1. ML Experimentation Workspace Provisioning on AWS Cloud9


    AWS Cloud9 is an integrated development environment (IDE) that provides a productive and flexible workspace for developing and running cloud applications. It's ideal for Machine Learning (ML) experimentation and other development work because it provides a consistent environment that is easily repeatable, and it can be integrated with other AWS services such as CodeCommit for version control.

    In the following program, I'll provide you with the Pulumi Python code to provision an AWS Cloud9 environment suitable for ML experimentation.

    Here's what each resource does:

    1. aws.cloud9.EnvironmentEC2 creates an AWS Cloud9 environment with an EC2 instance to run our development workspace.

      • We need to specify the instance type, which will determine the computational resources available for our experiments.
      • Optionally, we can also specify an image ID for the EC2 instance if there's a particular AMI we want to use that has predefined configurations.
    2. aws.codecommit.Repository creates a new CodeCommit repository to store code.

    • This is optional but recommended for version control.
    • We'll set up a Git repository to store and version our machine learning experiments.

    Let's create a Cloud9 environment and a CodeCommit repository with Pulumi:

    import pulumi import pulumi_aws as aws # Assume that the VPC and a public subnet are already created and you have the IDs ready. vpc_id = 'vpc-12345678' subnet_id = 'subnet-87654321' # Create an AWS Cloud9 EC2 environment for ML experimentation. environment_name = 'ml-experimentation-env' instance_type = 't3.large' # Instance type can be adjusted based on the needs of the ML workload. ml_environment = aws.cloud9.EnvironmentEC2(environment_name, instance_type=instance_type, subnet_id=subnet_id, # Optionally specify the image_id if needed. # image_id='ami-0abcbc123456789' ) # Optionally create an AWS CodeCommit repository to store our ML experiments code. repo_name = 'ml-experiments-repository' ml_experiments_repo = aws.codecommit.Repository(repo_name, repository_name=repo_name, description='Repository for ML experiments code' ) # Export the Cloud9 environment ID and CodeCommit repository clone URL to use later pulumi.export('cloud9_environment_id', ml_environment.id) pulumi.export('codecommit_repo_clone_url_http', ml_experiments_repo.clone_url_http)

    This Pulumi program does the following:

    • Sets up an AWS Cloud9 environment (EnvironmentEC2) on a specified type of EC2 instance to ensure we have the computational power for ML tasks.
    • Creates a CodeCommit repository (Repository) where we can store our ML codebase, keeping it backed up and version-controlled.

    To run this code, you would need to have Pulumi installed and set up with your AWS credentials. You can then save this code into a file named __main__.py and deploy it with the pulumi up command.

    What's happening behind the scenes is that Pulumi is converting this declarative code into the imperative API calls required to provision these resources on AWS. Once provisioned, you can access the Cloud9 IDE from the AWS console and get a complete development environment without any setup needed on your local machine.

    Feel free to customize the instance type, include additional Cloud9 environment configuration, or set up additional resources as needed for your specific ML projects.