1. Deploying AI Model Servers with Kubernetes CRDs


    To deploy AI model servers using Kubernetes Custom Resource Definitions (CRDs), you would define the CRD that represents your AI model server, and then use Pulumi to create instances of this custom resource in your Kubernetes cluster. CRDs allow you to extend Kubernetes with custom resources that have their own API endpoints, just like built-in resources such as Pods or Deployments.

    Below is a step-by-step guide and a Pulumi program written in Python to illustrate how to deploy an AI model server using Kubernetes CRDs:

    1. Define the CRD: First, you need to define a CRD YAML manifest which specifies the schema and characteristics of your custom resource for the AI model server.
    2. Create the CRD: Apply this CRD to your Kubernetes cluster to create a new resource type that your cluster can manage.
    3. Define a Custom Resource: Once the CRD is in place, you can define a custom resource, which represents a specific instance of the AI model server you want to deploy.
    4. Deploy the Custom Resource: Use Pulumi to deploy this custom resource, which will create the AI model servers as specified by the resource's configuration.

    In the Pulumi program below, we will simulate steps 2 to 4, assuming that the CRD is already defined and available in the cluster:

    import pulumi import pulumi_kubernetes as kubernetes # Step 2: Create the CRD (assuming it has already been defined as `ai_model_server_crd.yaml`). # Here we are simulating the application of an existing CRD YAML file using Pulumi. # Read in the CRD manifest from a file. crd_manifest = open('ai_model_server_crd.yaml').read() # Create the CRD using Pulumi crd = kubernetes.yaml.ConfigFile( "ai-model-server-crd", file='ai_model_server_crd.yaml', ) # Step 3: Define a Custom Resource based on the CRD. # Defining the custom resource arguments, typically this would be based on the schema # defined in your CRD and might include things like the model URI, hardware resources, etc. ai_model_server_args = { "apiVersion": "ai.example.com/v1", "kind": "AIModelServer", "metadata": { "name": "example-model-server" }, "spec": { # Example specification for an AI model server. # These fields would be determined by the CRD's schema for the custom resource. "modelUri": "gs://my-bucket/my-model", "resources": { "cpu": "100m", "memory": "200Mi", }, "someOtherSpec": { # Custom specs can be added here based on the CRD definition }, } } # Step 4: Deploy the Custom Resource with Pulumi. # Create an instance of the custom resource using the defined arguments. ai_model_server = kubernetes.apiextensions.CustomResource( "example-model-server", opts=pulumi.ResourceOptions(depends_on=[crd]), # Ensure the CRD is created before this resource api_version="ai.example.com/v1", kind="AIModelServer", metadata={ "name": "example-model-server" }, spec=ai_model_server_args["spec"], ) # Exporting the name of the AI Model Server so we can easily retrieve it later. pulumi.export("ai_model_server_name", ai_model_server.metadata['name'])

    In this program, we create a Kubernetes CRD from a file (which is not shown but assumed to be created and named ai_model_server_crd.yaml). After the CRD is defined in the cluster, we create a custom resource using the pulumi_kubernetes.apiextensions.CustomResource class. The custom resource is an instance of the AI model server, defined in line with the schema we have for our CRD.

    In an actual project, you would customize the spec of the ai_model_server_args to align with the specific configuration needs of your AI model server.

    Remember to replace the placeholder values like modelUri, the resource requirements, and any other custom specification fields with the appropriate values for your use case. The depends_on option ensures that the CRD is created before trying to create an instance of it.

    Remember that in a real-world setup, you will need to design and write the CRD YAML file (ai_model_server_crd.yaml) that fully captures the configuration of an AI model server setup as per your requirements. The CRD YAML file would be applied to the Kubernetes cluster once (usually by a cluster administrator), and the Pulumi program can then be run to deploy instances of the AI model server as needed.