1. Kubernetes CRDs for AI Experiment Tracking


    To manage Kubernetes Custom Resource Definitions (CRDs) for an AI experiment tracking application, we would first define the custom resources that represent the different aspects of our application — for instance, experiments, runs, metrics, parameters, and so on. The CRDs will allow us to define these custom resources in a structured way so that the Kubernetes API can understand and manage them.

    Here's a high-level overview of the steps we might take:

    1. Define the CRD for each resource type we want to track. This includes the schema and any validation rules.
    2. Deploy the CRDs to our Kubernetes cluster to make them available for use.
    3. Create instances of the custom resources to represent actual data in our AI experiment tracking system.
    4. Use a Kubernetes Operator or custom controller that might need to interact with these resources to manage the lifecycle of experiments.

    Below is a Pulumi program written in Python that demonstrates how to define and create a CRD and then an instance of that custom resource. Note that this example is illustrative, and the specific fields of your Custom Resources would depend on the exact requirements of your experiment tracking application.

    import pulumi import pulumi_kubernetes as k8s # Pulumi program to demonstrate the creation of a Kubernetes CRD for AI experiment # tracking and an instance of a custom resource. # Step 1: Define the CRD for an "Experiment". # We're defining a simplistic CRD with metadata and spec fields, where spec has details of the experiment. experiment_crd = k8s.apiextensions.v1.CustomResourceDefinition( "ai-experiment-crd", metadata=k8s.meta.v1.ObjectMetaArgs( name="experiments.ai.example.com" # Name is typically in the form of 'plural.group' ), spec=k8s.apiextensions.v1.CustomResourceDefinitionSpecArgs( group="ai.example.com", # Group name of the CRD versions=[k8s.apiextensions.v1.CustomResourceDefinitionVersionArgs( name="v1", # Version of the CRD served=True, storage=True, schema=k8s.apiextensions.v1.CustomResourceValidationArgs( open_api_v3_schema=k8s.apiextensions.v1.JSONSchemaPropsArgs( type="object", properties={ "spec": k8s.apiextensions.v1.JSONSchemaPropsArgs( type="object", properties={ "algorithm": k8s.apiextensions.v1.JSONSchemaPropsArgs(type="string"), "parameters": k8s.apiextensions.v1.JSONSchemaPropsArgs( type="array", items=k8s.apiextensions.v1.JSONSchemaPropsArgs(type="string") ), "metrics": k8s.apiextensions.v1.JSONSchemaPropsArgs( type="object", properties={ "accuracy": k8s.apiextensions.v1.JSONSchemaPropsArgs(type="number"), "loss": k8s.apiextensions.v1.JSONSchemaPropsArgs(type="number"), } ), }, required=["algorithm"] ), } ) ) )], scope="Namespaced", # CRD is namespaced (not cluster-wide) names=k8s.apiextensions.v1.CustomResourceDefinitionNamesArgs( plural="experiments", # Plural name used in the URL/resource name singular="experiment", # Singular name used when displaying single resources kind="Experiment", # Kind is the serialized kind of the resource short_names=["exp"] # Optionally, specify short names for CRD ) ) ) # Step 2: Deploy the CRD. # This is handled by Pulumi when up command is executed and the above resource is applied to the cluster. # Step 3: Create an instance of the "Experiment" custom resource. # This represents an actual experiment in the AI tracking system. experiment_instance = k8s.apiextensions.CustomResource( "example-experiment", api_version="ai.example.com/v1", # API version should match the CRD definition kind="Experiment", # Kind should match the CRD definition metadata=k8s.meta.v1.ObjectMetaArgs( name="exp-1234" # Name of this particular experiment instance ), spec={ "algorithm": "neural-network", "parameters": [ "learning_rate=0.01", "layers=3" ], "metrics": { "accuracy": 0.98, "loss": 0.05 } } ) # Step 4: Define the Kubernetes Operator or custom controller logic. # This is typically a separate program that watches for changes to the custom resources and reacts accordingly. # Note: The actual operator logic is not shown here as it depends on the specific operational requirements of the AI # experiment tracking system and would generally run as a separate deployment within the Kubernetes cluster. # Export the name of the CRD and the custom resource instance. pulumi.export("crd_name", experiment_crd.metadata["name"]) pulumi.export("experiment_name", experiment_instance.metadata["name"])

    This program sets up a CRD for an AI experiment which includes an algorithm, the parameters used during experiment run, and the metrics resulted from the experiment.

    In a real-world scenario, you would need to replace the algorithm, parameters, and metrics fields with the actual details relevant to your AI experiments. You would also need to develop an operator that understands how to interact with these custom resources. This operator would likely be a separate application written in Go, Python, or another language, which listens for events on these CRDs and performs required actions like setting up training jobs, tracking progress, and recording results.