Provision a GPU-Accelerated TKG Cluster by Using the kubectl Command in a Connected VMware Private AI Foundation with NVIDIA Environment

In VMware Private AI Foundation with NVIDIA, as a DevOps engineer, by using the Kubernetes API, you provision a TKG cluster that uses NVIDIA GPUs. Then, you can deploy container AI workloads from the NVIDIA NGC catalog.

You use kubectl to deploy the TKG cluster on the namespace configured by the cloud administrator.

Prerequisites

Verify with the cloud administrator that the following prerequisites are in place for the AI-ready infrastructure.

• VMware Private AI Foundation with NVIDIA is configured. See Preparing VMware Cloud Foundation for Private AI Workload Deployment.
• In a disconnected environment, a content library with Ubuntu TKr images is added to the vSphere namespace for AI workloads. See Configure a Content Library with Ubuntu TKr for a Disconnected VMware Private AI Foundation with NVIDIA Environment.

Procedure

1. Log in to the Supervisor control plane.

   kubectl vsphere login --server=SUPERVISOR-CONTROL-PLANE-IP-ADDRESS-or-FQDN --vsphere-username USERNAME

2. Provision a TKG cluster and install the NVIDIA GPU Operator and NVIDIA Network Operator on it.
   See Cluster Operator Workflow for Deploying AI/ML Workloads on TKGS Clusters.

What to do next

Deploy an AI container image from the NVIDIA NGC catalog.