GPU-Accelerated Machine Learning Pods on Kubernetes

Question

Pulumi · Accepted Answer

To deploy GPU-accelerated machine learning (ML) pods on a Kubernetes cluster, we will need to create the following:

1. A Kubernetes cluster capable of provisioning nodes with GPU resources.
2. A deployment or pod specification that requests GPU resources and runs the machine learning workload.

We will use Pulumi and Python to describe and deploy the necessary infrastructure.

### Kubernetes Cluster with GPU Nodes

First, we need to ensure our Kubernetes cluster has nodes with GPU capabilities. To facilitate this, we can use cloud provider-specific Kubernetes services like Google Kubernetes Engine (GKE) or Amazon Elastic Kubernetes Service (EKS) that support GPU-enabled nodes.

For this demonstration, let's consider using Google Kubernetes Engine (GKE). You will need to create a node pool with the appropriate machine types and GPU accelerators. The `google-native.container/v1.Cluster` resource is used here.

### Pods with GPU Requests

Once the cluster is ready, we define a pod or deployment with containers that request GPUs. This will indicate to Kubernetes that the container needs GPU resources. In the pod specification, we set `resources.limits` for the `nvidia.com/gpu` which indicates the number of GPUs the container is requesting.

We will use the `kubernetes.core/v1.Pod` resource to define a Kubernetes Pod that requests GPU resources.

Here's a full program that sets up the necessary infrastructure:

```python
import pulumi
import pulumi_kubernetes as kubernetes
import pulumi_google_native as google_native

# Configurations for your Kubernetes cluster and GPU settings
PROJECT_ID = 'your-gcp-project-id'
CLUSTER_NAME = 'gpu-enabled-cluster'
COMPUTE_ZONE = 'us-central1-a'
MACHINE_TYPE = 'n1-standard-4'
GPU_TYPE = 'nvidia-tesla-k80'
GPU_COUNT = 1
NODE_COUNT = 2

# Create a GKE cluster with the necessary configurations to support GPUs
cluster = google_native.container.v1.Cluster(
    "gpu-cluster",
    project=PROJECT_ID,
    name=CLUSTER_NAME,
    location=COMPUTE_ZONE,
    initial_node_count=NODE_COUNT,
    node_config=google_native.container.v1.NodeConfigArgs(
        machine_type=MACHINE_TYPE,
        guest_accelerators=[
            google_native.container.v1.AcceleratorConfigArgs(
                accelerator_count=GPU_COUNT,
                accelerator_type=GPU_TYPE
            ),
        ],
    ),
)

# Kubernetes provider to connect to the GKE cluster
k8s_provider = kubernetes.Provider(
    "gke-k8s",
    kubeconfig=cluster.name.apply(lambda name: gke_cluster_kubeconfig(PROJECT_ID, name, COMPUTE_ZONE)),
)

# Function to obtain the kubeconfig for the GKE cluster
def gke_cluster_kubeconfig(project_id, cluster_name, compute_zone):
    # Here you would fetch the kubeconfig from GKE. This is a placeholder for the logic
    # needed to obtain the kubeconfig.
    return "your-gke-cluster-kubeconfig"

# Define a pod that requests GPU resources
gpu_pod = kubernetes.core.v1.Pod(
    "gpu-pod",
    metadata=kubernetes.meta.v1.ObjectMetaArgs(
        name="gpu-pod",
        labels={"app": "gpu-accelerated"},
    ),
    spec=kubernetes.core.v1.PodSpecArgs(
        containers=[
            kubernetes.core.v1.ContainerArgs(
                name="ml-container",
                image="your-ml-container-image",  # Replace with your machine learning container image
                resources=kubernetes.core.v1.ResourceRequirementsArgs(
                    limits={"nvidia.com/gpu": GPU_COUNT},
                ),
                # Specify other container configurations (like env, ports, etc.)
            ),
        ],
    ),
    opts=pulumi.ResourceOptions(provider=k8s_provider),
)

# Export the GKE cluster name
pulumi.export('cluster_name', cluster.name)

# Export the pod name
pulumi.export('gpu_pod_name', gpu_pod.metadata.apply(lambda metadata: metadata.name))
```

This program sets up a GKE cluster configured with GPU nodes and a pod that is capable of running GPU-accelerated ML workloads.

Be sure to replace `your-gcp-project-id`, `your-gke-cluster-kubeconfig`, and `your-ml-container-image` with appropriate values for your project.

To apply this Pulumi program, run the following commands after ensuring Pulumi CLI and GCP SDK are configured:

```sh
pulumi up
```

This will deploy the resources as specified in the program. After you've confirmed the preview looks correct, select 'yes' to continue with the deployment.