Kubernetes offers a powerful orchestration platform for machine learning training, but memory management can be challenging due to specialized needs and resource constraints. This paper outlines how Kubernetes handles memory requests, limits, Quality of Service classes, and eviction policies for ML workloads, with special focus on GPU memory and ephemeral storage. Common pitfalls such as overcommitment, memory leaks, and ephemeral volume exhaustion are examined. We then provide best practices for stable, scalable memory utilization to help ML practitioners prevent out-of-memory events and ensure high-performance ML training pipelines.

翻译：Kubernetes为机器学习训练提供了强大的编排平台，但由于特殊需求和资源限制，内存管理可能颇具挑战。本文阐述了Kubernetes如何为ML工作负载处理内存请求、限制、服务质量类别和驱逐策略，特别关注GPU内存和临时存储。文中分析了过度承诺、内存泄漏和临时卷耗尽等常见陷阱。我们随后提供了稳定、可扩展的内存使用最佳实践，以帮助ML从业者防止内存不足事件，确保高性能的ML训练流水线。