1. Auto-Scaling for AI Model Serving Workloads

    Python

    Auto-scaling for AI model serving workloads is a powerful way to ensure your applications can dynamically allocate resources to meet demand, while also optimizing costs by scaling down when workloads decrease. This is accomplished by automatically adjusting the number or capacity of active servers, based on real-time demand.

    To set up auto-scaling for your AI model serving workloads on a cloud provider using Pulumi, you would use a combination of cloud services. Most cloud providers offer compute services that are capable of auto-scaling, often in combination with specific services for machine learning model deployment.

    Below, I will give you an example with AWS, utilizing its Elastic Compute Cloud (EC2) service along with AWS SageMaker for deploying machine learning models. We use Pulumi's aws package for creating an Auto Scaling Group and configuring auto-scaling policies based on metrics such as CPU usage or requests per minute.

    Here's a step-by-step guide on how it could be done:

    1. Setup an EC2 Auto Scaling Group. This group manages a collection of EC2 instances, and scales them in and out in response to demand.
    2. Create a Launch Configuration/Template that defines the EC2 instances to be launched within the Auto Scaling Group. This would include the instance size, AMI, and other configurations.
    3. Define Scaling Policies. These are the rules that determine when instances should be added or removed based on metrics like CPU utilization or custom metrics that better reflect your workload.
    4. Use AWS SageMaker to deploy your AI model. SageMaker can be used to easily deploy, monitor, and scale machine learning models.
    5. Integrate SageMaker Endpoint with Auto Scaling Group. Tie the SageMaker endpoint to Auto Scaling to dynamically add or remove resources as needed.

    Now let's walk through a code example that sets up auto-scaling for an AI model serving workload:

    import pulumi import pulumi_aws as aws # Create an AWS Auto Scaling Group to manage EC2 instances auto_scaling_group = aws.autoscaling.Group("aiModelAutoScalingGroup", # Providing a Launch Template launch_template=aws.autoscaling.GroupLaunchTemplateArgs( id=launch_template.id, version="$Latest", ), min_size=1, max_size=5, vpc_zone_identifiers=["subnet-049df61146adb8a18", "subnet-0e4ac3a09cc1a4a35"], # Specifying scaling policies based on desired metrics, for example, CPU utilization. # More sophisticated scaling policies can include custom metrics that reflect inference load target_group_arns=[alb_target_group.arn], health_check_type="ELB", health_check_grace_period=300, force_delete=True, tags={ "Name": "aiModelAutoScalingGroup", }) # Creating an auto-scaling policy for scaling out (adding instances) scale_out_policy = aws.autoscaling.Policy("scaleOutPolicy", scaling_adjustment=1, adjustment_type="ChangeInCapacity", cooldown=300, autoscaling_group_name=auto_scaling_group.name) # Creating an auto-scaling policy for scaling in (removing instances) scale_in_policy = aws.autoscaling.Policy("scaleInPolicy", scaling_adjustment=-1, adjustment_type="ChangeInCapacity", cooldown=300, autoscaling_group_name=auto_scaling_group.name) # Define a SageMaker endpoint for model serving # Assuming you already have a SageMaker model, endpoint configuration, etc. # Model deployments can be automated using Pulumi. # However, you need to have your model trained and saved in a format that SageMaker expects. # Typically, this step would be performed after your model is trained and ready for deployment. sagemaker_model = aws.sagemaker.Model("aiModel", execution_role_arn=iam_role.arn, primary_container=aws.sagemaker.ModelPrimaryContainerArgs( image=aws_sagemaker_prebuilt_image, model_data_url=model_data_s3_url, )) sagemaker_endpoint_config = aws.sagemaker.EndpointConfiguration("aiModelEndpointConfig", production_variants=[{ "instanceType": "ml.m4.xlarge", "modelName": sagemaker_model.name, "variantName": "variant-1", }]) sagemaker_endpoint = aws.sagemaker.Endpoint("aiModelEndpoint", endpoint_config_name=sagemaker_endpoint_config.name) # Export the endpoint name so that we can query it from the outside pulumi.export("sagemaker_endpoint_name", sagemaker_endpoint.endpoint_name)

    In this example, Pulumi is setting up auto-scaling for EC2 instances within an Auto Scaling Group (aws.autoscaling.Group). It uses a launch template that defines the configuration of instances when they are launched.

    The auto-scaling policies (aws.autoscaling.Policy) define the rules for scaling out (adding instances) and scaling in (removing instances). These are attached to our Auto Scaling Group.

    The AWS SageMaker resources (aws.sagemaker.Model, aws.sagemaker.EndpointConfiguration, and aws.sagemaker.Endpoint) represent the deployment of the trained AI model that clients can send inference requests to. We've exported the endpoint name so it can be accessed by other applications or for monitoring purposes.

    You'll need to replace placeholders (like subnet IDs, IAM role ARN, prebuilt SageMaker image ARN, and S3 URL for the model data) with actual values relevant to your AWS environment and use case.

    The auto-scaling policies, instance types, and other specifics can be optimized based on the cost, performance, and latency requirements of your AI workload.

    Configure AWS credentials and region settings using Pulumi configuration or environment variables before running this program. Make sure that the required IAM roles and policies have been created to grant the necessary permissions for SageMaker and EC2 Auto Scaling.


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