1. Dynamic Volume Provisioning for AI Model Training


    Dynamic volume provisioning in Kubernetes enables storage volumes to be created on-demand. This is particularly useful for scenarios like AI model training where different jobs or pods may require their own storage resources at runtime.

    When using Kubernetes for AI model training, you typically need a way to manage data storage for the input data sets as well as the output models. This can be achieved by using PersistentVolumes (PV) and PersistentVolumeClaims (PVC). The StorageClass resource is key in the dynamic provisioning process, allowing you to define different types of storage (e.g., SSD, HDD) and provision them automatically.

    Here's how to set it up with Pulumi:

    1. Define a StorageClass to specify the type of storage and provisioner. This will be used by Kubernetes to dynamically provision the required storage when a PersistentVolumeClaim is created.

    2. Create PersistentVolumeClaims that AI model training jobs will use to claim the dynamic storage. The PVC references the StorageClass and specifies the required storage size.

    3. Attach the provisioned volumes to the pods that run the training jobs, enabling them to access the data and store the output.

    Below is a Pulumi program in Python that sets up dynamic volume provisioning suitable for an AI model training scenario on Kubernetes.

    import pulumi import pulumi_kubernetes as k8s # Step 1: Define a StorageClass. # This StorageClass uses the standard storage provisioner provided by Kubernetes. storage_class = k8s.storage.v1.StorageClass( "ai-model-training-sc", metadata={ "name": "fast-storage", # Name of the StorageClass }, provisioner="kubernetes.io/gce-pd", # Assuming you're on GCP. Replace with your cloud provider's provisioner, like e.g., "kubernetes.io/aws-ebs". parameters={ "type": "pd-ssd", # This specifies an SSD type on GCP. }, reclaim_policy="Retain", volume_binding_mode="WaitForFirstConsumer", # This delays volume binding until the pod is scheduled. ) # Step 2: Create a PersistentVolumeClaim. # This PVC requests a volume of 50Gi using the StorageClass defined above. persistent_volume_claim = k8s.core.v1.PersistentVolumeClaim( "ai-model-training-pvc", metadata={ "name": "model-training-data", }, spec={ "access_modes": ["ReadWriteOnce"], # The volume can be mounted as read-write by a single node. "storage_class_name": storage_class.metadata["name"], # Reference the StorageClass created earlier. "resources": { "requests": { "storage": "50Gi", # Size of the volume } } } ) # Step 3: Attach the PVC to a Pod for training. # For this example, assume there is a pod that performs the training. training_pod = k8s.core.v1.Pod( "ai-model-training-pod", metadata={ "name": "model-training-pod", }, spec={ "containers": [{ "name": "model-trainer", "image": "your-training-container-image", # Replace with your training container image "volume_mounts": [{ "mount_path": "/data", # This is where the volume will be mounted inside the container "name": "training-data-volume", }], }], "volumes": [{ "name": "training-data-volume", "persistent_volume_claim": { "claim_name": persistent_volume_claim.metadata["name"], # Reference the PVC created earlier. } }] } ) # Export the StorageClass and PVC names so that they can be easily retrieved. pulumi.export('storage_class', storage_class.metadata["name"]) pulumi.export('persistent_volume_claim', persistent_volume_claim.metadata["name"])

    In this program:

    • We define a StorageClass named fast-storage, which specifies SSD as the storage type. The provisioner field should match the one your cloud provider supports. For instance, on AWS, you would use kubernetes.io/aws-ebs and gp2 for the parameters to specify General Purpose SSD volumes.
    • A PersistentVolumeClaim is created, which uses the storage class to request a 50Gi volume. The volume_binding_mode is WaitForFirstConsumer to ensure the volume is created close to the node where the pod will be scheduled.
    • We then define a Pod that references the PVC. The pod has a container that mounts the volume to /data, which is where the training data and output models can be stored.

    When the above Pulumi program runs, it will ensure that any pod that requires storage for AI model training will have a dedicated volume provisioned of the specified type and size.