1. ConfigMaps for Environment Variable Management in AI Services


    ConfigMaps are a Kubernetes feature that lets you separate configuration artifacts from image content to keep containerized applications portable. They are used to store non-confidential data in key-value pairs. In the context of AI services, ConfigMaps can be used to manage environment variables and other configuration data that are required for your application to run.

    While Pulumi allows you to manage infrastructure across various cloud providers and services, for Kubernetes resources such as ConfigMaps, you would use the Pulumi Kubernetes SDK.

    Below is a Python program that demonstrates how to create a ConfigMap in Kubernetes using Pulumi. It assumes that you have a Kubernetes cluster available and your kubectl command line is configured to interact with it.

    import pulumi from pulumi_kubernetes.core.v1 import ConfigMap # Define a ConfigMap resource for Kubernetes. # This ConfigMap will store environment variables and configuration data used by an AI service. ai_service_config = ConfigMap( 'ai-service-config', # Metadata provides information about the ConfigMap, such as its name and labels. metadata={ 'name': 'ai-service-env', 'labels': { 'app': 'ai-service' } }, # The actual data stored in the ConfigMap as key-value pairs. data={ 'MODEL_PATH': '/models/my-ai-model', 'API_KEY': 's3cr3t', 'LOG_LEVEL': 'info' } ) # Export the name of the ConfigMap so it can be easily retrieved if needed. pulumi.export('config_map_name', ai_service_config.metadata['name'])

    This program defines a ConfigMap named ai-service-env that could store environment variables relevant to an AI service. In this case:

    • MODEL_PATH describes the file system location where a machine learning model might be stored.
    • API_KEY might represent an API key needed for some service your application interacts with.
    • LOG_LEVEL could be used to determine the verbosity of the application's logging.

    We use pulumi.export to output the name of the ConfigMap, which can be useful for scripting or command-line interaction.

    Keep in mind that this is a basic example, and in a real-world situation you would need to secure sensitive data. For handling sensitive configuration in Kubernetes, you would typically use Secrets instead of ConfigMaps.

    To make full use of this ConfigMap, you would mount it to your Kubernetes pods that run your AI service application, allowing the application to reference the provided configuration. This can be done as part of your pod or deployment definitions using the Pulumi Kubernetes SDK.