Kubernetes-Based NVIDIA GPU Sharing for AI Workload Cost Efficiency

Question

Pulumi · Accepted Answer

To achieve NVIDIA GPU sharing in a Kubernetes cluster for AI workload cost efficiency, you would typically use underlying Kubernetes resources such as `ResourceClass`, `ResourceClaim`, and specific Container configurations that enable GPU sharing across pods. Pulumi provides an abstraction on top of Kubernetes resources to define and deploy them.

Below is an example of how you could use Pulumi with Kubernetes to allocate shareable GPU resources for your AI workloads. This program will create a custom resource class specifically designed for GPU sharing and a claim to use such resources.

The Pulumi `ResourceClass` defines a template for creating instances of a custom resource type. By setting parameters for GPU resources, we can manage how these resources are allocated to different workloads. In this case, we specify the GPU resource as part of the ResourceClass.

The `ResourceClaim` resource is used to request an allocation from the ResourceClass. Here, a claim specifies the allocation of GPU resources according to the specific class we have defined. A successful claim will ensure that the required number of GPU units are allocated for the Kubernetes pod using the claim.

This example assumes that you already have configured your Pulumi environment with a Kubernetes provider and that your cluster has nodes with NVIDIA GPUs.

Let's walk through the Pulumi code:

```python
import pulumi
import pulumi_kubernetes as k8s

# Create a Kubernetes GPU Resource Class
resource_class = k8s.resource.k8s.io.v1alpha2.ResourceClass(
    "gpu-resource-class",
    metadata=k8s.meta.v1.ObjectMetaArgs(
        name="gpu-resources",
    ),
    driver_name="nvidia.com/gpu",
    parameters_ref=k8s.core.v1.LocalObjectReferenceArgs(
        name="gpu-limits",
    ),
)

# Claim GPU resources using the ResourceClass defined above
resource_claim = k8s.resource.k8s.io.v1alpha1.ResourceClaim(
    "gpu-resource-claim",
    metadata=k8s.meta.v1.ObjectMetaArgs(
        name="ai-workload-gpu-claim",
    ),
    spec=k8s.resource.k8s.io.v1alpha1.ResourceClaimSpecArgs(
        resource_class_name=resource_class.metadata.name,
    ),
)

# Export the name of the resource claim
pulumi.export('resource_claim_name', resource_claim.metadata.name)
```

In this program:
- We import the relevant Pulumi libraries for Kubernetes resources.
- We create an instance of `ResourceClass` specific to GPU resources named `gpu-resource-class`. This will serve as a blueprint for the GPU resources we want to allocate.
- We define a `ResourceClaim` named `gpu-resource-claim` to request GPU resources from the `gpu-resource-class` class we created earlier. This claim can then be referenced in pod specifications to allocate GPU resources to AI workloads.
- Finally, we export the name of the `ResourceClaim` to make it easier to reference in subsequent configurations or commands.

Remember to tailor the `ResourceClass` and `ResourceClaim` parameters to the specifics of your Kubernetes cluster and the requirements of your AI workloads. This might include tweaking the `driver_name`, `parameters_ref`, and other properties. Additionally, your cluster must have the appropriate device plugins and drivers installed to manage and allocate GPU resources.

This code is solely an illustration of how you might define these resources with Pulumi. It won't create a fully functional GPU-sharing setup by itself, as a complete setup involves proper cluster configuration, installation of the NVIDIA device plugin for Kubernetes, and appropriate node configurations with GPU resources.

After deploying this Pulumi program to your cluster, your AI workloads can be optimized for cost efficiency by sharing GPU resources, leveraging Kubernetes' ability to orchestrate and manage complex resource allocation scenarios.