node-feature-discovery/docs/get-started/deployment-and-usage.md

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Deployment and Usage	default	3

Deployment and Usage

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Requirements

1. Linux (x86_64/Arm64/Arm)
2. kubectl (properly set up and configured to work with your Kubernetes cluster)

Deployment options

Operator

Deployment using the Node Feature Discovery Operator is recommended to be done via operatorhub.io.

1. You need to have OLM installed. If you don't, take a look at the latest release for detailed instructions.
2. Install the operator:

kubectl create -f https://operatorhub.io/install/nfd-operator.yaml

1. Create NodeFeatureDiscovery resource (in nfd namespace here):

cat << EOF | kubectl apply -f -
apiVersion: v1
kind: Namespace
metadata:
  name: nfd
---
apiVersion: nfd.kubernetes.io/v1alpha1
kind: NodeFeatureDiscovery
metadata:
  name: my-nfd-deployment
  namespace: nfd
EOF

Deployment Templates

The template specs provided in the repo can be used directly:

kubectl apply -f https://raw.githubusercontent.com/kubernetes-sigs/node-feature-discovery/{{ site.release }}/nfd-master.yaml.template
kubectl apply -f https://raw.githubusercontent.com/kubernetes-sigs/node-feature-discovery/{{ site.release }}/nfd-worker-daemonset.yaml.template

This will required RBAC rules and deploy nfd-master (as a deployment) and nfd-worker (as a daemonset) in the node-feature-discovery namespace.

Alternatively you can download the templates and customize the deployment manually.

Master-Worker Pod

You can also run nfd-master and nfd-worker inside the same pod

kubectl apply -f https://raw.githubusercontent.com/kubernetes-sigs/node-feature-discovery/{{ site.release }}/nfd-daemonset-combined.yaml.template

This creates a DaemonSet runs both nfd-worker and nfd-master in the same Pod. In this case no nfd-master is run on the master node(s), but, the worker nodes are able to label themselves which may be desirable e.g. in single-node setups.

Worker One-shot

Feature discovery can alternatively be configured as a one-shot job. The Job template may be used to achieve this:

NUM_NODES=$(kubectl get no -o jsonpath='{.items[*].metadata.name}' | wc -w)
curl -fs https://raw.githubusercontent.com/kubernetes-sigs/node-feature-discovery/{{ site.release }}/nfd-worker-job.yaml.template | \
    sed s"/NUM_NODES/$NUM_NODES/" | \
    kubectl apply -f -

The example above launces as many jobs as there are non-master nodes. Note that this approach does not guarantee running once on every node. For example, tainted, non-ready nodes or some other reasons in Job scheduling may cause some node(s) will run extra job instance(s) to satisfy the request.

Deployment with Helm

Node Feature Discovery Helm chart allow to easily deploy and manage NFD.

Prerequisites

Helm package manager should be installed.

Deployment with Helm

To install the chart with the release name node-feature-discovery:

git clone https://github.com/kubernetes-sigs/node-feature-discovery/
cd node-feature-discovery/deployment
export NFD_NS=node-feature-discovery
helm install node-feature-discovery ./node-feature-discovery/ --namespace $NFD_NS --create-namespace

The command deploys Node Feature Discovery on the Kubernetes cluster in the default configuration. The Configuration section describes how it can be configured during installation.

Configuration

You can override values from values.yaml and provide a file with custom values:

export NFD_NS=node-feature-discovery
helm install node-feature-discovery ./node-feature-discovery/ -f <path/to/custom/values.yaml> --namespace $NFD_NS --create-namespace

To specify each parameter separately you can provide them to helm install command:

export NFD_NS=node-feature-discovery
helm install node-feature-discovery ./node-feature-discovery/ --set nameOverride=NFDinstance --set master.replicaCount=2 --namespace $NFD_NS --create-namespace

Uninstalling the Chart

To uninstall the node-feature-discovery deployment:

export NFD_NS=node-feature-discovery
helm uninstall node-feature-discovery --namespace $NFD_NS

The command removes all the Kubernetes components associated with the chart and deletes the release.

Chart Parameters

In order to tailor the deployment of the Node Feature Discovery to your cluster needs We have introduced the following Chart parameters.

