Secure Multi-User JupyterHub for AI Research

Question

Pulumi · Accepted Answer

To create a secure multi-user JupyterHub designed for AI research, we can use a combination of cloud services and Pulumi resources to deploy a robust environment. The environment will ensure that each user has access to the necessary computational resources and that the data and computation processes are securely managed.

The following program will deploy a JupyterHub instance on AWS, leveraging the AWS SageMaker service. AWS SageMaker provides the necessary tools for machine learning development, including Jupyter notebooks, which can be used for collaborative AI research in a secure multi-user environment.

Before the Pulumi program, here are the key steps that the program will perform:

1. **Set Up SageMaker Domain and User Profile**: SageMaker Domains allow us to create isolated environments for users and assign user-specific profiles. User profiles within the domain enable each researcher to have a consistent set of tools, kernels, and storage.

2. **Create a SageMaker Notebook Instance**: We will create a Jupyter notebook instance where the actual data processing and machine learning development will take place.

3. **Configure Security**: The security in this setup is managed through AWS Identity and Access Management (IAM), which controls the authentication and authorization for SageMaker resources.

Here's the Pulumi Python program:

```python
import pulumi
import pulumi_aws as aws

# Configure AWS Provider
aws_provider = aws.Provider("aws", region="us-west-2")

# Create a SageMaker Domain which encapsulates the environment for the users
sagemaker_domain = aws.sagemaker.Domain("aiResearchDomain",
    auth_mode="IAM",
    default_user_settings=aws.sagemaker.DomainDefaultUserSettingsArgs(
        jupyter_server_app_settings=aws.sagemaker.DomainDefaultUserSettingsJupyterServerAppSettingsArgs(
            default_resource_spec=aws.sagemaker.DomainDefaultUserSettingsJupyterServerAppSettingsDefaultResourceSpecArgs(
                instance_type="ml.t3.medium",  # Choose instance type suitable for AI research workload
            ),
        ),
        kernel_gateway_app_settings=aws.sagemaker.DomainDefaultUserSettingsKernelGatewayAppSettingsArgs(
            default_resource_spec=aws.sagemaker.DomainDefaultUserSettingsKernelGatewayAppSettingsDefaultResourceSpecArgs(
                instance_type="ml.t3.medium",  # Choose instance type suitable for AI research workload
            ),
        ),
    ),
    domain_name="ai-research-domain",
    vpc_id=aws_vpc.id,  # Assume a VPC has been set up in your AWS account
    subnet_ids=[aws_subnet.id],  # Assume a Subnet has been set up in your VPC
    tags={
        "Environment": "research",
        "Project": "AIResearch",
    },
    opts=pulumi.ResourceOptions(provider=aws_provider),
)

# Create a SageMaker user profile within the domain for each researcher
researcher_user_profile = aws.sagemaker.UserProfile("ResearcherUserProfile",
    domain_id=sagemaker_domain.id,
    user_profile_name="researcher-user-profile",
    user_settings=aws.sagemaker.UserProfileUserSettingsArgs(
        jupyter_server_app_settings=aws.sagemaker.UserProfileUserSettingsJupyterServerAppSettingsArgs(
            default_resource_spec=aws.sagemaker.UserProfileUserSettingsJupyterServerAppSettingsDefaultResourceSpecArgs(
                instance_type="ml.t3.medium",
            ),
        ),
    ),
    opts=pulumi.ResourceOptions(provider=aws_provider),
)

# Export the URLs for researchers to access their Jupyter environments in SageMaker
pulumi.export("JupyterUserProfileURL", pulumi.Output.concat("https://", aws_provider.region, ".console.aws.amazon.com/sagemaker/home?region=", aws_provider.region, "#/user-profiles/", sagemaker_domain.domain_id, "/", researcher_user_profile.user_profile_name))
```

This Pulumi program creates the necessary resources for setting up a multi-user JupyterHub on AWS SageMaker. Here's an overview of what we've defined:

- **SageMaker Domain (`sagemaker.Domain`)**: This creates an isolated environment where all user activities will be contained.
- **User Profile (`sagemaker.UserProfile`)**: This sets up individual profiles for researchers within the domain which includes their Jupyter server settings.
- **Export URL**: To access the JupyterHub environment, we construct and export the URL that each researcher can visit to start their AI research environment.

It's essential to replace `aws_vpc.id` and `aws_subnet.id` with the actual VPC and Subnet IDs where you plan to deploy the SageMaker domain. You can get these values from your existing AWS infrastructure setup.

Please ensure your AWS account has the necessary permissions and that the Pulumi CLI is correctly set up and configured with your AWS credentials to apply this program successfully.

Each researcher can access their JupyterHub environment using the URL exported by the program, providing a collaborative and secure AI research platform on AWS.