1. Persistent Storage for AI Data Pipelines


    Persistent storage is essential for AI data pipelines, as it allows data to be stored across machine learning experiments, pipeline executions, and system restarts. In cloud-native environments, this is often achieved by provisioning persistent volumes that can be mounted and used by containers running in a Kubernetes (k8s) cluster.

    We'll use Pulumi to provision a Kubernetes Persistent Volume (PV) and Persistent Volume Claim (PVC). A PV is a piece of storage in the cluster that has been provisioned by an administrator or dynamically provisioned using Storage Classes. A PVC is a request for storage by a user that can be fulfilled by a PV.

    In this program, we'll create a PV backed by networked storage (such as AWS EBS, Azure Disk, or Google Persistent Disk), and then we'll create a PVC to request a slice of that PV for use by our AI data pipeline. This is an essential pattern for ensuring that your AI pipelines have the storage they need to operate effectively.

    Here is how you can do it with Pulumi using Python:

    import pulumi import pulumi_kubernetes as k8s # Configure the storage class storage_class = k8s.storage.v1.StorageClass( "storage-class", metadata={"name": "fast"}, provisioner="kubernetes.io/aws-ebs", parameters={"type": "gp2"}, # Use AWS General Purpose SSD (gp2) reclaim_policy="Retain", # Retain the volume when the PVC is deleted volume_binding_mode="WaitForFirstConsumer" # Delay binding until a pod needs the PVC ) # Provision a persistent volume persistent_volume = k8s.core.v1.PersistentVolume( "persistent-volume", metadata={"name": "pv-data-pipeline"}, spec=k8s.core.v1.PersistentVolumeSpecArgs( capacity={"storage": "100Gi"}, # Request 100 GB of storage access_modes=["ReadWriteOnce"], # The volume can be mounted as read-write by a single node persistent_volume_reclaim_policy="Retain", # Keep the volume after the PVC is deleted storage_class_name=storage_class.metadata["name"], # Use the storage class defined above aws_elastic_block_store=k8s.core.v1.AWSElasticBlockStoreVolumeSourceArgs( volume_id="<EBS_VOLUME_ID>" # You would replace this with your EBS volume ID ) ) ) # Create a persistent volume claim persistent_volume_claim = k8s.core.v1.PersistentVolumeClaim( "persistent-volume-claim", metadata={"name": "pvc-data-pipeline"}, spec=k8s.core.v1.PersistentVolumeClaimSpecArgs( access_modes=["ReadWriteOnce"], # Match the access modes of the PV resources=k8s.core.v1.ResourceRequirementsArgs( requests={"storage": "100Gi"} # Request the same amount of storage as the PV ), storage_class_name=storage_class.metadata["name"], # Use the same storage class ) ) # Export the PVC name that will be used to mount this volume in your pods pulumi.export("persistent_volume_claim_name", persistent_volume_claim.metadata["name"])

    In the program above:

    • We begin by creating a StorageClass, which is a way for administrators to describe the "classes" of storage they offer. Different classes might map to quality-of-service levels or backup policies.
    • We then create a PersistentVolume, representing a piece of storage that will persist beyond the lifecycle of any individual Pod. This PV will be backed by AWS EBS, but that could be swapped for Azure Disk, Google Persistent Disk, or any other cloud provider's block storage solution.
    • Next, we create a PersistentVolumeClaim that Kubernetes workloads can use to claim a piece of the PersistentVolume. This decouples the specific details of the storage from the workloads that use it.
    • Finally, we export the name of the Persistent Volume Claim so that it can be used as a reference when deploying our Pods that will conduct the AI data pipeline work.

    Remember to replace <EBS_VOLUME_ID> with the ID of the EBS volume you want to use as the backend for this PV. This ID links the Kubernetes PersistentVolume to the actual storage provided by the cloud provider.