General parameters

Name	Type	Default	description
image.repository	string	gcr.io/k8s-staging-nfd/node-feature-discovery	NFD image repository
image.pullPolicy	string	Always	Image pull policy
imagePullSecrets	list	[]	ImagePullSecrets is an optional list of references to secrets in the same namespace to use for pulling any of the images used by this PodSpec. If specified, these secrets will be passed to individual puller implementations for them to use. For example, in the case of docker, only DockerConfig type secrets are honored. [https://kubernetes.io/docs/concepts/containers/images#specifying-imagepullsecrets-on-a-pod](More info)
serviceAccount.create	bool	true	Specifies whether a service account should be created
serviceAccount.annotations	dict	{}	Annotations to add to the service account
serviceAccount.name	string		The name of the service account to use. If not set and create is true, a name is generated using the fullname template
rbac	dict		RBAC parameteres
nameOverride	string		Override the name of the chart
fullnameOverride	string		Override a default fully qualified app name

Master pod parameters

| master.* | dict | | NFD master deployment configuration | | master.instance | string | | Instance name. Used to separate annotation namespaces for multiple parallel deployments | | master.replicaCount | integer | 1 | Number of desired pods. This is a pointer to distinguish between explicit zero and not specified | | master.podSecurityContext | dict | {} | SecurityContext holds pod-level security attributes and common container settings | | master.service.type | string | ClusterIP | NFD master service type | | master.service.port | integer | port | NFD master service port | | master.resources | dict | {} | NFD master pod resources management | | master.nodeSelector | dict | {} | NFD master pod node selector | | master.tolerations | dict | Scheduling to master node is disabled | NFD master pod tolerations | | master.annotations | dict | {} | NFD master pod metadata | | master.affinity | dict | | NFD master pod required node affinity |

Worker pod parameters

| worker.* | dict | | NFD master daemonset configuration | | worker.configmapName | string | nfd-worker-conf | NFD worker pod ConfigMap name | | worker.config | string | `` | NFD worker service configuration | | worker.podSecurityContext | dict | {} | SecurityContext holds pod-level security attributes and common container settings | | worker.securityContext | dict | {} | Container security settings | | worker.resources | dict | {} | NFD worker pod resources management | | worker.nodeSelector | dict | {} | NFD worker pod node selector | | worker.tolerations | dict | {} | NFD worker pod node tolerations | | worker.annotations | dict | {} | NFD worker pod metadata |

Build Your Own

If you want to use the latest development version (master branch) you need to build your own custom image. See the Developer Guide for instructions how to build images and deploy them on your cluster.

Usage

NFD-Master

NFD-Master runs as a deployment (with a replica count of 1), by default it prefers running on the cluster's master nodes but will run on worker nodes if no master nodes are found.

For High Availability, you should simply increase the replica count of the deployment object. You should also look into adding inter-pod affinity to prevent masters from running on the same node. However note that inter-pod affinity is costly and is not recommended in bigger clusters.

NFD-Master listens for connections from nfd-worker(s) and connects to the Kubernetes API server to add node labels advertised by them.

If you have RBAC authorization enabled (as is the default e.g. with clusters initialized with kubeadm) you need to configure the appropriate ClusterRoles, ClusterRoleBindings and a ServiceAccount in order for NFD to create node labels. The provided template will configure these for you.

NFD-Worker

NFD-Worker is preferably run as a Kubernetes DaemonSet. This assures re-labeling on regular intervals capturing changes in the system configuration and mames sure that new nodes are labeled as they are added to the cluster. Worker connects to the nfd-master service to advertise hardware features.

When run as a daemonset, nodes are re-labeled at an interval specified using the -sleep-interval option. In the [template](https://github.com/kubernetes-sigs/node-feature-discovery/blob/{{ site.release }}/nfd-worker-daemonset.yaml.template#L26) the default interval is set to 60s which is also the default when no -sleep-interval is specified. Also, the configuration file is re-read on each iteration providing a simple mechanism of run-time reconfiguration.

TLS authentication

NFD supports mutual TLS authentication between the nfd-master and nfd-worker instances. That is, nfd-worker and nfd-master both verify that the other end presents a valid certificate.

