1. Scaling AI Inference Endpoints with Service Quotas


    To scale AI inference endpoints effectively, one must ensure that the cloud services in use can accommodate the scaling requirements. This can involve several steps, including configuring service quotas to allow for enough resources, as well as managing the cloud resources that serve the inference endpoints. Since you've expressed a need for scaling AI inference endpoints with service quotas, it will depend on the cloud provider and services you are using for your AI workload.

    AWS Example: Scaling SageMaker Endpoint with Service Quota Increase

    AWS SageMaker allows you to deploy AI models for inference, and AWS Service Quotas allows you to manage the service limits. Consider an example where you have an existing SageMaker Endpoints, and you need to scale up the service by increasing the number of instances in the endpoint configuration. Scaling might be necessary due to increased traffic requiring more inference capacity.

    Below is a Pulumi program written in Python that demonstrates how to scale up an AWS SageMaker endpoint with an update to service quotas using Pulumi with the AWS provider.

    import pulumi import pulumi_aws as aws # Retrieve existing resources. In practice, these should match up with the names you've used. # If this is a new setup, you'll need to create these resources and replace these with actual Pulumi resources. sagemaker_endpoint_config_name = "existing-endpoint-config-name" sagemaker_endpoint_name = "existing-endpoint-name" # Increase the SageMaker service quota. # The quota code and service code are specific to the type of quota you're increasing. # You will need to find the right codes for the quotas you wish to increase. # For instance, "ml.c4.2xlarge.instance.count" is a made-up example. service_quota = aws.servicequotas.ServiceQuota("ai_service_quota", quota_code="desired-quota-code", # Quota code as per AWS documentation service_code="sagemaker", # Service code as per AWS documentation value=10 # New quota value, assuming we're increasing it to allow 10 instances ) # Update SageMaker endpoint configuration with new resource values. # This modification signals that you want more instances of a specific type for inference. # In this case, `DesiredInstanceCount` is a made-up example and would be specific to the resource you are using. sagemaker_endpoint_config = aws.sagemaker.EndpointConfiguration.get("existing-sagemaker-endpoint-config", id=sagemaker_endpoint_config_name, endpoint_config_name=sagemaker_endpoint_config_name) sagemaker_endpoint = aws.sagemaker.Endpoint("existing-sagemaker-endpoint", endpoint_name=sagemaker_endpoint_name, endpoint_config_name=sagemaker_endpoint_config.endpoint_config_name.apply( lambda name: sagemaker_endpoint_config # Replace with new config if applicable ), # Other necessary configurations... ) # Export the endpoint URL so you can use it in other systems (CI/CD, monitoring, etc.) pulumi.export("endpoint_url", sagemaker_endpoint.endpoint_url) # This assumes the updated configuration will automatically apply to the endpoint

    This program uses Pulumi to define an AWS service quota increase (ServiceQuota) to allow more resources for your AI inference endpoint and assumes we're updating an existing SageMaker Endpoint with a new configuration that can have increased capacity.

    Key Points to Understand in the Program:

    • Service Quotas: AWS provides specific codes to identify each service quota. You need to change the quota_code value to the specific quota you wish to increase.
    • SageMaker Resources: The existing SageMaker Endpoint and Configuration are retrieved and assumed to be updated with the new resource values for scaling.
    • Exported Output: The endpoint URL is exported as an output from the Pulumi stack. This is useful for accessing the endpoint outside of Pulumi, such as in a CI/CD pipeline or in application code that needs to make inference requests.

    Remember to replace placeholder values with your actual resource names, IDs, and desired quota values. Quota codes for AWS Service Quotas can be found in the AWS documentation.

    This example is specific to AWS, if you're using another cloud provider like Azure or GCP, you would use their respective resources and methods to achieve the scaling of AI inference endpoints.

    Feel free to adjust the resources and scaling logic to match your specific use case and resource names within your cloud environment.