diff --git a/content/en/docs/tasks/manage-gpus/scheduling-gpus.md b/content/en/docs/tasks/manage-gpus/scheduling-gpus.md index b8ddf0946e..4f1857138e 100644 --- a/content/en/docs/tasks/manage-gpus/scheduling-gpus.md +++ b/content/en/docs/tasks/manage-gpus/scheduling-gpus.md @@ -10,133 +10,57 @@ description: Configure and schedule GPUs for use as a resource by nodes in a clu {{< feature-state state="beta" for_k8s_version="v1.10" >}} -Kubernetes includes **experimental** support for managing AMD and NVIDIA GPUs +Kubernetes includes **experimental** support for managing GPUs (graphical processing units) across several nodes. -This page describes how users can consume GPUs across different Kubernetes versions -and the current limitations. - - - +This page describes how users can consume GPUs, and outlines +some of the limitations in the implementation. ## Using device plugins -Kubernetes implements {{< glossary_tooltip text="Device Plugins" term_id="device-plugin" >}} +Kubernetes implements {{< glossary_tooltip text="device plugins" term_id="device-plugin" >}} to let Pods access specialized hardware features such as GPUs. As an administrator, you have to install GPU drivers from the corresponding hardware vendor on the nodes and run the corresponding device plugin from the -GPU vendor: +GPU vendor. Here are some links to vendors' instructions: -* [AMD](#deploying-amd-gpu-device-plugin) -* [NVIDIA](#deploying-nvidia-gpu-device-plugin) +* [AMD](https://github.com/RadeonOpenCompute/k8s-device-plugin#deployment) +* [NVIDIA](https://github.com/NVIDIA/k8s-device-plugin#quick-start) -When the above conditions are true, Kubernetes will expose `amd.com/gpu` or -`nvidia.com/gpu` as a schedulable resource. +Once you have installed the plugin, your cluster exposes a custom schedulable resource such as `amd.com/gpu` or `nvidia.com/gpu`. You can consume these GPUs from your containers by requesting -`.com/gpu` the same way you request `cpu` or `memory`. -However, there are some limitations in how you specify the resource requirements -when using GPUs: +the custom GPU resource, the same way you request `cpu` or `memory`. +However, there are some limitations in how you specify the resource +requirements for custom devices. -- GPUs are only supposed to be specified in the `limits` section, which means: - * You can specify GPU `limits` without specifying `requests` because - Kubernetes will use the limit as the request value by default. - * You can specify GPU in both `limits` and `requests` but these two values - must be equal. - * You cannot specify GPU `requests` without specifying `limits`. -- Containers (and Pods) do not share GPUs. There's no overcommitting of GPUs. -- Each container can request one or more GPUs. It is not possible to request a - fraction of a GPU. +GPUs are only supposed to be specified in the `limits` section, which means: +* You can specify GPU `limits` without specifying `requests`, because + Kubernetes will use the limit as the request value by default. +* You can specify GPU in both `limits` and `requests` but these two values + must be equal. +* You cannot specify GPU `requests` without specifying `limits`. -Here's an example: +Here's an example manifest for a Pod that requests a GPU: ```yaml apiVersion: v1 kind: Pod metadata: - name: cuda-vector-add + name: example-vector-add spec: restartPolicy: OnFailure containers: - - name: cuda-vector-add - # https://github.com/kubernetes/kubernetes/blob/v1.7.11/test/images/nvidia-cuda/Dockerfile - image: "registry.k8s.io/cuda-vector-add:v0.1" + - name: example-vector-add + image: "registry.example/example-vector-add:v42" resources: limits: - nvidia.com/gpu: 1 # requesting 1 GPU + gpu-vendor.example/example-gpu: 1 # requesting 1 GPU ``` -### Deploying AMD GPU device plugin - -The [official AMD GPU device plugin](https://github.com/RadeonOpenCompute/k8s-device-plugin) -has the following requirements: - -- Kubernetes nodes have to be pre-installed with AMD GPU Linux driver. - -To deploy the AMD device plugin once your cluster is running and the above -requirements are satisfied: -```shell -kubectl create -f https://raw.githubusercontent.com/RadeonOpenCompute/k8s-device-plugin/v1.10/k8s-ds-amdgpu-dp.yaml -``` - -You can report issues with this third-party device plugin by logging an issue in -[RadeonOpenCompute/k8s-device-plugin](https://github.com/RadeonOpenCompute/k8s-device-plugin). - -### Deploying NVIDIA GPU device plugin - -There are currently two device plugin implementations