February 2026
Intermediate to advanced
406 pages
4h 57m
Chinese
Content preview from Kubernetes 上的生成式人工智能 (Chinese Edition)
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第三部分：调优
本部分涵盖LLM的调优工作，重点探讨在Kubernetes环境中管理和优化这类高负载任务所面临的运维挑战。 生成式人工智能生命周期 的关键差异在于从训练到调优的转变。不同于传统机器学习通常需要从零开始训练模型，此处我们通常基于大型预训练基础模型展开工作。定制化作为后续可选步骤，旨在将模型专用于特定目的。 目标在于精炼而非从头创建。
尽管模型调优技术日新月异，但调度、资源管理和成本优化的运维难题始终存在。
本部分各章节将阐述如何在Kubernetes生态系统中应对这些挑战：
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第6章《模型定制》阐述了LLM的定制技术，重点探讨常见挑战及Kubernetes生态系统中项目的解决方案。
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第7章《任务调度优化》聚焦于任务调度、配额管理以及针对Kubernetes调优工作负载的GPU配置优化。
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