MULTI-INSTANCE GPU（MIG）深度学习最佳用法示例.pdf

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1、NVIDIAMULTI-INSTANCE GPU (MIG）深度学习最佳用法示例张雪萌，杨值#page#AGENDAIntroduction to MIG Multi-Instance GPU）MIG managementKubernetes support for MIGMIG for deep learningTraining#page#MotivationWhy we use Multiple-lnstance GPU (MIG）why？To maximize GPU utilization.when？If your application cannot fully utilize a

2、single GPU.How？UseMG to run multiple workloads in parallel ona single A100 GPU.One GPU to serve single user with multiple applications， or multiple users.#page#MULTI-INSTANCE GPU (MIG）Optimize GPU Utilization， Expand Access to More Users with Guaranteed Quality of ServiceUSERDUp To7GPU Instances In

3、a Single A100；防GPUInstance0Dedicated SM，Memory，L2 cache，Bandwidth forhardware QoS8isolationUSER1GPUInstance1Simultaneous Workload Execution WithUSER2Guaranteed Quality Of Service:GPUInstance2AU MIG instances run in parallel with predictablethroughputalatencyUSER3GPUInstance3公Right Sized GPU Allocati

4、on:Different sized MIG instances based on targetUSER4GPUInstance4workloadsUSEREDiverse Deployment Environments：GPUInstance5Supportedwith Bare metal，Docker，Kubernetes，VirtualizedEnv.USER公HGPU Instance粥MG User Guide:https:/ guide/index.html#page#MIG USE CASESWorkloads need QoS guaranteed with low late

5、ncy responseSingle Tenant:Multiple UsersMultiple Tenant：Multiple UsersSingle User：Multiple AppsSingle or Multiple AppAppADDAPDADSupportinternal usergroupsortoprovideAsingle user running multiple GPU-basedAbilitytoprovide separateGPUInstancesapplicationstodifferentcustomers.EgMultipleinference jobs(e

6、batchsize=1）E.g. Shared resource usageE.g.Jupyternotebooksformodelexploration#page#GPU INSTANCE (GI）Hardware partition of a GPU:Streaming Multiprocessors (SMS)，memory， bandwidth.Applications running simultaneously in all GPU Instances.Ability to provide separate GPU Instances to smal/medium/large si