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In this presentation, we will unveil the secrets and journey to enable an IBM's AI Accelerator in Kubernetes. We utilize wide range of tools and frameworks such as device plugin, custom scheduler, metrics exporter, webhooks and custom resources together to satisfy real requirements from various stakeholders - general users, cluster administrators and driver/runtime developers. Our device plugin and custom scheduler can accept special preference such as topology-aware allocation to enable RDMA, and webhook-based validator guides them to follow specification changes. To sync up allocation status among the components, we carefully defined a custom resource after performance estimation. From developer perspective, we provide various debug-purpose capabilities: for example, device allocation by PCI address for inspection, and pseudo device mode to achieve non-real-device test. In addition, multi-architecture support gives freedom of platform choice to all of the participants.
Takuya Mishina is a Staff Research Scientist at IBM Research - Tokyo. He has been working for enhancing cloud infrastructure lifecycle management such as security and compliance posture management. Recent interests include extending the automation mechanism to provide usable AI hardware... Read More →
Tatsuhiro Chiba is a STSM and Manager at IBM Research, specialized in performance optimization and acceleration of large scale AI and HPC workloads on Hybrid Cloud. He is leading a project to enhance OpenShift performance and sustainability for AI and HPC by exploiting various cloud... Read More →
