1. Distributed Machine Learning Workflows on Kubernetes Helm


    To set up distributed machine learning workflows on Kubernetes using Helm, you can manage the deployment using Pulumi. For this purpose, you can use the pulumi_kubernetes provider, which allows you to deploy resources on a Kubernetes cluster in a declarative way. Helm charts are a great way to package such distributed systems because they encapsulate all of the resource definitions necessary to run an application, service, or a piece of software on a Kubernetes cluster.

    First, you need to have a Kubernetes cluster up and running. If you don't already have one, you can create one using cloud providers like AWS, Azure, or Google Cloud, among others. Once you have your cluster ready, ensure you have kubectl configured, with access to your cluster.

    With Pulumi, you can deploy Helm charts directly. The Chart class from the pulumi_kubernetes.helm.v3 module is used to deploy a Helm chart into a Kubernetes cluster.

    Below is a program that demonstrates how to deploy a distributed machine learning workflow on Kubernetes using Helm with Pulumi:

    import pulumi import pulumi_kubernetes as kubernetes # First, create a Kubernetes provider instance to interact with the cluster. # Note that this assumes you have a kubeconfig file correctly set up and Pulumi is authorized to interact with your cluster. k8s_provider = kubernetes.Provider('k8s-provider') # Then, specify the Helm chart for your machine learning application. # You need to replace `chart_name` and `chart_version` with actual values for your use-case. # Also, if your chart is not in the default Helm chart repository, specify `repo` accordingly. machine_learning_chart = kubernetes.helm.v3.Chart( 'machine-learning-chart', kubernetes.helm.v3.ChartOpts( chart='chart_name', version='chart_version', fetch_opts=kubernetes.helm.v3.FetchOpts( repo='http://your-helm-chart-repository/', ), # If your Helm chart requires custom values, you define them here. values={ 'worker': { 'replicaCount': 3, }, 'parameterServer': { 'replicaCount': 2, }, # Add more custom values as required by your Helm chart. }, ), opts=pulumi.ResourceOptions(provider=k8s_provider) ) # Finally, if you want to expose an endpoint (for example, a Jupyter notebook or a dashboard), # you can capture the output of the service deployment and obtain its endpoint. # Export the URL of the application, adjust the resource type and names as needed. application_url = machine_learning_chart.get_resource('v1/Service', 'my-ml-application') pulumi.export('applicationUrl', pulumi.Output.from_input(application_url.status).apply(lambda status: status['loadBalancer']['ingress'][0]['ip'])) # Now, running this Pulumi program will deploy your distributed machine learning workflow using the specified Helm chart onto your Kubernetes cluster.

    In this program:

    • We created a pulumi_kubernetes.Provider, assuming that you have a kubeconfig set up and Pulumi can access your cluster.
    • We then instantiated a Helm Chart with the ChartOpts specifying the chart and version of the machine learning Helm chart you want to deploy.
    • You may need to specify additional information like the repository URL (repo) if your chart is not within the default Helm repositories.
    • Custom values are set in the values dictionary. These values will configure your machine learning workflow, such as the number of worker replicas, parameter servers, and any other configurable parameter your chart supports.
    • In the final step, we attempt to export the URL of the deployed application. Note that you must adjust the resource type and name according to your chart's output. Typically, you'd look for a Service of type LoadBalancer to expose your application externally.

    To run this Pulumi program:

    • Save the code to a file named __main__.py.
    • Install the required Pulumi Kubernetes package by running pip install pulumi_kubernetes.
    • Run pulumi up to preview and deploy the changes. If you're happy with the plan, select yes to proceed with the deployment.