1. Kubernetes Cluster Orchestration for Ray Workloads


    To orchestrate Ray workloads on a Kubernetes cluster, you'll need a Kubernetes cluster running and configured with the necessary resources to support Ray. Ray is an open-source framework that provides a simple, universal API for building distributed applications. It is particularly well-suited for high-performance machine learning applications.

    Pulumi can be used to define, deploy, and manage a Kubernetes cluster with all the necessary configurations for Ray. For this example, we'll use the pulumi_kubernetes provider to create a new Kubernetes cluster on Google Kubernetes Engine (GKE). This cluster will then be configured to run Ray workloads.

    Here is a Pulumi program that demonstrates how to create a GKE cluster, configure the node pool to meet Ray's requirements, and then deploy Ray using a pre-existing Helm chart.

    Before diving into the code, let's break down what each piece does:

    1. Import necessary Pulumi libraries: You'll need pulumi and pulumi_gcp to work with Google Cloud.
    2. Create a GKE Cluster: We'll create a GKE cluster with an adequate number of nodes and appropriate machine types to handle the compute-intensive tasks.
    3. Deploy Ray using Helm Chart: Helm charts simplify the deployment of applications on Kubernetes. We'll deploy Ray using its Helm chart, which sets up Ray and its dependencies on the cluster.

    Pulumi Program for Ray on GKE

    import pulumi import pulumi_gcp as gcp import pulumi_kubernetes as kubernetes # Step 1: Provision a GKE Cluster. # This creates a GKE cluster with the default node pool removed, afterwards a separate node pool optimized for Ray will be added. gke_cluster = gcp.container.Cluster("ray-cluster", initial_node_count=1, remove_default_node_pool=True, min_master_version="latest", ) # Step 2: Create a separate node pool for Ray with the necessary configurations. ray_node_pool = gcp.container.NodePool("ray-node-pool", cluster=gke_cluster.name, initial_node_count=3, # Start with 3 nodes. location=gke_cluster.location, node_config={ "machineType": "n1-standard-4", # A standard machine type that should fit the needs of most Ray workloads. "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" ], }, ) # Step 3: Deploy Ray on the cluster using its Helm chart. helm_chart = kubernetes.helm.v3.Chart("ray", kubernetes.helm.v3.ChartOpts( chart="ray", version="1.0", # This is a placeholder version, replace it with the version you want to deploy. fetch_opts=kubernetes.helm.v3.FetchOpts( repo="https://ray-project.github.io/ray-helm-charts/", ), ), opts=pulumi.ResourceOptions(depends_on=[ray_node_pool]), # Ensure the node pool is set up before deploying Ray. ) # Export the cluster name and kubeconfig kubeconfig = gke_cluster.name.apply(lambda name: gcp.container.get_kubeconfig(cluster_name=name)) pulumi.export("cluster_name", gke_cluster.name) pulumi.export("kubeconfig", kubeconfig)

    Explanation of the Pulumi Program

    1. At the beginning of the program, we import the Pulumi GCP and Kubernetes libraries which allow us to interact with GCP resources and Kubernetes resources, respectively.

    2. We create a GKE cluster using gcp.container.Cluster. We configure it to remove the default node pool as we will create a custom one tailored for Ray workloads.

    3. We add a custom node pool for Ray by using gcp.container.NodePool, specifying the machine type and OAuth scopes. The OAuth scopes give the node pool permissions to use GCP's compute, storage, logging, and monitoring services.

    4. We deploy the Ray framework on the cluster using a Helm chart. Helm charts simplify the management of Kubernetes applications, defining all the needed resources in a compact form.

    5. The kubeconfig is an output that allows you to connect to your cluster using kubectl or any Kubernetes management tool. It is obtained by calling gcp.container.get_kubeconfig, which generates the configuration needed to connect to the GKE cluster.

    6. Finally, we export the cluster name and kubeconfig. The exported kubeconfig can be used outside of Pulumi to interact with the Kubernetes cluster.

    Next Steps

    With this Pulumi program, you've created a Kubernetes cluster on GKE and deployed Ray, ready to run distributed machine learning workloads. To run your specific workloads on Ray, you would typically package your machine learning application into Docker containers, push them to a registry, and create Kubernetes jobs or deployments to run them.

    Remember to replace the placeholder version of the Ray Helm chart ("1.0") with the actual version you wish to deploy. You can find the latest chart versions and their configurations on the Ray Helm Chart repository.

    The pulumi up command would apply this configuration and create all the resources. If successful, you'll receive the outputs defined by pulumi.export and can then use the generated kubeconfig to manage your cluster.