1. Secure Multi-user Environments for Machine Learning Workflows


    To create a secure multi-user environment for machine learning workflows on the cloud, you need to set up an infrastructure that satisfies the following requirements:

    1. A managed platform for running machine learning workloads.
    2. User authentication and access management.
    3. Network security to control traffic to and from the resources.
    4. Resource isolation to ensure one user's processes do not interfere with another's.

    Let’s take AWS as an example. AWS provides Amazon SageMaker which is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.

    Amazon SageMaker offers multi-user environments using Domain and User Profile resources, which you can manage with AWS' native Pulumi provider.

    • AWS SageMaker Domain: This resource sets up a domain, which represents a shared multi-tenant environment where users can collaboratively work on Jupyter notebooks, experiments, etc.
    • AWS SageMaker User Profile: This resource defines user profiles within a domain that represent individual users, with their own authentication and workspace settings.

    Below is a Pulumi Python program that sets up a secure multi-user environment using AWS SageMaker. This program:

    1. Creates a SageMaker domain.
    2. Sets up user profiles within that domain.
    3. Configures network settings for security.

    Make sure to replace the placeholders (<subnet-id>, <your-role-arn>, etc.) with appropriate values specific to your AWS setup.

    import pulumi import pulumi_aws as aws # Initialize a new AWS provider instance if needed. # aws_provider = aws.Provider('myprovider', region='us-west-2') # Create a SageMaker Domain which will host the user profiles and provide an # endpoint where users can access Jupyter notebooks and other SageMaker # resources. sagemaker_domain = aws.sagemaker.Domain("my-domain", auth_mode="IAM", default_user_settings=aws.sagemaker.DomainDefaultUserSettingsArgs( execution_role="<your-role-arn>", security_groups=["<security-group-id>"], # Other settings like JupyterServerAppSettings, KernelGatewayAppSettings, etc. can be provided here. ), domain_name="my-secure-ml-domain", subnet_ids=["<subnet-id>"], # Subnets must be provided for VPC mode. vpc_id="<vpc-id>", tags={ "Environment": "Production", "Team": "MachineLearning", }, ) # Now let's create user profiles within our domain. Each user profile is a user # within your machine learning environment. user_profile = aws.sagemaker.UserProfile("user-profile", domain_id=sagemaker_domain.id, user_profile_name="user1", tags={ "Name": "user1", }, ) # Export the endpoint domain URL so that users can access SageMaker studio. pulumi.export("sagemaker_studio_url", sagemaker_domain.url)

    In this program, we’re setting up a domain with IAM authentication, which means AWS IAM roles will control access to the SageMaker environment. Each user profile created within this domain represents a unique development environment for a user.

    Each user profile and domain can be customized further based on the specific needs, such as setting up resource policies, enabling or restricting certain tools, etc.

    Remember to properly secure your network by setting up appropriate security groups and subnet ACLs which are referenced in this program. You should also provide the requisite IAM roles and policies that allow users to interact with the SageMaker environment and other AWS resources as needed.

    After deploying this Pulumi program with pulumi up, it will provide an endpoint URL, which users can use to access the Amazon SageMaker studio and begin working on machine learning projects in a secure, isolated, and collaborative environment.