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Achieving scalable and fault-tolerant distributed AI model training that runs efficiently across multiple nodes remains a key challenge for platform administrators and ML engineers. This problem is further exacerbated by interactive GPU workloads, such as Jupyter notebooks, that generate intermittent computations followed by idle periods while users refine their code and explore the results.
This talk will present how transparent GPU checkpointing can be integrated with Kubernetes to improve both cost efficiency and cluster utilization for distributed AI workloads. By automatically capturing and restoring the state of training jobs, this approach enables seamless recovery from preemptions or failures. This session will also explore how checkpoint policies integrate with the Kueue, JobSet, and TrainJob APIs for Kubernetes-native, infrastructure-level checkpointing of GPU workloads - empowering users to leverage preemptible spot instances for reliable, cost-effective AI model training.
Andrey Velichkevich is a Senior Software Engineer at Apple and is a key contributor to the Kubeflow open-source project. He is a member of Kubeflow Steering Committee and a co-chair of Kubeflow AutoML and Training WG. Additionally, Andrey is an active member of the CNCF WG AI. He... Read More →
Radostin Stoyanov is a PhD student in the Scientific Computing research group at the University of Oxford. His research focuses on improving the resilience and performance of HPC and cloud computing systems.
