unlocking-the-power-of-kubernetes-ai-driven-innovations-for-next-gen-infrastructure-han-kubernetes-zha-hao-daepxiao-27dya-shi-zha-ai-tan-brandon-kang-akamai-technologies.pdf

1、Unlocking the Power of Kubernetes(for AI)AI-Driven Innovations for Next-Gen InfrastructureBrandon Kang Who am I?Brandon Kang(姜相鎭)Akamai Technologies,APJ CTGPrincipal Technical Solutions ArchitectBack to Basics Why Kubernetes?Scalability&PortabilitySelf-Healing&ExtensibilityResource OptimizationAutom

2、ated Rollouts and RollbacksService Discovery and Load BalancingEtc.Why Deploy AI on Kubernetes?Dynamic Resource ScalingBurst WorkloadsResource IsolationShared InfrastructureGPU/TPU ManagementEfficient UtilizationConsistencyObservabilityWhy Deploy AI on Kubernetes?Resource ManagementKubernetes can sc

3、hedule and allocate resources efficiently,ensuring that AI workloads make optimal use of available hardware,such as GPUs and TPUs,which are often essential for AI tasksCost ManagementEfficient resource management helps in controlling costs,especially in cloud environments where resource usage direct

4、ly impacts expenses.Why Deploy AI on Kubernetes?Custom Resource Definitions(CRDs)Kubernetes allows for the creation of custom resources to support specialized AI workloads and requirementsIntegration with AI ToolsKubernetes integrates well with various AI frameworks and tools,such as TensorFlow,PyTo

5、rch,and Nvidias NeMo,facilitating seamless deployment and operation of AI modelsWhy Deploy AI on Kubernetes?Why organizations deploy workloads on containers(Source:Red Hat)Why Deploy AI on Kubernetes?Major types of workloads and systems containerized with Kubernetes(Source:Red Hat)Using GPUs for AI

6、WorkloadsUsing GPUs on Kubernetes NodesDynamic Resource AllocationKubernetes can dynamically allocate GPU resources based on workload requirements,ensuring optimal utilization and minimizing idle resourcesCost EfficiencyBy efficiently managing GPU resources,organizations can control costs,especially