#Create Hardware Profile using CLI

This document describes how to create HardwareProfile resources using the command line and provides a sample YAML.

#Prerequisites

• You have access to a Kubernetes cluster with the platform installed.
• You have configured kubectl to communicate with your cluster.
• You have a namespace where you have permissions to view or create HardwareProfile resources (typically a cluster-scoped resource or in a specific admin namespace).

#Create a HardwareProfile

Create a YAML file named gpu-high-performance-profile.yaml with the following content:

apiVersion: infrastructure.opendatahub.io/v1alpha1
kind: HardwareProfile
metadata:
  name: gpu-high-performance-profile
  namespace: kube-public
spec:
  # Define resource limitations and defaults
  identifiers:
    - identifier: "nvidia.com/gpu"
      displayName: "GPU"
      minCount: "1"
      maxCount: "8"
      defaultCount: "1"
      resourceType: Accelerator
    - identifier: "cpu"
      displayName: "CPU"
      minCount: "4"
      maxCount: "32"
      defaultCount: "8"
      resourceType: CPU
    - identifier: "memory"
      displayName: "Memory"
      minCount: "16Gi"
      maxCount: "128Gi"
      defaultCount: "32Gi"
      resourceType: Memory
  # Configure Node Selectors and Tolerations for scheduling
  scheduling:
    type: Node
    node:
      nodeSelector:
        accelerator: nvidia-a100
        node-role.kubernetes.io/worker: "true"
      tolerations:
        - key: "nvidia.com/gpu"
          operator: "Exists"
          effect: "NoSchedule"

Then apply the YAML file to your cluster using kubectl:

kubectl apply -f gpu-high-performance-profile.yaml -n kube-public

#Check HardwareProfile Status

You can check whether the HardwareProfile has been successfully created using the following command:

kubectl get hardwareprofile gpu-high-performance-profile -n kube-public

The output should look similar to this:

NAME                           AGE
gpu-high-performance-profile   2m

Once correctly applied, your Data Scientists will be able to select GPU High Performance when deploying their Inference Services using the UI, and the constraints specified in the profile will automatically validate and inject into the deployed workloads.