1. Auto-Scaling Clusters for Large Language Models


    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.

    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.