# Deployment and usage · Node Feature Discovery
Deployment and usage Table of contents Requirements Deployment options Operator Deployment templates Build your own Usage NFD-Master NFD-Worker TLS...
Author: Kubernetes SIGs

Node Feature Discovery
This documentation is for Node Feature Discovery version that is no longer supported. Please upgrade and visit the documentation of the latest stable release.

# Deployment and usage

## Table of contents

## 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

3. Create `NodeFeatureDiscovery` object (in `nfd` namespace here):

       cat << EOF | kubectl apply -f -
       apiVersion: v1
       kind: Namespace
       metadata:
         name: nfd
       ---
       apiVersion: nfd.kubernetes.io/v1
       kind: NodeFeatureDiscovery
       metadata:
         name: my-nfd-deployment
         namespace: nfd
       spec:
         operand:
           namespace: nfd
           image: k8s.gcr.io/nfd/node-feature-discovery:v0.7.0
           imagePullPolicy: IfNotPresent
       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/v0.7.0/nfd-master.yaml.template
    kubectl apply -f https://raw.githubusercontent.com/kubernetes-sigs/node-feature-discovery/v0.7.0/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/v0.7.0/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=$(kubectl get no -o jsonpath='{.items[*].metadata.name}' | wc -w)
    curl -fs https://raw.githubusercontent.com/kubernetes-sigs/node-feature-discovery/v0.7.0/nfd-worker-job.yaml.template | \
        sed s"/NUM_NODES/$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.

### 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 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 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 on each labeling pass (determined by `--sleep-interval`) which makes run-time re-configuration of nfd-worker possible.

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

The (empty-by-default) example config 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/v0.7.0/nfd-worker-daemonset.yaml.template
    kubectl delete -f https://raw.githubusercontent.com/kubernetes-sigs/node-feature-discovery/v0.7.0/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/v0.7.0/nfd-prune.yaml.template
    kubectl -n node-feature-discovery wait job.batch/nfd-prune --for=condition=complete && \
        kubectl delete -f https://raw.githubusercontent.com/kubernetes-sigs/node-feature-discovery/v0.7.0/nfd-prune.yaml.template

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

Node Feature Discovery
v0.7

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The software is under the terms of Apache License 2.0.