Auto-Scaling Clusters for Large Language Models

Question

Pulumi · Accepted Answer

Creating auto-scaling clusters for large language models in the cloud generally involves creating a compute cluster and configuring an auto-scaling policy that dynamically adjusts the number of instances or compute resources in the cluster based on demand, workload, or other metrics.

Here's a Pulumi Python program demonstrating how to create an auto-scaling cluster on Google Cloud Platform using their native Kubernetes Engine (GKE) and Compute Engine services. This example sets up a GKE cluster and configures the Kubernetes Horizontal Pod Autoscaler, which automatically scales the number of pods in a deployment based on CPU or memory utilization.

### Program Explanation

1. **GKE Cluster Creation**: We will create a Kubernetes cluster using Pulumi’s GCP provider. This will serve as the environment to deploy our large language models.

2. **Node Pool Configuration**: A Node Pool will be configured for our GKE cluster to define a group of nodes within the cluster that all have the same configuration.

3. **Deployment and Service Configuration**: We will set up a Kubernetes Deployment to manage our pods, and a Service to provide a stable endpoint.

4. **Horizontal Pod Autoscaler (HPA)**: The HPA will automatically scale the number of pods in the deployment depending on the CPU usage.

5. **Exposing External IP (Optional)**: If we want to access our service from outside, we might create an Ingress or a Load Balancer service to expose an external IP.

This is a high-level workflow, and the specific parameters (like CPU thresholds) would be configured based on the actual workloads expected for your large language model.

```python
import pulumi
import pulumi_gcp as gcp

# Create a GKE cluster.
cluster = gcp.container.Cluster('gke-cluster',
    initial_node_count=3,
    min_master_version='latest',
    node_config={
        'machine_type': 'n1-standard-1',
        'oauth_scopes': [
            'https://www.googleapis.com/auth/compute',
            'https://www.googleapis.com/auth/devstorage.read_only',
            'https://www.googleapis.com/auth/logging.write',
            'https://www.googleapis.com/auth/monitoring'
        ],
    },
)

# Create a node pool with autoscaling enabled.
node_pool = gcp.container.NodePool('primary-node-pool',
    cluster=cluster.name,
    initial_node_count=3,
    autoscaling={
        'min_node_count': 1,
        'max_node_count': 5,
    },
    node_config={
        'machine_type': 'n1-standard-1',
        'oauth_scopes': [
            'https://www.googleapis.com/auth/compute',
            'https://www.googleapis.com/auth/devstorage.read_only',
            'https://www.googleapis.com/auth/logging.write',
            'https://www.googleapis.com/auth/monitoring',
        ],
    },
)

# Kubernetes YAML manifest for a deployment managing large language models application.
pod_labels = {'name': 'llm-platform'}
deployment = gcp.kubernetes.yaml.ConfigFile('llm-deployment',
    file='llm_deployment.yaml',
)

# Set up a horizontal pod autoscaler to automatically adjust the number of deployed pods
# based on CPU Utilization
hpa = gcp.kubernetes.yaml.ConfigFile('hpa',
    file='hpa.yaml',
)

pulumi.export('kubeconfig', cluster.endpoint.apply(
    lambda endpoint: cluster.master_auth.apply(
        lambda master_auth: pulumi.Output.secret(f'''apiVersion: v1
clusters:
- cluster:
    certificate-authority-data: {master_auth.cluster_ca_certificate}
    server: https://{endpoint}
  name: cluster
contexts:
- context:
    cluster: cluster
    user: cluster
  name: cluster
current-context: cluster
kind: Config
preferences: {{}}
users:
- name: cluster
  user:
    auth-provider:
      config:
        cmd-args: config config-helper --format=json
        cmd-path: gcloud
        expiry-key: '{{.credential.token_expiry}}'
        token-key: '{{.credential.access_token}}'
      name: gcp
'''))))

# Note: To use the exported kubeconfig for following operations,
# you have to run Pulumi commands (`pulumi up`, `pulumi destroy`, `pulumi stack export`, etc.)
# with Pulumi CLI or use Pulumi Automation API.

```

This Pulumi program uses `pulumi_gcp.container.Cluster` to create a GKE cluster with a specified initial number of nodes. The `pulumi_gcp.container.NodePool` resource defines an auto-scaling node pool, which allows it to scale in and out intelligently based on demand. The `autoscaling` property is critical here, stating the minimum and maximum number of nodes that can scale.

Next, the `gcp.kubernetes.yaml.ConfigFile` is used to deploy the actual Kubernetes objects such as a Deployment and a Horizontal Pod Autoscaler from YAML files (`llm_deployment.yaml` and `hpa.yaml`). These files would include the container image for the large language model, resource requests, and limits, and settings for the HPA such as target CPU utilization.

Finally, we export a kubeconfig that can be used to interact with our cluster using `kubectl`. This output will be encrypted since it contains sensitive materials. Remember to replace `llm_deployment.yaml` and `hpa.yaml` with the actual paths to your YAML configuration files when using this program.

This is an example of building and managing auto-scaling infrastructure that might be used for demanding workloads like large language models. With Pulumi, all these cloud resources are provisioned in a repeatable, declarative, and version-controlled way.