Deploying JupyterHub for AI Research on EKS Clusters

Question

Pulumi · Accepted Answer

Deploying JupyterHub on an Amazon Elastic Kubernetes Service (EKS) cluster for AI research involves a few major steps:

1. **Setting up an EKS cluster**: This is the backbone of your JupyterHub deployment as it provides a scalable and managed Kubernetes environment.
2. **Configuring JupyterHub**: Configuration typically involves setting up a Helm chart that describes all the necessary components of JupyterHub.
3. **Deploying JupyterHub**: This involves actually running the Helm chart on your EKS cluster, leading to the creation of all necessary Kubernetes resources.
4. **Accessing JupyterHub**: Once deployed, you'll set up access to JupyterHub either through a LoadBalancer or an Ingress controller.

The following program sets up an EKS cluster and deploys JupyterHub to be used for AI research. In this context, we use the open-source Zero to JupyterHub project, which provides a Helm chart for easy JupyterHub deployment on Kubernetes clusters.

In our program, we install a Python package called `pulumi_eks` which will be used for creating an EKS cluster. To manage JupyterHub helm chart, we'll need the `pulumi_kubernetes` provider. The actual JupyterHub deployment would normally involve specifying specific Helm chart values suitable for AI research requirements, but for simplicity, we will use default values here.

The process of deploying JupyterHub to an EKS cluster can be quite intricate and requires a good understanding of Kubernetes and Helm. However, Pulumi aims to abstract and simplify this process with familiar programming constructs.

Here is the complete program that accomplishes the deployment:

```python
import pulumi
import pulumi_eks as eks
import pulumi_kubernetes as k8s

# Create an EKS cluster with default settings.
# By default, this will create all the required infrastructure such as VPC, subnets, and worker nodes.
cluster = eks.Cluster('ai-research-cluster')

# We load the Kubernetes provider so that we can later interact with the EKS cluster using the Kubernetes API.
# The 'kubeconfig' for connecting to the cluster is an output property from the cluster resource.
k8s_provider = k8s.Provider('k8s-provider', kubeconfig=cluster.kubeconfig)

# Here we define our JupyterHub Helm release using the Helm chart from the Zero to JupyterHub project.
jupyterhub_release = k8s.helm.v3.Release(
    'jupyterhub',
    args=k8s.helm.v3.ReleaseArgs(
        chart='jupyterhub',
        version='0.11.1',  # Use the version that suits your needs
        fetch_opts=k8s.helm.v3.FetchOptsArgs(
            repo='https://jupyterhub.github.io/helm-chart/'
        ),
        namespace='jupyterhub',
        # This value file contains the specifics for AI research environments.
        # More customization is required for production scenarios.
        # This file would outline resources(requests/limits), storage, and authentication integrations.
        value_yaml_files=['jupyterhub-values.yaml'],
    ),
    opts=pulumi.ResourceOptions(provider=k8s_provider)
)

# Finally, we export the cluster's kubeconfig and the endpoint of the JupyterHub so it can be accessed.
pulumi.export('kubeconfig', cluster.kubeconfig)
pulumi.export('jupyterhub_endpoint', jupyterhub_release.status.apply(lambda status: status.endpoint))
```

Explanation:

- The EKS cluster is created using Pulumi's EKS module, which sets up all the necessary infrastructure.
- We create a Kubernetes provider to interact with the EKS cluster through the Kubernetes API, using the kubeconfig generated by the EKS resource.
- We're using the JupyterHub Helm chart from the Zero to JupyterHub project. The specific version of the Helm chart should be chosen based on compatibility and features needed.
- We install the JupyterHub Helm chart, specifying a namespace where it should be deployed. This operation will create all the Kubernetes resources needed for JupyterHub to work.

The `jupyterhub-values.yaml` is a YAML file that contains all the JupyterHub settings tailored for your AI research environment. This includes configurations such as user storage, resource limits for users' pods, authentication mechanisms, and any custom configurations for JupyterHub itself or the single user Jupyter notebook servers.

Once deployed, JupyterHub's endpoint can be accessed through the value exported as `jupyterhub_endpoint`.

For any customization, you would edit the `jupyterhub-values.yaml` file to specify the desired settings before deploying with Pulumi.

Please note that this program assumes you have set up the Pulumi CLI and AWS credentials. The `pulumi up` command would execute this program to create the infrastructure and deploy JupyterHub.