Kubernetes Love Machine Learning, Even on Private Cloud - Hui Luo, VMware

Kubernetes Love Machine Learning, Even on Private Cloud - Hui Luo, VMware

Kubernetes Love Machine Learning, Even on Private Cloud - Kubernetes has established as a good platform for machine learning workloads by extending support of accelerators like GPU, all major public cloud provider are offering GPU enabled Kubernetes services, but public cloud is not the only option for users. This talk is going to cover 3 major challenges that facing private cloud when enable GPU on Kubernetes. I will also demonstrate and discuss some of the projects that help to solve those challenges: 1) Private cloud usually needs to support a wider range of GPU types, in some case, to support heterogeneous GPU in one cluster 2) To support complex hardware topology like RDMA, NVLINK 3) GPU resource contention is usually very high when limited GPU resource shared by multiple teams

Kubernetes Love Machine Learning, Even on Private Cloud - Hui Luo, VMware

Kubernetes has established as a good platform for machine learning workloads by extending support of accelerators like GPU, all major public cloud provider are offering GPU enabled Kubernetes services, but public cloud is not the only option for users. There are ongoing efforts from the community to make running machine learning workloads with Kubernetes on private cloud as easy as on public cloud. This talk is going to cover 3 major challenges that facing private cloud when enable GPU on Kubernetes. I will also demonstrate and discuss some of the projects that help to solve those challenges: 1) Private cloud usually needs to support a wider range of GPU types, in some case, to support heterogeneous GPU in one cluster 2) To support complex hardware topology like RDMA, NVLINK 3) GPU resource contention is usually very high when limited GPU resource shared by multiple teams

kubernetes machine-learning

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