1. Containerized JupyterHub for Collaborative AI Research on Kubernetes


    To create a containerized JupyterHub for collaborative AI research on Kubernetes, we will set up a Kubernetes cluster and deploy JupyterHub using Helm, which is a package manager for Kubernetes. JupyterHub allows multiple users to work on Jupyter notebooks, manage their workspaces and collaborate. Kubernetes will provide the orchestration of the JupyterHub instances, handling tasks like scaling, failover, and deployment.

    In this Pulumi program for Python, we will:

    1. Create a Kubernetes cluster using the gcp.container.Cluster resource, which represents a Google Kubernetes Engine (GKE) cluster.

    2. Set up a Kubernetes provider to interact with the created cluster.

    3. Deploy JupyterHub onto the cluster using the pulumi_kubernetes.helm.v3.Chart resource, which allows us to deploy Helm charts in our Kubernetes cluster.

    Here's a complete Pulumi program that performs these steps:

    import pulumi import pulumi_gcp as gcp import pulumi_kubernetes as k8s from pulumi_kubernetes.helm.v3 import Chart, ChartOpts # Step 1: Create a GKE cluster cluster = gcp.container.Cluster("jupyterhub-cluster", initial_node_count=1, min_master_version="latest", node_version="latest", node_config={ "machine_type": "n1-standard-1", "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 2: Setup the Kubernetes provider k8s_provider = k8s.Provider("k8s-provider", kubeconfig=cluster.endpoint.apply(lambda endpoint: cluster.master_auth.apply(lambda auth: f""" apiVersion: v1 clusters: - cluster: certificate-authority-data: {auth.0.cluster_ca_certificate} server: https://{endpoint} name: gke-cluster contexts: - context: cluster: gke-cluster user: gke-cluster-user name: gke-cluster-context current-context: gke-cluster-context kind: Config preferences: {{}} users: - name: gke-cluster-user user: auth-provider: config: cmd-args: config config-helper --format=json cmd-path: gcloud expiry-key: '{{.credential.token_expiry}}' token-key: '{{.credential.access_token}}' name: gcp """))) # Step 3: Deploy JupyterHub using a Helm chart jupyterhub_chart = Chart("jupyterhub", ChartOpts( chart="jupyterhub", version="0.9.0", fetch_opts=k8s.helm.v3.FetchOpts( repo="https://jupyterhub.github.io/helm-chart/", ), ), opts=pulumi.ResourceOptions(provider=k8s_provider)) pulumi.export('cluster_name', cluster.name) pulumi.export('jupyterhub_chart', jupyterhub_chart.status)


    1. GKE Cluster: We begin by creating a GKE cluster named "jupyterhub-cluster". We initialize it with a single node, specify the machine type of the node, and provide the necessary OAuth scopes required for GKE.

    2. Kubernetes Provider: We then set up a Kubernetes provider that will allow Pulumi to deploy resources to our cluster. The provider needs Kubernetes configuration data to communicate with the cluster, which we construct using the cluster endpoint and master authentication data.

    3. JupyterHub Helm Chart: We deploy JupyterHub on the Kubernetes cluster using a predefined Helm chart. Helm charts define all the Kubernetes resources needed for a deployable application. In this case, we're using the jupyterhub chart from the JupyterHub Helm chart repository. We use the specific version 0.9.0 of the JupyterHub Helm chart, but you might want to use the most recent version that meets your needs.

    4. Exports: Finally, we export the cluster name and the status of the JupyterHub Helm chart deployment so we can easily retrieve them after the deployment.

    To run this Pulumi program, you will need to have Pulumi installed, be authenticated with GCP, and set up the gcloud CLI. After you run pulumi up, this program will provision a GKE cluster and deploy JupyterHub, ready for your collaborative AI research work.