Taming the Memory Beast: Strategies for Reliable ML Training on Kubernetes
It addresses memory management issues for ML practitioners using Kubernetes, but it is incremental as it focuses on applying existing Kubernetes features rather than introducing new methods.
This paper tackles the problem of memory management challenges in machine learning training on Kubernetes, examining how Kubernetes handles memory and providing best practices to prevent out-of-memory events and ensure stable, scalable training pipelines.
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.