1. High-Performance Batch Scoring Using Dask on Kubernetes


    To achieve high-performance batch scoring using Dask on Kubernetes, you'll need to set up a Dask cluster within your Kubernetes environment. Dask is a flexible library for parallel computing in Python, which is very effective for batch scoring tasks. Kubernetes is an excellent platform for running such workloads due to its ability to manage containerized applications and services.

    First, you'd typically utilize the Dask Kubernetes library to dynamically launch a Dask scheduler and workers in your Kubernetes cluster. However, in your Pulumi program, you'll manually define the necessary components.

    Here’s how you can do it using Pulumi and the Kubernetes Python SDK:

    1. Kubernetes Job Resource: Use the kubernetes.batch.v1.Job class to create the necessary jobs for the Dask scheduler and worker. This class helps you run batch processing workloads on your cluster.

    2. Dask Docker Images: You'll have to use Docker images that contain Dask components for the scheduler and worker. There are official Dask images available on DockerHub that you can use.

    3. Scheduler Service: To access the Dask scheduler from within the cluster, you’ll create a Kubernetes Service using kubernetes.core.v1.Service.

    Below is a Pulumi program that creates a basic Dask cluster setup with one scheduler and multiple worker jobs. Remember that this example assumes you have a running Kubernetes cluster and Pulumi is already configured to interact with it.

    import pulumi import pulumi_kubernetes as kubernetes # Define the job for the Dask Scheduler. dask_scheduler_job = kubernetes.batch.v1.Job( "dask-scheduler-job", spec=kubernetes.batch.v1.JobSpecArgs( template=kubernetes.core.v1.PodTemplateSpecArgs( spec=kubernetes.core.v1.PodSpecArgs( containers=[kubernetes.core.v1.ContainerArgs( name="dask-scheduler", image="daskdev/dask:latest", # Using the official Dask image. args=["dask-scheduler", "--host", ""], )], restart_policy="Never", ) ) ) ) # Create a service for the Dask Scheduler for communication. dask_scheduler_service = kubernetes.core.v1.Service( "dask-scheduler-service", metadata=kubernetes.meta.v1.ObjectMetaArgs( labels={"app": "dask-scheduler"} ), spec=kubernetes.core.v1.ServiceSpecArgs( ports=[kubernetes.core.v1.ServicePortArgs( port=8786, # Dask scheduler default port. )], selector={"app": "dask-scheduler"}, # Selects the pod running the dask scheduler. type="ClusterIP", ) ) # Define the job for the Dask Workers. dask_worker_job = kubernetes.batch.v1.Job( "dask-worker-job", spec=kubernetes.batch.v1.JobSpecArgs( parallelism=4, # Number of worker replicas. template=kubernetes.core.v1.PodTemplateSpecArgs( spec=kubernetes.core.v1.PodSpecArgs( containers=[kubernetes.core.v1.ContainerArgs( name="dask-worker", image="daskdev/dask:latest", # Using the official Dask image. args=["dask-worker", "dask-scheduler-service:8786"], )], restart_policy="Never", ) ) ) ) # Export the Dask scheduler service cluster IP for access within the cluster. pulumi.export("dask_scheduler_cluster_ip", dask_scheduler_service.spec.apply(lambda spec: spec.cluster_ip))

    In this program:

    • We define a Kubernetes Job for the Dask Scheduler with the necessary container configurations.
    • A Kubernetes Service is created to allow other pods and applications, including workers, to communicate with the scheduler via a consistent IP address.
    • We define another Kubernetes Job for the Dask Workers and point them to communicate with the scheduler through the scheduler service created.

    This setup allows you to perform high-performance computing tasks like batch scoring models with Dask on Kubernetes. After deploying this Pulumi program, you can scale up or down the worker job count depending on your workload.

    Keep in mind that real-world usage might require you to configure additional details such as volume mounts for data, resource constraints for the pods, and proper networking setup for complex communication patterns.