1. Serverless Development Environment for AI Applications on AWS Cloud9


    If you're looking to create a serverless development environment for AI applications, AWS Cloud9 is a great choice. Cloud9 is an integrated development environment (IDE) that allows you to write, run, and debug code with just a browser. It includes a code editor, debugger, and terminal. AWS Cloud9 comes prepackaged with essential tools for popular programming languages and the AWS Command Line Interface (CLI) pre-installed, so you don’t need to install files or configure your laptop for this serverless development.

    Below is a Pulumi Python program that sets up a Cloud9 environment for serverless development. The main components are:

    • EnvironmentEC2: Creates a Cloud9 EC2 environment for development, which is suitable for serverless applications since you're using AWS's compute resources. You can specify the instance type depending on your needs. For AI applications, an instance with more compute and memory might be appropriate.
    • EnvironmentMembership: Manages access to your Cloud9 environment. You can share your environment with other AWS users by specifying their User ARN and assigning permissions.

    Here's the program that sets this up:

    import pulumi import pulumi_aws as aws # Create an AWS Cloud9 EC2 environment for serverless development. # This will launch an EC2 instance to access the AWS Cloud9 IDE. # Documentation: https://www.pulumi.com/registry/packages/aws/api-docs/cloud9/environmentec2/ cloud9_environment = aws.cloud9.EnvironmentEC2("my_ai_dev_environment", instance_type="t3.medium", # Choose an instance type suitable for your AI application needs. name="ai-serverless-dev-environment", # Name your Cloud9 environment. description="Cloud9 Environment for Serverless AI Development", # Provide a useful description. automatic_stop_time_minutes=30, # Set an automatic stop time for the environment to save costs. owner_arn=pulumi.config.require("owner_arn"), # Specify your AWS Account as the owner. subnet_id=pulumi.config.require("subnet_id") # Optional: specify the VPC subnet ID if you need the environment in a specific VPC. ) # Optionally, manage memberships to allow other developers to access your Cloud9 environment. # Documentation: https://www.pulumi.com/registry/packages/aws/api-docs/cloud9/environmentmembership/ cloud9_environment_membership = aws.cloud9.EnvironmentMembership("dev_membership", user_arn="arn:aws:iam::123456789012:user/AnotherDeveloper", # Replace with the ARN of the user you wish to share access with. environment_id=cloud9_environment.id, permissions="read-write", # Assign the permission level for the user. Options are "read-only" or "read-write". ) # Export the Cloud9 IDE URL for accessing the environment. pulumi.export("cloud9_ide_url", cloud9_environment.ide_url)

    Some things to note:

    • Replace "arn:aws:iam::123456789012:user/AnotherDeveloper" with the actual ARN of the AWS user you wish to share your Cloud9 environment with.
    • t3.medium instance type is specified for this example; however, depending on your application requirements, you may choose a more powerful instance type.
    • The owner_arn and subnet_id are grabbed from the Pulumi config, which you would need to set using pulumi config set owner_arn your-arn-value and pulumi config set subnet_id your-subnet-id commands before running pulumi up.

    Make sure your AWS CLI is configured, and you have installed the Pulumi CLI and chosen your preferred programming language SDK. This program should be placed in a Pulumi project directory, and you can deploy it using the pulumi up command.

    When you run the program, Pulumi will provision the specified resources in your AWS account, and you will get a URL for the Cloud9 environment where you can start developing your serverless AI applications.