for NVIDIA GPUs: - -#### Official NVIDIA GPU device plugin - -The [official NVIDIA GPU device plugin](https://github.com/NVIDIA/k8s-device-plugin) -has the following requirements: - -- Kubernetes nodes have to be pre-installed with NVIDIA drivers. -- Kubernetes nodes have to be pre-installed with [nvidia-docker 2.0](https://github.com/NVIDIA/nvidia-docker) -- Kubelet must use Docker as its container runtime -- `nvidia-container-runtime` must be configured as the [default runtime](https://github.com/NVIDIA/k8s-device-plugin#preparing-your-gpu-nodes) - for Docker, instead of runc. -- The version of the NVIDIA drivers must match the constraint ~= 384.81. - -To deploy the NVIDIA device plugin once your cluster is running and the above -requirements are satisfied: - -```shell -kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/1.0.0-beta4/nvidia-device-plugin.yml -``` - -You can report issues with this third-party device plugin by logging an issue in -[NVIDIA/k8s-device-plugin](https://github.com/NVIDIA/k8s-device-plugin). - -#### NVIDIA GPU device plugin used by GCE - -The [NVIDIA GPU device plugin used by GCE](https://github.com/GoogleCloudPlatform/container-engine-accelerators/tree/master/cmd/nvidia_gpu) -doesn't require using nvidia-docker and should work with any container runtime -that is compatible with the Kubernetes Container Runtime Interface (CRI). It's tested -on [Container-Optimized OS](https://cloud.google.com/container-optimized-os/) -and has experimental code for Ubuntu from 1.9 onwards. - -You can use the following commands to install the NVIDIA drivers and device plugin: - -```shell -# Install NVIDIA drivers on Container-Optimized OS: -kubectl create -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/stable/daemonset.yaml - -# Install NVIDIA drivers on Ubuntu (experimental): -kubectl create -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/stable/nvidia-driver-installer/ubuntu/daemonset.yaml - -# Install the device plugin: -kubectl create -f https://raw.githubusercontent.com/kubernetes/kubernetes/release-1.14/cluster/addons/device-plugins/nvidia-gpu/daemonset.yaml -``` - -You can report issues with using or deploying this third-party device plugin by logging an issue in -[GoogleCloudPlatform/container-engine-accelerators](https://github.com/GoogleCloudPlatform/container-engine-accelerators). - -Google publishes its own [instructions](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus) for using NVIDIA GPUs on GKE . - ## Clusters containing different types of GPUs If different nodes in your cluster have different types of GPUs, then you @@ -147,10 +71,13 @@ For example: ```shell # Label your nodes with the accelerator type they have. -kubectl label nodes accelerator=nvidia-tesla-k80 -kubectl label nodes accelerator=nvidia-tesla-p100 +kubectl label nodes node1 accelerator=example-gpu-x100 +kubectl label nodes node2 accelerator=other-gpu-k915 ``` +That label key `accelerator` is just an example; you can use +a different label key if you prefer. + ## Automatic node labelling {#node-labeller} If you're using AMD GPU devices, you can deploy @@ -179,19 +106,18 @@ kubectl describe node cluster-node-23 ``` ``` - Name: cluster-node-23 - Roles: - Labels: beta.amd.com/gpu.cu-count.64=1 - beta.amd.com/gpu.device-id.6860=1 - beta.amd.com/gpu.family.AI=1 - beta.amd.com/gpu.simd-count.256=1 - beta.amd.com/gpu.vram.16G=1 - beta.kubernetes.io/arch=amd64 - beta.kubernetes.io/os=linux - kubernetes.io/hostname=cluster-node-23 - Annotations: kubeadm.alpha.kubernetes.io/cri-socket: /var/run/dockershim.sock - node.alpha.kubernetes.io/ttl: 0 - … +Name: cluster-node-23 +Roles: +Labels: beta.amd.com/gpu.cu-count.64=1 + beta.amd.com/gpu.device-id.6860=1 + beta.amd.com/gpu.family.AI=1 + beta.amd.com/gpu.simd-count.256=1 + beta.amd.com/gpu.vram.16G=1 + kubernetes.io/arch=amd64 + kubernetes.io/os=linux + kubernetes.io/hostname=cluster-node-23 +Annotations: node.alpha.kubernetes.io/ttl: 0 +… ``` With the Node Labeller in use, you can specify the GPU type in the Pod spec: @@ -210,11 +136,14 @@ spec: resources: limits: nvidia.com/gpu: 1 - nodeSelector: - accelerator: nvidia-tesla-p100 # or nvidia-tesla-k80 etc. + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + – matchExpressions: + – key: beta.amd.com/gpu.family.RV # Raven GPU family + operator: Exist ``` -This will ensure that the Pod will be scheduled to a node that has the GPU type +This ensures that the Pod will be scheduled to a node that has the GPU type you specified. - -