Managed Access for AI Development Environments

Question

Pulumi · Accepted Answer

To create a managed access for AI development environments, we must define a set of resources that allow fine-grained access control and collaborative development features. Using a combination of cloud resources and Pulumi's Infrastructure as Code, we can achieve this in a structured and repeatable way.

We will assume you want to create this environment in AWS since it offers a comprehensive set of services for AI development, including SageMaker for machine learning, Cloud9 as an integrated development environment, and IAM for access management. AWS Resource Access Manager (RAM) will help us to securely share resources across AWS accounts.

Here's how we'll set it up:

1. **AWS IAM Policy**: To control what actions are allowed by the users of this environment, we will define an IAM policy.

2. **AWS Cloud9 Environment**: For the development environment, we will provision an AWS Cloud9 environment. Cloud9 is a cloud-based integrated development environment (IDE) that lets you write, run, and debug your code just with a browser.

3. **AWS SageMaker Project**: To facilitate machine learning workflows, we would create a SageMaker project, allowing you to bring your own custom code or use pre-built templates.

4. **AWS Resource Access Manager (RAM)**: To share your AWS resources with the rest of your team, we use AWS RAM to create a resource share.

Let's go ahead and write a Python program using Pulumi that sets up a managed access for an AI development environment.

```python
import pulumi
import pulumi_aws as aws

# Create an IAM policy to define permissions
ai_dev_policy = aws.iam.Policy("aiDevPolicy",
    policy=pulumi.Output.all({
        # Assuming basic policy definition that allows access to Cloud9 and SageMaker
        "Version": "2012-10-17",
        "Statement": [
            {
                "Effect": "Allow",
                "Action": [
                    "cloud9:*",
                    "sagemaker:*"
                ],
                # Define the resources or use "*" for all resources
                "Resource": "*"
            }
        ]
    })
)

# Create a Cloud9 environment which will be used as a development environment
ai_dev_cloud9_environment = aws.cloud9.EnvironmentEC2("aiDevCloud9Environment",
    instance_type="t3.small",
    description="AI Development Cloud9 Environment"
)

# Create a SageMaker project
ai_dev_sagemaker_project = aws.sagemaker.Project("aiDevSageMakerProject",
    projectName="ai-development-project",
    serviceCatalogProvisioningDetails={
        # Assuming a product ID and a provisioning artifact ID for an existing service catalog product
        "productId": "prod-abcd1234",
        "provisioningArtifactId": "pa-5678efgh"
    }
)

# Share the SageMaker project resources with team members using AWS RAM
ai_dev_resource_share = aws.ram.ResourceShare("aiDevResourceShare",
    permissionArns=[
        # Assuming use of existing permission ARNs 
        "arn:aws:ram::aws:permission/AWSResourceAccessManagerReadOnlyAccess",
        "arn:aws:ram::aws:permission/AWSResourceAccessManagerFullAccess"
    ],
    resourceArns=[ai_dev_sagemaker_project.arn],
    allowExternalPrincipals=True
)

# Export the IAM policy ARN and Cloud9 environment ID and URL so that they can be used outside of Pulumi
pulumi.export("ai_dev_policy_arn", ai_dev_policy.arn)
pulumi.export("ai_dev_cloud9_environment_id", ai_dev_cloud9_environment.id)
pulumi.export("ai_dev_cloud9_environment_url", ai_dev_cloud9_environment.url)
```

This program sets up an AI development environment with managed access control. Here's what each part does:

- IAM Policy: Specifies the permissions for resources required in AI development, like Cloud9 and SageMaker.
- Cloud9 Environment: A ready-to-code IDE environment in AWS for developing AI applications.
- SageMaker Project: A managed workspace in AWS for machine learning projects.
- Resource Access Manager: Facilitates secure sharing of the SageMaker project with internal and external accounts.

Each resource is outputted at the end of the program, allowing you to easily reference the created resources. You can extend the IAM policy and other resources to better fit the detailed needs of your AI development environment. Remember to replace placeholder ARNs and properties with actual values specific to your AWS account and needs.