1. Auto-scaling TensorFlow Serving Clusters on GCP.


    Auto-scaling TensorFlow Serving clusters on Google Cloud Platform involves creating an environment where your TensorFlow Serving application can automatically adjust its resources based on traffic and usage patterns. Google Kubernetes Engine (GKE) is one such environment that integrates well with auto-scaling features.

    Let's break down the necessary steps and resources:

    1. Create a GKE cluster: This will be the compute environment where your TensorFlow Serving application runs.
    2. Node Pool Autoscaling: You can set up a GKE cluster with node pools that scale automatically. This means that as demand increases or decreases, GKE will automatically adjust the number of nodes in the node pool.
    3. Horizontal Pod Autoscaling (HPA): You can configure your pods to scale horizontally (in or out) based on observed CPU utilization or other select metrics.

    Below is a program written in Python that uses Pulumi to create an auto-scaling GKE cluster suitable for serving a TensorFlow application. The steps outlined within the code create a cluster, set up node pool autoscaling, and configure HPA.

    import pulumi import pulumi_gcp as gcp # Specify the project and location for resources. project = "my-gcp-project" # Replace with your GCP project ID. location = "us-central1" # Replace with your preferred GCP region. # Create a GKE cluster. cluster = gcp.container.Cluster( "tf-serving-cluster", initial_node_count=1, node_config=gcp.container.ClusterNodeConfigArgs( preemptible=False, machine_type="n1-standard-1", # Choose the machine type based on your requirements. ), autoscaling=gcp.container.ClusterAutoscalingArgs( enable_node_autoprovisioning=True, resource_limits=[gcp.container.ClusterAutoscalingResourceLimitArgs( resource_type="cpu", minimum=1, maximum=10, )], ), location=location, project=project, ) # Auto-scaling settings for the node pool. node_pool = gcp.container.NodePool( "tf-serving-node-pool", initial_node_count=1, autoscaling=gcp.container.NodePoolAutoscalingArgs( min_node_count=1, max_node_count=5, ), location=location, node_config=gcp.container.NodePoolNodeConfigArgs( preemptible=True, machine_type="n1-standard-1", # Consider the workload to determine the machine type. 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", ], ), management=gcp.container.NodePoolManagementArgs( auto_repair=True, auto_upgrade=True, ), cluster=cluster.name, project=project, ) # Export the cluster name and Kubeconfig for later use with kubectl or other GKE operations. pulumi.export("cluster_name", cluster.name) pulumi.export("kubeconfig", pulumi.Output.all( cluster.name, cluster.endpoint, cluster.master_auth.cluster_ca_certificate, ).apply(lambda args: ( """apiVersion: v1 clusters: - cluster: certificate-authority-data: {} server: https://{} name: {} contexts: - context: cluster: {} user: {} name: {} current-context: {} kind: Config preferences: {} users: - name: {} 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 """.format( args[2], # Embeds the cluster's CA certificate. args[1], # Embeds the cluster endpoint. args[0], # Use the cluster name for the context name. args[0], args[0], args[0], args[0], args[0], ) ))) # Note: # Horizontal Pod Autoscaling (HPA) is not set in this script and is typically applied to the Deployment or similar workloads. # You would need to create a Deployment that runs TensorFlow Serving and define the HPA settings # for that Deployment to enable automatic scaling of the TensorFlow Serving pods based on CPU utilization or custom metrics.

    This program starts by defining the project and location for the resources. Then, it creates a GKE cluster and defines its initial settings, such as the number of nodes and machine types. It sets up the autoscaling policies for both the cluster and a node pool, specifying the range for the minimum and maximum number of nodes.

    Lastly, the program outputs the cluster name and configuration details that can be used to connect to the cluster using kubectl or other GCP tools.

    After this Pulumi program is applied and the cluster is up, you would deploy your TensorFlow Serving application on the cluster and define the HPA settings for the deployment to enable the autoscaling of pods.

    Keep in mind that to apply HPA, you would need to deploy your actual workload (for example, TensorFlow Serving containers) to the cluster, then apply an HPA configuration to that workload. The HPA configuration is not shown here as it typically involves Kubernetes manifests which would be applied post-cluster creation and after your application has been deployed.

    Remember to replace my-gcp-project with your own GCP project ID and us-central1 with your desired GCP region. Adjust the machine types and autoscaling limits accordingly based on your specific application needs and anticipated workload.