TLS authentication is enabled by specifying -ca-file, -key-file and -cert-file args, on both the nfd-master and nfd-worker instances. The template specs provided with NFD contain (commented out) example configuration for enabling TLS authentication.

The Common Name (CN) of the nfd-master certificate must match the DNS name of the nfd-master Service of the cluster. By default, nfd-master only check that the nfd-worker has been signed by the specified root certificate (-ca-file). Additional hardening can be enabled by specifying -verify-node-name in nfd-master args, in which case nfd-master verifies that the NodeName presented by nfd-worker matches the Common Name (CN) of its certificate. This means that each nfd-worker requires a individual node-specific TLS certificate.

Configuration

NFD-Worker supports dynamic configuration through a configuration file. The default location is /etc/kubernetes/node-feature-discovery/nfd-worker.conf, but, this can be changed by specifying the-config command line flag. Configuration file is re-read whenever it is modified which makes run-time re-configuration of nfd-worker straightforward.

Worker configuration file is read inside the container, and thus, Volumes and VolumeMounts are needed to make your configuration available for NFD. The preferred method is to use a ConfigMap which provides easy deployment and re-configurability.

The provided nfd-worker deployment templates create an empty configmap and mount it inside the nfd-worker containers. Configuration can be edited with:

kubectl -n ${NFD_NS} edit configmap nfd-worker-conf

See nfd-worker configuration file reference for more details. The (empty-by-default) [example config](https://github.com/kubernetes-sigs/node-feature-discovery/blob/{{ site.release }}/nfd-worker.conf.example) contains all available configuration options and can be used as a reference for creating creating a configuration.

Configuration options can also be specified via the -options command line flag, in which case no mounts need to be used. The same format as in the config file must be used, i.e. JSON (or YAML). For example:

-options='{"sources": { "pci": { "deviceClassWhitelist": ["12"] } } }'

Configuration options specified from the command line will override those read from the config file.

Using Node Labels

Nodes with specific features can be targeted using the nodeSelector field. The following example shows how to target nodes with Intel TurboBoost enabled.

apiVersion: v1
kind: Pod
metadata:
  labels:
    env: test
  name: golang-test
spec:
  containers:
    - image: golang
      name: go1
  nodeSelector:
    feature.node.kubernetes.io/cpu-pstate.turbo: 'true'

For more details on targeting nodes, see node selection.

Uninstallation

Operator Was Used for Deployment

If you followed the deployment instructions above you can simply do:

kubectl -n nfd delete NodeFeatureDiscovery my-nfd-deployment

Optionally, you can also remove the namespace:

kubectl delete ns nfd

See the node-feature-discovery-operator and OLM project documentation for instructions for uninstalling the operator and operator lifecycle manager, respectively.

Manual

Simplest way is to invoke kubectl delete on the deployment files you used. Beware that this will also delete the namespace that NFD is running in. For example:

kubectl delete -f https://raw.githubusercontent.com/kubernetes-sigs/node-feature-discovery/{{ site.release }}/nfd-worker-daemonset.yaml.template
kubectl delete -f https://raw.githubusercontent.com/kubernetes-sigs/node-feature-discovery/{{ site.release }}/nfd-master.yaml.template

Alternatively you can delete create objects one-by-one, depending on the type of deployment, for example:

NFD_NS=node-feature-discovery
kubectl -n $NFD_NS delete ds nfd-worker
kubectl -n $NFD_NS delete deploy nfd-master
kubectl -n $NFD_NS delete svc nfd-master
kubectl -n $NFD_NS delete sa nfd-master
kubectl delete clusterrole nfd-master
kubectl delete clusterrolebinding nfd-master

Removing Feature Labels

NFD-Master has a special -prune command line flag for removing all nfd-related node labels, annotations and extended resources from the cluster.

kubectl apply -f https://raw.githubusercontent.com/kubernetes-sigs/node-feature-discovery/{{ site.release }}/nfd-prune.yaml.template
kubectl -n node-feature-discovery wait job.batch/nfd-prune --for=condition=complete && \
    kubectl delete -f kubectl apply -f https://raw.githubusercontent.com/kubernetes-sigs/node-feature-discovery/{{ site.release }}/nfd-prune.yaml.template

NOTE: You must run prune before removing the RBAC rules (serviceaccount, clusterrole and clusterrolebinding).