Set Up Kubernetes Master on Google Cloud with On-Premise GPUs

To create a Kubernetes master on Google Cloud and add on-premise instances with GPUs as nodes, follow these steps:

Step 1: Set Up the Kubernetes Master on Google Cloud

1. Create a Google Kubernetes Engine (GKE) Cluster:

• Go to the Google Cloud Console.
• Navigate to the Kubernetes Engine section and click on Clusters.
• Click Create and configure your cluster:
  • Choose the Standard cluster type.
  • Select a region or zone close to your on-premise instances.
  • Configure the master and node settings as needed.
• Click Create to launch the cluster.

2. Obtain Cluster Credentials:

• After the cluster is created, click on the cluster name to view details.
• Click on the Connect button to get the command for obtaining the cluster credentials.
• Run the provided gcloud command in your terminal to authenticate your local environment with the GKE cluster:
  sh gcloud container clusters get-credentials <cluster-name> --region <region>

Step 2: Set Up On-Premise Nodes with GPU

1. Install Kubernetes Components on On-Premise Instances:

• On each on-premise instance, install Docker, kubeadm, kubelet, and kubectl:
  sh sudo apt-get update sudo apt-get install -y docker.io sudo apt-get install -y apt-transport-https curl curl -s https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add - sudo apt-add-repository "deb http://apt.kubernetes.io/ kubernetes-xenial main" sudo apt-get install -y kubelet kubeadm kubectl sudo apt-mark hold kubelet kubeadm kubectl

2. Install NVIDIA Drivers and GPU Support:

• Install the NVIDIA drivers and CUDA Toolkit:
  sh sudo apt-get install -y nvidia-driver-450 sudo apt-get install -y nvidia-cuda-toolkit
• Install the NVIDIA Kubernetes device plugin:
  sh kubectl apply -f https://github.com/NVIDIA/k8s-device-plugin/blob/master/nvidia-device-plugin.yml

3. Join the On-Premise Nodes to the GKE Cluster:

• On your GKE master node, create a token to allow nodes to join the cluster:
  sh kubeadm token create --print-join-command
• The output will provide a command that you need to run on each on-premise instance:
  sh kubeadm join <master-ip>:<port> --token <token> --discovery-token-ca-cert-hash sha256:<hash>
• Run this command on each of your on-premise instances to join them to the cluster.

Step 3: Verify and Configure the Cluster

1. Verify Nodes in the Cluster:

• Run the following command on your GKE master to see all nodes:
  sh kubectl get nodes
• You should see both the GKE nodes and the on-premise nodes listed.

2. Configure Workloads to Use GPUs:

• To use the GPUs on your on-premise nodes, create a Kubernetes deployment that requests GPU resources:
  yaml apiVersion: apps/v1
kind: Deployment
metadata:
name: gpu-workload
spec:
replicas: 1
template:
metadata:
labels:
app: gpu-workload
spec:
containers:
- name: gpu-container
image: <your-gpu-enabled-image>
resources:
limits:
nvidia.com/gpu: 1 # Request one GPU
  
• Deploy this configuration with:
  sh kubectl apply -f gpu-deployment.yaml

Step 4: Monitor and Manage the Cluster

• Use Kubernetes tools like kubectl, Kubernetes Dashboard, or other monitoring tools to manage your cluster.
• Regularly update and patch both your GKE cluster and on-premise nodes to ensure compatibility and security.

Important Notes

• Ensure network connectivity between your GKE cluster and on-premise instances. You might need a VPN or a secure connection to manage this.
• Properly configure firewall rules to allow traffic between the GKE master and on-premise nodes.
• Regularly update GPU drivers and Kubernetes versions on both GKE and on-premise nodes.

This setup will allow you to have a Kubernetes cluster spanning both Google Cloud and your on-premise infrastructure with GPU capabilities.

Conclusion

To simply create a Kubernetes master on Google Cloud and also want to add on-premise instances along with GPUs as nodes, then follow all the above-mentioned steps in the described order. For doing it seamlessly, make sure that you have the best GPU dedicated server.