Pod Failures in Kubernetes: Mastering Challenges with Specialized Devices

Kubernetes is the de facto standard for container orchestration, but when it comes to handling specialized hardware like GPUs and other accelerators, things get complicated. In this article, we explore the challenges that arise in managing failure modes when running Pods with devices in Kubernetes.

Impact on Developers and DevOps Teams

The boom of AI/ML applications brings new challenges for Kubernetes. These workloads often heavily rely on specialized hardware, and a device failure can significantly impact performance and lead to frustrating disruptions. According to the Llama-Paper published in 2024, hardware issues, particularly GPU failures, are one of the main causes of disruptions in AI/ML training.

In a talk by Ryan Hallisey and Piotr Prokop at KubeCon, it was highlighted that NVIDIA receives 19 remediation requests per 1000 nodes daily! The increasing use of spot models in data centers and overcommitment on power supply make device failures the norm and part of the business model.

What is Kubernetes Lacking?

Despite these challenges, Kubernetes’ view on resources remains very static. The resource concept is simple: either the hardware is present or not. If it is present, Kubernetes assumes it remains fully functional. However, it lacks robust support for dealing with complete or partial hardware failures. These outdated assumptions, combined with the overall complexity of a setup, lead to a variety of failure modes.

With these insights in mind, developers and DevOps teams can develop proactive strategies to minimize the impact of hardware failures and increase the resilience of their applications.

Collaborating with partners like ayedo, who have extensive experience in Kubernetes implementation, can help develop robust solutions that tackle the challenges of the modern DevOps environment.

A better understanding of the mechanisms behind the scenes can make the difference when it comes to ensuring the uptime and efficiency of your applications.

Source: Kubernetes Blog