布式训练和推理任务建立稳固的基础。这些模块涵盖了在芯片之间维持高速通信而不牺牲数据完整性的关键基础设施要求。你将学会架构安全、可扩展的系统，满足企业级生产标准。

这些资源——从交互式 Colab 和开源方案到深度文档——都旨在满足你在开发旅程每一步的具体需求。

为开发者而生

我们知道工程师重视实践、代码优先的学习方式。因此，该中心充满了开源代码方案和深度技术文档。这些资产也设计为对 AI Agent 摄取友好，意味着无论你是手动浏览还是使用 AI 辅助开发工具，都能将我们的最佳实践无缝集成到你的工作流程中。

TPU 开发者中心是我们通过开放且易于使用的生态系统使 TPU 更易获得的承诺。我们邀请你探索这些方案，跟随我们的操作指南，并利用这个不断增长的、为你成功量身定制的教育资源合集。

准备好开始了吗？ 立即访问 TPU 开发者中心，开始构建 AI 的未来。

English / 原文

Today we are thrilled to announce the official launch of the TPU Developer Hub—a new educational resource designed to empower model builders, optimizers, and developers to unlock the full performance of Google Cloud TPUs. As the landscape of AI development rapidly evolves, this hub will grow into your centralized destination for high-quality, actionable, and up-to-date guidance, ensuring you have the tools necessary to succeed with TPU infrastructure and its supporting software stack.

You can rely on the TPU Hub to provide regular updates to help you find the latest technical content from across Google. Whether you are just beginning your journey or are a seasoned practitioner looking to squeeze every ounce of performance out of your models, the hub provides a growing list of educational resources required to bridge the gap between concept and production.

New Educational Resources: What You’ll Find

Our content covers the end-to-end developer lifecycle, spanning pre-training, post-training, and inference workloads. The hub provides resources for many layers of your project, from architecting massive training clusters to optimizing for low-latency inference:

Hardware Architecture & Infrastructure Consumption: Gain an understanding of TPU hardware design and foundational architecture. Learn how to access these capabilities effectively across our various Cloud infrastructure consumption modes, including bare-metal kernels and specific Cloud TPU service offerings. We provide clear guidance on selecting the right infrastructure tier to match your specific computational requirements.
Software Stack Capabilities: Learn about the layers of the TPU software stack, including specialized compiler technology and XLA, to ensure your models are running on optimized primitives. Learn how you can migrate and deploy PyTorch on TPU with virtually no migration costs. This section simplifies the transition process for developers already working within common ML frameworks.
Tracing, Debugging & Observability: Utilize advanced telemetry and XProf tooling to gain granular visibility into your workloads, helping you pinpoint performance bottlenecks with precision. Our guides show you how to interpret complex diagnostic data to streamline your iteration cycles. You will learn to monitor system health in real-time, ensuring your models maintain peak efficiency throughout the training or inference process.
Parallelism & Optimization Strategies: Explore advanced scaling techniques, including multi-chip execution models and joint-optimization approaches—such as Pallas kernels—to hill climb your model performance and maximize efficiency. These resources include proven recipes for managing parallelism, from basic configurations to complex, large-scale distributed training setups. We also highlight optimized strategies for advanced inference, such as KV cache offloading.
Networking & Security: Establish a resilient foundation for your distributed training and inference jobs with deep dives into networking foundations and end-to-end security best practices. These modules cover the critical infrastructure requirements for maintaining high-speed communication between chips without sacrificing data integrity. You will learn to architect secure, scalable systems that meet enterprise-grade production standards.

These resources—ranging from interactive Colabs and open-source recipes to deep-dive documentation—are designed to meet your specific needs at every step of your development journey.

Designed for Developers

We know that engineers value practical, code-first learning. That’s why the hub is packed with open-source code recipes and deep-dive technical documentation. These assets are also designed to be agent-ingestion friendly, meaning whether you are browsing manually or using AI-assisted development tools, you can seamlessly integrate our best practices into your workflow.

The TPU Developer Hub is our commitment to making TPUs accessible through an open and easy-to-use ecosystem. We invite you to explore the recipes, follow our how-to guides, and take advantage of the growing collection of educational resources tailored for your success.

Ready to get started? Visit the TPU Developer Hub today and start building the future of AI.