Dedicated GPUs for Kubernetes Pods? Here’s How It Works!

Yes, in Kubernetes, each GPU can be dedicated to a specific Pod. Kubernetes supports GPU scheduling, allowing you to assign Dedicated GPU to Pods just as you would with CPUs or memory.

To dedicate a GPU to a specific Pod:

1. Node Preparation: Ensure the nodes in your Kubernetes cluster have GPUs installed and are running the appropriate GPU drivers (e.g., NVIDIA drivers). You also need to install NVIDIA’s Kubernetes device plugin on these nodes to manage the GPUs.
2. Requesting GPUs in a Pod Spec:

• In your Pod’s YAML specification, you can request GPUs by adding a resource request for nvidia.com/gpu (or other vendor-specific resource names).
• The number you specify will be the number of GPUs dedicated to the Pod. Here’s an example of a Pod spec requesting 1 GPU:

   apiVersion: v1
   kind: Pod
   metadata:
     name: gpu-pod
   spec:
     containers:
     - name: gpu-container
       image: nvidia/cuda:11.0-base
       resources:
         limits:
           nvidia.com/gpu: 1  # Request 1 GPU

3. Pod Scheduling: When the Pod is scheduled, Kubernetes will ensure that the requested GPU(s) are allocated exclusively to that Pod. No other Pod will be able to use the GPU(s) assigned to that Pod.
4. Multi-GPU Nodes: If a node has multiple GPUs, you can specify how many GPUs you want the Pod to use, and Kubernetes will allocate the specified number of GPUs from that node to the Pod.

This setup ensures that each Pod can have dedicated access to one or more GPUs, isolating GPU resources per Pod as needed.

Conclusion

Yes, in the case of Kubernetes, every single GPU can be fully dedicated to a particular integrated pod. Kubernetes completely benefits in GPU scheduling, letting you to simply allot the best GPU dedicated server to pods just as anyone would easily do with system memory or CPU.