1. High-Availability Inference Endpoints on GCP


    Creating high-availability inference endpoints on Google Cloud Platform (GCP) typically involves deploying a service that can handle inference requests and distribute the load across multiple instances to ensure uptime and scalability. The gcp.vertex.AiEndpoint resource provided by the Pulumi GCP package is a suitable choice for this.

    To set up high-availability inference endpoints with Pulumi, you will define resources in Python that tell Pulumi how to configure the infrastructure on GCP. In this setup, the AiEndpoint resource represents a Google Vertex AI endpoint, which allows you to deploy, maintain, and serve machine learning models with robustness and at scale.

    Here is how you would define a Pulumi program in Python to create a high-availability inference endpoint:

    1. Import the necessary Pulumi packages for GCP.
    2. Create an AI platform endpoint that defines the necessary configurations such as the endpoint name, project details, and location.
    3. Set up related properties that meet your specific requirements, including resources like models that you may want to deploy to the endpoint for making predictions.

    Let's look at what the code might look like:

    import pulumi import pulumi_gcp as gcp # Define the Google Vertex AI Endpoint. # Replace the details like `your_project`, `your_location`, and `your_display_name` # with appropriate values for your GCP project. ai_endpoint = gcp.vertex.AiEndpoint("high-availability-inference-endpoint", project="your_project", location="your_location", display_name="your_display_name", description="High-Availability Inference Endpoint" ) # If you need to deploy a model to the endpoint, you can use "AiModel". # Remember to replace `model_name` and other placeholders with your own details. ai_model = gcp.vertex.AiModel("high-availability-model", project="your_project", location="your_location", display_name="model_name", description="Your Model Description", artifact_uri="gs://your-bucket/path-to-your-model/", container_spec=gcp.vertex.AiModelContainerSpecArgs( image_uri="gcr.io/your-project/your-container-image:tag", command=["/usr/bin/tensorflow_model_server"], args=[ "--model_name=model_name", "--model_base_path=gs://your-bucket/path-to-your-model/serving", "--rest_api_port=8080", "--port=8500" ], env=[ gcp.vertex.AiModelEnvArgs( name="PORT", value="8500" ), ], ports=[ gcp.vertex.AiModelPortArgs( container_port=8500 ), ] ), prediction_resources=gcp.vertex.AiModelPredictionResourcesArgs( min_replica_count=2, # Ensuring high availability max_replica_count=5 # Scale up based on demand ) ) # Export the endpoint_id of the deployed AI endpoint. pulumi.export("endpoint_id", ai_endpoint.endpoint_id)

    In the above code, your_project should be replaced with your actual GCP project ID, your_location with the region where you want to create the endpoint, and your_display_name with a user-friendly name for the endpoint.

    The AiModel code block is optional and used when deploying a model to the newly created inference endpoint. Here, you provide the container specs, environment variables, and ports along with min_replica_count and max_replica_count to make sure your model is highly available and can autoscale to handle varying loads.

    This Pulumi program gives you a starting point for deploying high-availability inference endpoints on GCP with Vertex AI. You can extend this by adding more configurations and connecting it with other GCP resources as needed for your project.