Google adds GPU & TPU support to GKE Autopilot
Google has updated its GKE Autopilot service to include support for GPU and TPU resources, allowing for automated network resource allocation. This update simplifies the management of accelerator-based workloads by integrating custom ComputeClass definitions.
Why it matters
This enhancement streamlines cloud infrastructure management for developers running high-performance AI and machine learning workloads on Kubernetes.
Google has added GPU and TPU support to managed DRANET on Google Kubernetes Engine Autopilot clusters, extending automated network resource allocation for accelerator-based workloads in its managed Kubernetes service.
GKE Autopilot users can now set up Pods that request network interfaces for TPUs and Remote Direct Memory Access, or RDMA, without managing the underlying nodes directly. The feature works through a combination of Autopilot clusters, custom ComputeClass definitions and ResourceClaimTemplate objects that tie workloads to the required networking resources.
Autopilot is Google's managed mode for GKE, in which the company handles node management, scaling, security settings and other cluster configuration tasks. Managed DRANET lets users request and allocate networking resources for Pods, including interfaces needed for accelerator-based workloads.
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