1. Scalable Machine Learning Workflows with Dask on Kubernetes


    To create scalable Machine Learning workflows with Dask on Kubernetes using Pulumi, you would typically want to follow these high-level steps:

    1. Create a Kubernetes Cluster: You need a Kubernetes cluster to run Dask workers and the scheduler.
    2. Install Dask: Deploy Dask on the Kubernetes cluster, typically by using Helm charts or directly deploying Docker images on Kubernetes.
    3. Configure Autoscaling: For the cluster to be scalable, configure the Kubernetes autoscaler or a Dask-specific autoscaler to manage the number of worker nodes.
    4. Deploy your ML Workflow: Once Dask is ready, you can deploy your machine learning workflows as you would on any Dask cluster.

    Let's create a simple Pulumi program to set up such an environment. This example assumes you are familiar with Kubernetes and have the necessary cloud credentials configured for Pulumi to create resources on your behalf. We'll proceed with the following steps:

    • Provision a Kubernetes cluster on a cloud provider using Pulumi's SDKs.
    • Set up Dask using Pulumi Kubernetes resources or Helm charts.
    • Define autoscaling parameters if supported by the chosen cloud provider.

    For this instance, let's imagine we're setting this up on AWS using Amazon EKS for Kubernetes. First, you need to import the required Pulumi SDK modules:

    import pulumi import pulumi_aws as aws import pulumi_kubernetes as k8s from pulumi_aws import eks

    Next, let's define our AWS EKS cluster:

    # Define the EKS cluster. cluster = eks.Cluster("eks-cluster", instance_type="t2.medium", desired_capacity=2, min_size=1, max_size=4, ) # Export the cluster's kubeconfig. pulumi.export('kubeconfig', cluster.kubeconfig)

    With the cluster defined, we need to deploy Dask. For simplicity, we may use existing Helm charts (though the Helm provider isn't explicitly listed in the search results, it can be easily installed with Pulumi):

    # Set up the Helm provider to deploy Dask. helm_provider = k8s.helm.v3.Provider("helm", cluster.kubeconfig) # Install Dask using the official Helm chart. dask_chart = k8s.helm.v3.Chart("dask", k8s.helm.v3.ChartOpts( chart="dask", version="4.5.4", # specify the version of Dask chart you want to deploy fetch_opts=k8s.helm.v3.FetchOpts( repo="https://helm.dask.org/" ), ), opts=pulumi.ResourceOptions(provider=helm_provider) )

    In the example above, we've defined an EKS cluster with a size that can automatically scale between 1 and 4 EC2 instances (t2.medium). We then exported the kubeconfig for connecting to the cluster via kubectl. Furthermore, we deployed Dask using its Helm chart.

    Autoscaling will be managed by EKS itself, so there's no need for additional configuration within Pulumi unless you are deploying a Dask-specific scaler that requires further setup. Note that although the latest available version of the Dask Helm chart has been specified in the example, you should use the most appropriate version for your workflow and compatibility requirements.

    The pulumi.export statement at the end is used to output the Kubernetes cluster's configuration, which can be used to interface with the cluster using kubectl or other Kubernetes tools.

    This is a high-level overview, and actual implementations may need to handle specifics such as networking, VPC configurations, cloud provider IAM roles, Dask configuration options, and more based on your particular requirements and cloud best practices.