Merge pull request #44915 from ArangoGutierrez/automatedlabels

Document NFD for GPU Labeling

Author: k8s-ci-robot

content/en/docs/tasks/manage-gpus/scheduling-gpus.md

@@ -64,7 +64,7 @@ spec:
           gpu-vendor.example/example-gpu: 1 # requesting 1 GPU
 ```
 
-## Clusters containing different types of GPUs
+## Manage clusters with different types of GPUs
 
 If different nodes in your cluster have different types of GPUs, then you
 can use [Node Labels and Node Selectors](/docs/tasks/configure-pod-container/assign-pods-nodes/)
@@ -83,10 +83,46 @@ a different label key if you prefer.
 
 ## Automatic node labelling {#node-labeller}
 
-If you're using AMD GPU devices, you can deploy
-[Node Labeller](https://github.com/RadeonOpenCompute/k8s-device-plugin/tree/master/cmd/k8s-node-labeller).
-Node Labeller is a {{< glossary_tooltip text="controller" term_id="controller" >}} that automatically
-labels your nodes with GPU device properties.
+As an administrator, you can automatically discover and label all your GPU enabled nodes
+by deploying Kubernetes [Node Feature Discovery](https://github.com/kubernetes-sigs/node-feature-discovery) (NFD).
+NFD detects the hardware features that are available on each node in a Kubernetes cluster.
+Typically, NFD is configured to advertise those features as node labels, but NFD can also add extended resources, annotations, and node taints.
+NFD is compatible with all [supported versions](/releases/version-skew-policy/#supported-versions) of Kubernetes.
+By default NFD create the [feature labels](https://kubernetes-sigs.github.io/node-feature-discovery/master/usage/features.html) for the detected features.
+Administrators can leverage NFD to also taint nodes with specific features, so that only pods that request those features can be scheduled on those nodes.
 
-Similar functionality for NVIDIA is provided by
-[GPU feature discovery](https://github.com/NVIDIA/gpu-feature-discovery/blob/main/README.md).
+You also need a plugin for NFD that adds appropriate labels to your nodes; these might be generic
+labels or they could be vendor specific. Your GPU vendor may provide a third party
+plugin for NFD; check their documentation for more details.
+
+{{< highlight yaml "linenos=false,hl_lines=6-18" >}}
+apiVersion: v1
+kind: Pod
+metadata:
+  name: example-vector-add
+spec:
+  # You can use Kubernetes node affinity to schedule this Pod onto a node
+  # that provides the kind of GPU that its container needs in order to work
+  affinity:
+    nodeAffinity:
+      requiredDuringSchedulingIgnoredDuringExecution:
+        nodeSelectorTerms:
+        - matchExpressions:
+          - key: "gpu.gpu-vendor.example/installed-memory"
+            operator: Gt # (greater than)
+            values: ["40535"]
+          - key: "feature.node.kubernetes.io/pci-10.present" # NFD Feature label
+            values: ["true"] # (optional) only schedule on nodes with PCI device 10
+  restartPolicy: OnFailure
+  containers:
+    - name: example-vector-add
+      image: "registry.example/example-vector-add:v42"
+      resources:
+        limits:
+          gpu-vendor.example/example-gpu: 1 # requesting 1 GPU
+{{< /highlight >}}
+
+#### GPU vendor implementations
+
+- [Intel](https://intel.github.io/intel-device-plugins-for-kubernetes/cmd/gpu_plugin/README.html)
+- [NVIDIA](https://github.com/NVIDIA/gpu-feature-discovery/#readme)