GPU Sharing Scheduler Extender in Kubernetes
More and more data scientists run their Nvidia GPU based inference tasks on Kubernetes. Some of these tasks can be run on the same Nvidia GPU device to increase GPU utilization. So one important challenge is how to share GPUs between the pods. The community is also very interested in this topic.
Now there is a GPU sharing solution on native Kubernetes: it is based on scheduler extenders and device plugin mechanism, so you can reuse this solution easily in your own Kubernetes.
- Kubernetes 1.11+
- golang 1.10+
- NVIDIA drivers ~= 361.93
- Nvidia-docker version > 2.0 (see how to install and it's prerequisites)
- Docker configured with Nvidia as the default runtime.
For more details about the design of this project, please read this Design document.
You can check this User Guide.
git clone https://github.com/AliyunContainerService/gpushare-scheduler-extender.git && cd gpushare-scheduler-extender docker build -t cheyang/gpushare-scheduler-extender .
git clone https://github.com/AliyunContainerService/gpushare-device-plugin.git && cd gpushare-device-plugin docker build -t cheyang/gpushare-device-plugin .
- golang > 1.10
mkdir -p $GOPATH/src/github.com/AliyunContainerService cd $GOPATH/src/github.com/AliyunContainerService git clone https://github.com/AliyunContainerService/gpushare-device-plugin.git cd gpushare-device-plugin go build -o $GOPATH/bin/kubectl-inspect-gpushare-v2 cmd/inspect/*.go
- Demo 1: Deploy multiple GPU Shared Pods and schedule them on the same GPU device in binpack way
- Demo 2: Avoid GPU memory requests that fit at the node level, but not at the GPU device level
- Integrate Nvidia MPS as the option for isolation
- Automated Deployment for the Kubernetes cluster which is deployed by kubeadm
- Scheduler Extener High Availablity
- Generic Solution for GPU, RDMA and other devices
If you are intrested in GPUShare and would like to share your experiences with others, you are warmly welcome to add your information on ADOPTERS.md page. We will continuousely discuss new requirements and feature design with you in advance.