1. AI Environment Setup And Tear Down Workflows with AWS SSM


    Setting up and tearing down environments with AWS Systems Manager (SSM) involves automating resource management and configuration tasks to perform these operations efficiently and repeatably. AWS SSM provides a suite of capabilities such as running commands across an infrastructure, automating software provisioning, managing instance configurations, and much more.

    In this context, we'll create a Pulumi program for AWS that uses Systems Manager to set up an Artificial Intelligence (AI) environment. This could involve creating an SSM document to set up the necessary software and configurations, as well as creating another document for tearing down the environment when it's no longer needed.

    Automating Environment Setup with AWS SSM Documents

    AWS SSM Documents define a series of steps to be executed on managed instances and are an essential part of automation within AWS Systems Manager. They can be used to install software, apply configurations, or even run complex scripts.

    Let's assume our AI environment requires the installation of certain packages, configuration of environment variables, and setting up of data storage paths. We'll define an SSM Document to accomplish this setup. You can later modify the document contents to represent the actual commands or scripts you want to run.

    Tearing Down the AI Environment

    Similarly, you might need to remove installed software, clean up environment variables, and delete data storage paths when tearing down the environment. We'll create another SSM Document for these teardown steps.

    Below is a Python program for Pulumi that outlines creating these SSM Documents. This program is a starting point and needs to be customized for your specific AI environment setup and teardown requirements.

    import pulumi import pulumi_aws as aws # SSM Document for setting up the AI environment setup_document = aws.ssm.Document("aiEnvSetup", content={ "schemaVersion": "2.2", "description": "Document to set up AI environment.", "mainSteps": [ # Insert the specific commands or configuration management steps here ], }, document_type="Command", tags={ "Purpose": "AI Environment Setup", }) # SSM Document for tearing down the AI environment teardown_document = aws.ssm.Document("aiEnvTeardown", content={ "schemaVersion": "2.2", "description": "Document to tear down AI environment.", "mainSteps": [ # Insert the specific commands or configuration management steps here ], }, document_type="Command", tags={ "Purpose": "AI Environment Teardown", }) # Export the names of the setup and teardown documents pulumi.export('setup_document', setup_document.name) pulumi.export('teardown_document', teardown_document.name)


    • Two AWS SSM Documents are defined, aiEnvSetup and aiEnvTeardown, using the aws.ssm.Document resource. These documents include placeholders for the mainSteps field where commands and scripts should be specified.
    • The document_type is set to "Command", meaning the documents are intended to run shell commands on target instances.
    • We tag the documents with a "Purpose" to make their intent clear.
    • Finally, we export the names of both documents so they can be easily retrieved after the deployment.

    Remember that this is a template; you will need to fill in the mainSteps field with the actual code that sets up and tears down your AI environment.

    To use these documents, you would typically target them using SSM Run Command or State Manager, choosing the managed instances they get applied to. This is a key part of using AWS SSM for operations automation at scale.