1. Auto-Scaling AI Workflows with Consul and Terraform Integration


    It appears you are interested in auto-scaling AI workflows, possibly for machine learning, and you'd like to use Consul for service discovery or configuration and Terraform for infrastructure as code. In the context of Pulumi, while Pulumi itself serves as an alternative to Terraform, it provides similar functionality and can work with many of the same providers and services that Terraform does.

    With Pulumi, you would define your infrastructure using real programming languages like Python, TypeScript, JavaScript, or Go, rather than the declarative HCL used by Terraform. For this scenario, I will demonstrate how to create a Pulumi program in Python that sets up auto-scaling for AI (machine learning) workflows with integration to services that may fill similar roles as Consul.

    In AWS, one might use SageMaker for machine learning workflows, and AWS Auto Scaling along with other AWS services for setting up auto-scaling. AWS Systems Manager (SSM) Parameter Store or AWS App Config could be used for application configuration similar to Consul.

    Here is a Pulumi program that sets up an AWS SageMaker Pipeline for machine learning workflows and configures auto-scaling for an associated EC2 instance group (assuming this is where the AI workload will run). Note that for a fully functional machine learning pipeline, you'd need to configure SageMaker jobs with specific machine learning models, data sources, and compute resources, which is beyond the scope of this initial example. However, the program will show how to create the foundational services:

    import pulumi import pulumi_aws as aws import pulumi_aws_native as aws_native # Create an IAM role for AWS SageMaker to access AWS services sagemaker_role = aws.iam.Role('SageMakerRole', assume_role_policy={ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "sagemaker.amazonaws.com" }, }], }) # Attach a policy to the SageMaker role for necessary permissions sagemaker_policy_attachment = aws.iam.RolePolicyAttachment('SageMakerPolicyAttachment', role=sagemaker_role.name, policy_arn=aws.iam.ManagedPolicy.AMAZON_SAGE_MAKER_FULL_ACCESS) # Define a SageMaker Pipeline - Replace the placeholder definitions with actual pipeline steps sagemaker_pipeline = aws_native.sagemaker.Pipeline("ai-workflow-pipeline", role_arn=sagemaker_role.arn, pipeline_name="AIWorkflowPipeline", # Define the SageMaker pipeline steps here pipeline_definition={ # Pipeline steps would be defined here as JSON # This is a place holder for the actual definition }) # Define an EC2 Auto Scaling Group for the AI Workflows # As an example, this could be a fleet of instances where ML models are deployed auto_scaling_group = aws.autoscaling.Group("AutoScalingGroup", desired_capacity=1, max_size=3, min_size=1, health_check_type="EC2", vpc_zone_identifiers=["subnet-xxxxxxxx"], # Replace with actual subnet IDs # Define launch configuration, scaling policies, etc. ) # Enable auto-scaling based on CPU utilization cpu_scaling_policy = aws.autoscaling.Policy("CpuScalingPolicy", auto_scaling_group_name=auto_scaling_group.name, adjustment_type="ChangeInCapacity", scaling_adjustment=1, cooldown=300) # Although this program doesn't directly integrate with Consul, # the infrastructure setup here is the start of an auto-scaling architecture. # Additional components and logic would be needed to fully implement the # desired auto-scaling behavior and optionally integrate with configuration services. # Lastly, export the SageMaker Pipeline ARN so that we can easily reference it pulumi.export('sagemaker_pipeline_arn', sagemaker_pipeline.arn)

    In this program, I have outlined:

    1. IAM Role for SageMaker: This creates an IAM role that SageMaker can assume to work with other AWS services.
    2. SageMaker Pipeline: A Pulumi resource that represents the AWS SageMaker Pipeline, which is a tool for orchestrating complex machine learning workflows.
    3. Auto Scaling Group: This sets up a simple auto-scaling group for EC2 instances. This might represent a set of servers where your machine learning models are deployed or tested. The group scales based on CPU usage, which is a common metric for simple scaling needs.

    Remember, for a proper workflow, you'd need to define pipeline_definition with actual steps. You might also require additional permissions attached to the SageMaker role, and you would configure the auto-scaling group with launch configurations or templates, and specify proper subnet IDs.

    This set up does not include a Pulumi resource representing Consul because AWS offers its own set of tools and services for configuration management and service discovery. If you needed to integrate Consul, it could be done with additional scripting and setup outside the scope of this basic infrastructure script.