1. High-Throughput Inference with Auto-Scaling GPU Instances


    If you're aiming to set up a high-throughput inference service that utilizes GPUs and automatically scales to handle the workload, you would likely be looking at deploying a set of GPU-enabled virtual machines (VMs) or containers that are orchestrated by an auto-scaling service.

    In our case, we'll consider an auto-scaling setup on AWS using EC2 instances with attached GPU hardware. We'll also incorporate AWS SageMaker for the machine learning inference endpoints and the auto-scaling configuration to manage demand.

    Pulumi allows you to define cloud resources and infrastructure using real programming languages, such as Python. Below is a Pulumi Python program that demonstrates how to create an auto-scaling SageMaker endpoint for high-throughput inference, with the ability to add GPU instances as needed.

    The main components include:

    1. SageMaker Model: Represents the model that will be used for inferencing.
    2. SageMaker Endpoint Configuration: Contains the configuration for the endpoint, such as the instance type which will be GPU-optimized for our use case.
    3. SageMaker Endpoint: The actual endpoint where the inferencing requests are sent.
    4. AutoScaling Policy: Defines how the endpoint will scale in response to real-time metrics. In this scenario, we would scale based on the overall utilization of the GPU instances.

    Here's the Pulumi program:

    import pulumi import pulumi_aws as aws # Define the SageMaker Model, which is the first step in setting up an inference pipeline. # Replace `execution_role_arn` with your SageMaker role's ARN, and `image` with your custom SageMaker docker image URL sagemaker_model = aws.sagemaker.Model("example-sagemaker-model", execution_role_arn="arn:aws:iam::123456789012:role/SageMakerRole", primary_container={ "image": "123456789012.dkr.ecr.us-west-2.amazonaws.com/your-custom-image-url", } ) # Define the SageMaker Endpoint Configuration with a GPU instance type. # Use an appropriate instance type like 'ml.p3.2xlarge' which is GPU enabled. sagemaker_endpoint_config = aws.sagemaker.EndpointConfiguration("example-sagemaker-endpoint-config", production_variants=[{ "variantName": "Variant1", "modelName": sagemaker_model.name, "initialInstanceCount": 1, "instanceType": "ml.p3.2xlarge", }] ) # Deploy the SageMaker Endpoint based on the configuration. sagemaker_endpoint = aws.sagemaker.Endpoint("example-sagemaker-endpoint", endpoint_config_name=sagemaker_endpoint_config.name ) # Configure auto-scaling for the SageMaker Endpoint. # Adjust the resource ID, minimum and maximum number of instances, and the target utilization as per your requirements. autoscaling_policy = aws.appautoscaling.Policy("example-sagemaker-autoscaling-policy", resource_id=sagemaker_endpoint.arn.apply(lambda arn: f"endpoint/{arn.split('/')[-1]}/variant/Variant1"), scalable_dimension="sagemaker:variant:DesiredInstanceCount", service_namespace="sagemaker", policy_type="TargetTrackingScaling", target_tracking_scaling_policy_configuration={ "targetValue": 70.0, "scaleInCooldown": 300, "scaleOutCooldown": 300, "predefinedMetricSpecification": { "predefinedMetricType": "SageMakerVariantInvocationsPerInstance", }, } ) # Export the SageMaker Endpoint name. pulumi.export('sagemaker_endpoint_name', sagemaker_endpoint.endpoint_name)

    In this program, the SageMaker Model resource defines the machine learning model that will handle the inference requests. We use a pre-built Docker image that is stored in Amazon ECR, which contains the model and any required inference code.

    The SageMaker Endpoint Configuration specifies the type of instances that the endpoint should use, including their count, and it's connected to the previously specified model. Here, we're specifically asking for GPU-enabled instances, which are suitable for high-throughput inference tasks.

    We then create the SageMaker Endpoint itself, which is the resource that receives real-time inference requests.

    Lastly, the AutoScaling Policy resource allows you to set up auto-scaling for the endpoint, ensuring your infrastructure scales up or down based on the load, maintaining efficient resource utilization and cost-effectiveness. We configure it to track a metric tied to the invocations per instance of your endpoint, scaling in and out to keep this metric around a target value.

    Ensure you replace placeholders like execution_role_arn, image, and resource_id with actual values based on your AWS setup and application requirements. The instance type ml.p3.2xlarge is an example; pick an instance type that suits your workload needs.

    To deploy this infrastructure with Pulumi, you need to have the Pulumi CLI installed, AWS credentials configured on your system, and then you simply run pulumi up in the directory containing this program. Pulumi handles the provisioning and configuration of the resources.