1. Managed Kubeflow Pipelines on GKE for ML Workflows


    To create Managed Kubeflow Pipelines on GKE (Google Kubernetes Engine) for Machine Learning (ML) workflows, you need to deploy a Kubernetes cluster on GCP (Google Cloud Platform) and then install Kubeflow on that cluster, configuring it to run your ML workflows.

    Pulumi provides a way to define, deploy, and manage cloud infrastructure using programming languages like Python. In this context, you can use Pulumi's GCP provider to create and manage your GKE cluster.

    First, you will need to create a GKE cluster using Pulumi's gcp.container.Cluster resource. This resource allows you to define a Kubernetes cluster's properties, like its node pool configuration, networking settings, and more.

    Once the cluster is up and running, the next step is to set up Kubeflow. Pulumi does not have a specific resource provider for Kubeflow as it is an application layer on top of Kubernetes. Installing Kubeflow typically involves using kubectl with configuration files (YAMLs), which can be automated with Pulumi using pulumi_kubernetes provider to apply these configurations.

    Let's walk through a Pulumi Python program that:

    1. Creates a GKE cluster ready for deploying Kubeflow.
    2. Applies the necessary Kubeflow resource configurations (you would need the Kubeflow config files for this, which are outside the scope of Pulumi).

    Here's how you can define your GKE cluster for Kubeflow using Pulumi with Python:

    import pulumi import pulumi_gcp as gcp # Step 1: Create a GKE cluster cluster = gcp.container.Cluster("kubeflow-cluster", initial_node_count=3, node_config={ "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", ], "machine_type": "n1-standard-1", }, # Depending on your specific needs, you may configure more properties of the cluster. # See the GCP documentation for all available options. ) # Export the Cluster name and the Kubeconfig file which can be used to interact with the cluster using `kubectl`. kubeconfig = pulumi.Output.all(cluster.name, cluster.endpoint, cluster.master_auth).apply( lambda args: """apiVersion: v1 clusters: - cluster: certificate-authority-data: {0} server: https://{1} name: kubeflow-cluster contexts: - context: cluster: kubeflow-cluster user: kubeflow-cluster name: kubeflow-cluster current-context: kubeflow-cluster kind: Config preferences: {{}} users: - name: kubeflow-cluster user: client-certificate-data: {2} client-key-data: {3} """.format(args[2]['clusterCaCertificate'], args[1], args[2]['clientCertificate'], args[2]['clientKey']) ) pulumi.export('kubeconfig', kubeconfig) # Step 2: Deploy Kubeflow # You would include here the necessary steps to deploy Kubeflow, like loading configuration YAML files # and applying them using the Kubernetes provider in Pulumi. # This might involve using `pulumi_kubernetes` as mentioned above.

    In the code above, you're creating a GKE cluster with a specified node count and machine type suitable for ML tasks. The node_config dictionary includes scopes for various GCP services that your ML workloads might need to access.

    Since Kubeflow installation involves several Kubernetes resources and configurations, direct integration through Pulumi may not be straightforward. Typically, you would clone Kubeflow's repository and apply the manifests using kubectl - this process can be orchestrated using Pulumi's pulumi_kubernetes provider but is not shown here as it is specific to your Kubeflow configuration.

    To interact with your GKE cluster, you would use kubectl with the generated kubeconfig file. The kubeconfig variable in the code snippet prepares the kubeconfig information required to connect to the GKE cluster, which you can export as a file and use with kubectl.

    Remember, after creating the GKE cluster, you would need to follow Kubeflow's documentation for installing it on your cluster, which mainly involves applying Kubernetes YAML files. These steps are Kubeflow-specific and would need to be integrated into the Pulumi program as per your specific machine learning workflow requirements.