1. Managed ML Workflow Deployment with CloudFormation


    Managing a machine learning (ML) workflow often involves setting up a series of resources, such as compute instances, storage buckets, and data processing services. AWS CloudFormation is a service that helps you model and set up your Amazon Web Services resources so that you can spend less time managing those resources and more time focusing on your applications. In the context of managed ML workflows, you might utilize CloudFormation to define and deploy the infrastructure needed for ML workloads automatically.

    With Pulumi, you can programmatically define CloudFormation stacks to manage your ML workflows using familiar programming languages, such as Python. Below is a Python program that uses Pulumi with AWS to create a CloudFormation stack. This stack deploys an Amazon SageMaker training job, which is a common component of a managed ML workflow. Amazon SageMaker is a fully managed service that provides developers and data scientists with the ability to build, train, and deploy machine learning models quickly. Although creating a SageMaker training job doesn't cover every aspect of ML workflows, it gives you an idea of how you would use Pulumi for such tasks.

    The following program is an example of how to create a CloudFormation stack for a SageMaker training job using Pulumi with Python:

    import pulumi import pulumi_aws as aws # Define a CloudFormation stack that includes a SageMaker training job. # For the SageMaker training job, define the necessary parameters and capabilities. # Here we are providing a script inline, but in real use cases, you might want to reference # a script hosted on S3 or passed as a parameter. ml_workflow_stack = aws.cloudformation.Stack("ml-workflow-stack", # Define a SageMaker training job template inline. template_body=pulumi.Output.all().apply(lambda _: """ AWSTemplateFormatVersion: '2010-09-09' Description: An AWS CloudFormation template for SageMaker Training Job. Resources: SageMakerTrainingJob: Type: 'AWS::SageMaker::TrainingJob' Properties: TrainingJobName: 'pulumi-sagemaker-training-job' AlgorithmSpecification: TrainingInputMode: 'File' TrainingImage: '382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:1' OutputDataConfig: S3OutputPath: 's3://my-sagemaker-bucket/output/' ResourceConfig: InstanceCount: 1 InstanceType: 'ml.m4.xlarge' VolumeSizeInGB: 10 RoleArn: 'arn:aws:iam::123456789012:role/SageMakerRole' StoppingCondition: MaxRuntimeInSeconds: 3600 InputDataConfig: - ChannelName: 'train' DataSource: S3DataSource: S3DataType: 'S3Prefix' S3Uri: 's3://my-sagemaker-bucket/input/train' S3DataDistributionType: 'FullyReplicated' """), # Some CloudFormation capabilities required to create IAM resources capabilities=["CAPABILITY_IAM", "CAPABILITY_NAMED_IAM"] ) # Export the outputs - CloudFormation stack name. pulumi.export('ml_workflow_stack_name', ml_workflow_stack.name)

    This program creates a CloudFormation stack with a template that defines a SageMaker training job. The template_body is defined inline with the required properties for a SageMaker training job, such as algorithm specifications, output data configuration, resource configuration (including instance type and count), IAM role ARN, stopping condition, and input data configuration.

    Please ensure you have the necessary permissions in AWS IAM to create associated resources and run this job, and replace the placeholder values with actual S3 bucket paths, role ARNs, and any other specifics pertinent to your workflow.

    For more in-depth information on AWS CloudFormation resources using Pulumi:

    To understand more about AWS SageMaker and how to configure it using CloudFormation specifically:

    Remember, to run this Pulumi program, you will need to have Pulumi installed, the AWS Pulumi plugin, and your cloud credentials configured.