Planetary Computer Hub

This repository contains the configuration and continuous deployment for the Planetary Computer's Hub, a Dask-Gateway enabled JupyterHub deployment focused on supporting scalable geospatial analysis.

For general questions or discussions about the Planetary Computer, use the microsoft/PlanetaryComputer repository.

Overview

See the user documentation for an overview of what all is provided.

This deployment is relatively complex, and contains a few Microsoft Planetary Computer-specific aspects. For developers or system administrators looking to deploy their own hub, consult the deployment guide. This can serve as a concrete example.

There are two main components to the planetary-computer-hub repository:

1. helm: A wrapper around the daskhub helm chart.
2. terraform: Terraform code to deploy all the necessary Azure resources and the Hub itself.

Helm

The most interesting pieces are the YAML configuration files. These are used by the Terraform helm-release provider to customize the JupyterHub and Dask Gateway charts (see hub.tf). In addition to these values_files, the hub.tf terraform module passes some terraform variables through to the chart using set blocks.

The bulk of the configuration is done in values.yaml. See the inline comments there for documentation on why those values are set.

profiles.yaml configures daskhub.jupyterhub.singleuser.ProfileList. The helm-release provider does not lend itself to setting List values, and we need to get the various image tags from the terraform configuration. We place this in its own file to keep things a bit more manageable.

jupyterhub_opencensus_monitor.yaml sets daskhub.jupyterhub.hub.extraFiles.jupyterhub_open_census_monitor.stringData to be the jupyterhub_opencensus_monitor.py script (see below). We couldn't figure out out to get the helm-release provider working with with kubectl's set-file so we needed to inline the script. There's probably a better way to do this.

Finally, the custom UI elements used by the Hub process and additional notebook server configuration are included under helm/chart/files and helm/cart/templates. These are mounted into the pods. See custom UI for more.

Terraform

The terraform directory contains all the deployment code for the Hub. It manages the Azure resources and Helm release.

The terraform code is split into deployment-specific directories (prod, staging) and a resources directory that contains the shared configuration between the two deployments. To the extent possible, resources should be defined in resources. staging and prod should only contain configuration (e.g. the URL for the hub, or the size of the core VM).

Additionally, there's a shared directory, which contains the definition for resources that are shared between the two. Currently, this includes a Storage Account and file share for mounting data volumes onto notebook pods. Resources in the shared directory are deployed manually.

acr.tf

This module creates the Azure Container Registry used for Hub images. Its deployment is a bit strange, an artifact of the deployment history and a desire to use the same container registry for both the staging and prod deployments.

These images are available publicly through the Microsoft Container Registry. See https://github.com/microsoft/planetary-computer-containers for more.

aks.tf

This module deploys the Kubernetes cluster using Azure Kubernetes Service.

Most of the configuration is around node pools. We use the default node pool for "core" JupyterHub pods (e.g. the hub pod). We add a user_pool for users, and a cpu_worker_pool for Dask workers (using preemptible nodes).

In addition to the node pools configured here, we attach two GPU node pools. See scripts/gpu. We're following this upstream issue to deploy GPU node pools through terraform.

hub.tf

This uses the helm_release provider to deploy the Hub using our Helm chart. See helm above for more.

keyvault.tf

We manually place some secrets in an Azure Key Vault. These are accessed in keyvault.tf and used in the deployment. The Azure Service Principal used by Terraform must have permissions to read these keys.

logs.tf

This deploys a Log Analytics workspace, Log Analytics solution, and application insights.

outputs.tf

A terraform values are used later in the process (e.g. the Kubernetes configuration to start tests). These are exported in outputs.tf.

providers.tf

This sets the versions of the Terraform providers we use.

rg.tf

Creates a Resource Group to contain all the created Azure resources.

variables.tf

Defines the variables that can be controlled by the staging / prod deployments. See the variable descriptions for documentation on what each variable is used for.

vnet.tf

Creates the Azure Virtual Network used by the Kubernetes Cluster.

data-volumes.tf

Creates an Azure Storage Account, File share, and Kubernetes Secret for mounting the file share. This is used to mount read-only, static files into all the user pods (e.g. a dataset for a machine learning competition).

Manual Resources

We rely on a few "manual" resources that are created outside of this repository. These include

• A storage account and container for Terraform state
• A keyvault for secrets

The service principal used by Terraform should have access to the manual resources resource group.

Keyvault secrets reference

This table documents the values we set in keyvault. They can be created with

$ az keyvault secret set --vault-name pc-deploy-secrets --name '<prefix>--<key-name>' --value '<key-value>'

Continuous deployment

This repository deploys on commits to the staging environment on commits main. We commit to production on tags. The deployment is done through GitHub Actions.

We created a service principal to mange deployment.

To enable creating network security groups

$ az role assignment create \
    --role "/subscriptions/<subscription-id>/providers/Microsoft.Authorization/roleDefinitions/4d97b98b-1d4f-4787-a291-c67834d212e7" \
    --assignee "<service-principal-id>" \
    --scope="/subscriptions/<subscription-id>/resourceGroups/MC_pcc-staging-rg_pcc-staging-cluster_westeurope/providers/Microsoft.Network/routeTables/aks-agentpool-27180469-routetable"

Likewise for production (change the resource group name in the scope).

AKS RBAC

Requires the service principal executing terraform to also have permissions on the Kubernetes Cluster.

$ az role assignment create \
    --role "Azure Kubernetes Service RBAC Writer" \
    --scope "/subscriptions/$ARM_SUBSCRIPTION_ID/resourceGroups/pcc-staging-2-rg/providers/Microsoft.ContainerService/managedClusters/pcc-staging-2-cluster" \
    --assignee $ARM_CLIENT_ID

Velero backup configuration

The Terraform deployment also installs velero on the cluster via helm. See velero.tf.

This requires the manual creation of some resources.

Opencensus monitor service

jupyterhub_opencensus_monitory.py module is deployed as a JuptyerHub service. It collects metrics on usage from the JupyterHub REST API. It would ideally be refactored into a standalone repository: jupyterhub/jupyterhub#3116.

API Management integration

The Planetary Computer API is deployed using API Management. The hub includes an integration to automatically insert the logged in user's subscription key as an environment variable. This is used by libraries like planetary-computer to automatically sign requests. See daskhub.jupyterhub.hub.extraConfig.pre_spawn_hook in values.yaml for where this is done.

Testing

We used the JupyterHub admin panel to create a user for tests, [email protected]. The tests/ starts a notebook server for this user and verifies that a few common operations work.

ACR Integration

A previous iteration used a common Azure Container Registry for both staging and prod. After splitting, we need to manually grant the staging cluster access to the ACR.

$ az aks update -n pcc-staging-cluster -g pcc-staging-rg --attach-acr pcccr

Custom UI

We're able to customize the JupyterHub and jupyterlab UIs following the approach outlined in https://discourse.jupyter.org/t/customizing-jupyterhub-on-kubernetes/1769/4.

To test changes to the templates locally, install jupyterhub and run it from the root of the project directory, which includes a jupyterhub_config.py file. Changes to the template files in helm/chart/files/etc/jupyterhub/templates/ can be previewed at localhost:8000.

Additional References

Many of the concepts used here were learned in deployments at the pangeo-cloud-federation and 2i2c pilot hubs. Those might serve as additional references for how to deploy a Hub.

Contributing

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