1. High-availability Storage for LLMs with Kubernetes


    To create high-availability storage for large language models (LLMs) with Kubernetes, we'll need to create an infrastructure that can handle large datasets, support high I/O operations, and provide redundancy to ensure data is not lost and is accessible at all times. The following Pulumi program will create a Kubernetes Storage Class with the necessary properties to fulfill these requirements.

    The key components we will use are:

    • StorageClass: This is a Kubernetes resource that defines how a volume should be created. To make the storage highly available, we will specify a storage provisioner that supports replication and failover.

    • PersistentVolumeClaim (PVC): PVCs are used by Kubernetes Pods to request physical storage. This claim will utilize the StorageClass we defined to provision the high-availability storage.

    • StatefulSet: Instead of using a Deployment, which is better for stateless applications, we'll use a StatefulSet for applications that require stable, unique network identifiers, stable persistent storage, and ordered, graceful deployment and scaling.

    • PodDisruptionBudget: To ensure that a certain number or percentage of replicas remain available during maintenance, updates, or node failures, we will create a PodDisruptionBudget.

    • Lease: This is a lightweight resource to manage leader election which can be beneficial for high-availability workloads to decide which Pod should be the leader in a group of Pods running the same application.

    Here's a basic program to get you started on setting these up with Pulumi in Python:

    import pulumi import pulumi_kubernetes as k8s # A high-performance storage class: in a real-world scenario, you'd replace # the 'provisioner', 'parameters', and other fields with your storage solution. storage_class = k8s.storage.v1.StorageClass( "high-availability-storage-class", metadata=k8s.meta.v1.ObjectMetaArgs( name="high-performance" ), provisioner="your-storage-provisioner", # Specify your provisioner here parameters={ "replication-type": "regional-pd", # Example parameter for GCE "type": "pd-standard" }, reclaim_policy="Retain", # "Retain" ensures data persists after a PVC is deleted. volume_binding_mode="WaitForFirstConsumer", # Helps delay binding until a consumer needs the volume. ) # The PersistentVolumeClaim used by your workload pvc = k8s.core.v1.PersistentVolumeClaim( "high-availability-pvc", metadata=k8s.meta.v1.ObjectMetaArgs( name="high-availability-claim" ), spec=k8s.core.v1.PersistentVolumeClaimSpecArgs( access_modes=["ReadWriteOnce"], # Depending on the storage system, this can also be ReadWriteMany or ReadOnlyMany storage_class_name=storage_class.metadata.name, resources=k8s.core.v1.ResourceRequirementsArgs( requests={ "storage": "10Gi" # Define the size of the storage required } ) ) ) # PodDisruptionBudget to ensure high-availability during voluntary disruptions pdb = k8s.policy.v1beta1.PodDisruptionBudget( "high-availability-pdb", metadata=k8s.meta.v1.ObjectMetaArgs( name="high-availability-pdb" ), spec=k8s.policy.v1beta1.PodDisruptionBudgetSpecArgs( min_available=1, # Minimum number of available pods selector=k8s.meta.v1.LabelSelectorArgs( match_labels={"app": "your-llm-application"} # Match labels with your application ), ) ) # Note: The following StatefulSet and Lease resources are just placeholders. # You would need to customize them with the specifics of your application. # StatefulSet to deploy your LLM application stateful_set = k8s.apps.v1.StatefulSet( "high-availability-statefulset", metadata=k8s.meta.v1.ObjectMetaArgs( name="high-availability-app" ), # ... Complete this with StatefulSet specifics such as container image, port, volume mounts, etc. ) # Lease to manage leader election (if your application requires it) lease = k8s.coordination.v1beta1.Lease( "high-availability-lease", metadata=k8s.meta.v1.ObjectMetaArgs( name="high-availability-lease" ), # ... Complete this with Lease specifics if your application needs inter-pod coordination. ) # Export the storage class and persistent volume claim names pulumi.export("storage_class_name", storage_class.metadata.name) pulumi.export("persistent_volume_claim", pvc.metadata.name)

    In the above program:

    • The storage_class defines the type of storage we're using. Depending on the backend storage you're using, the provisioner and parameters fields should be set accordingly to ensure high I/O and failover capabilities. For example, if you're using Google Cloud, replication-type might be set to regional-pd to use regional persistent disks which are replicated across two zones to deliver high availability.

    • The pvc is set to claim 10Gi of storage, which will be provisioned utilizing the storage_class we just defined.

    • The pdb assures that there is at least one replica available during voluntary disruptions.

    • The stateful_set and lease are placeholders that you'll need to flesh out with your specific application details.

    This program sets up the basic infrastructure for high-availability storage, but the details may vary according to the specific requirements of the workload and backend storage in use. To run LLMs effectively, you'll need ample compute resources, which may involve configuring additional Kubernetes resources that are not covered in this basic program.