1. Stateful Machine Learning Model Training on Kubernetes


    To achieve stateful Machine Learning model training on Kubernetes using Pulumi, you'll need to define Kubernetes resources that can persist the state of your training job. This typically involves creating a training pod, a persistent volume to store the model data, and a persistent volume claim to provide a request for storage.

    We will use the following Kubernetes resources in our Pulumi program:

    • Pod: The smallest deployable unit of computing that can be created and managed in Kubernetes. For machine learning model training, a pod will contain at least one container that runs the training code.
    • PersistentVolume (PV): A piece of storage in the cluster that has been provisioned by an administrator. It is a resource in the cluster just like a node is a cluster resource.
    • PersistentVolumeClaim (PVC): A request for storage by a user. It is similar to a pod. Pods consume node resources and PVCs consume PV resources.

    Here's a Pulumi program that demonstrates how to set up these resources for stateful ML model training on Kubernetes:

    import pulumi from pulumi_kubernetes.core.v1 import Pod, PersistentVolume, PersistentVolumeClaim # Define the PersistentVolume for machine learning model data. ml_model_data_pv = PersistentVolume( "ml-model-data-pv", spec={ "storageClassName": "manual", "capacity": { "storage": "10Gi" }, "accessModes": ["ReadWriteOnce"], "persistentVolumeReclaimPolicy": "Retain", "hostPath": { "path": "/mnt/data" } } ) # Define the PersistentVolumeClaim to request storage for the model data. ml_model_data_pvc = PersistentVolumeClaim( "ml-model-data-pvc", spec={ "storageClassName": "manual", "accessModes": ["ReadWriteOnce"], "resources": { "requests": { "storage": "10Gi" } } } ) # Define the Pod where the machine learning model training will occur. ml_training_pod = Pod( "ml-training-pod", spec={ "containers": [{ "name": "ml-container", "image": "tensorflow/tensorflow:latest", # Example image for illustrative purposes. "ports": [{"containerPort": 80}], "volumeMounts": [{ "mountPath": "/var/ml_model", "name": "ml-model-volume" }], }], "volumes": [{ "name": "ml-model-volume", "persistentVolumeClaim": { "claimName": ml_model_data_pvc.metadata["name"] } }] } ) # Export the name of the pod to access it. pulumi.export("ml_training_pod_name", ml_training_pod.metadata["name"])

    In this program, we start by creating a PersistentVolume which represents a storage area that we've manually allocated on a disk available to the Kubernetes cluster. The hostPath indicates that we are using local storage, which is suitable for single node clusters. Production clusters would use a different type of storage, often provided by a cloud provider or a network storage system.

    Next, we define a PersistentVolumeClaim that Kubernetes can use to allocate space on our PersistentVolume. The request specifies that we need 10 gigabytes of storage and we expect to have read and write access to it.

    Finally, we create the Pod where our machine learning training will occur. The pod definition includes a container which references a Tensorflow Docker image (you would replace this with the appropriate image for your use case). Additionally, we define a volume mount using the claim we created earlier. This is where the state of our ML model can be saved persistently across container restarts.

    Once you apply this Pulumi program, it will create the required Kubernetes resources to facilitate stateful machine learning model training. Note that this example assumes you have a default Kubernetes configuration already set up to interact with your cluster, and Pulumi is configured properly to deploy to that cluster.