1. Redis Pub/Sub for Asynchronous AI Model Inference


    To achieve Redis Pub/Sub for asynchronous AI model inference, you would typically set up a Redis instance configured with Pub/Sub capabilities. With these capabilities, you can publish messages, such as inference tasks, to a Redis channel and have one or multiple subscribers process these messages asynchronously.

    In this program, we'll use Pulumi to provision a Google Cloud Platform (GCP) Redis instance with Pub/Sub features enabled. The following components are used:

    • Google Cloud Memorystore for Redis: A fully managed Redis service for fast in-memory data store. Public documentation can be found here.

    The program is structured as follows:

    • Import the required Pulumi libraries for GCP.
    • Set up a GCP Redis instance with appropriate configurations for Pub/Sub.
    • Export the Redis instance details which might be required for connecting to it from an application.

    Now, let's go ahead and write a Pulumi program in Python to provision a Redis instance with Pub/Sub enabled on GCP.

    import pulumi import pulumi_gcp as gcp # Create a GCP Redis instance redis_instance = gcp.redis.Instance("ai-model-inference-redis", # Provide the necessary options as needed. For example: # region: Specify the region where the Redis instance will be created. region="us-central1", # Update this as per your location requirement. # tier: Select the service tier (e.g., STANDARD_HA for high availability) tier="STANDARD_HA", # memory_size_gb: Set the capacity of the Redis instance in GB. memory_size_gb=1, # authorized_network: Set the authorized network for the Redis instance. # Ensure that your compute resources are within this network to connect to this Redis instance. authorized_network="default", # redis_configs: Additional configurations for Redis instance. # Enable Pub/Sub feature through these configurations if needed. # E.g., setting maxmemory-policy to allkeys-lru for caching scenarios. # Note that specific Pub/Sub configuration options may vary and should be set as per requirement. redis_configs = { "maxmemory-policy": "allkeys-lru" }, ) # Export the host and port for the Redis instance to connect from an application. pulumi.export("redis_host", redis_instance.host) pulumi.export("redis_port", redis_instance.port)

    This code snippet does not include all possible configurations for a Redis instance. You should adjust the "redis_configs" section and other parameters as per your exact requirements for the AI model inference workload. For instance, you may require different Redis configs or a larger instance for memory-heavy operations.

    You can then use the exported Redis host and port to configure your AI model inference application to connect to and use the Redis instance for Pub/Sub messaging. Your application would publish inference tasks to a Redis channel using a Redis client library, and your inference workers would subscribe to the channel to receive and process tasks asynchronously.