1. Single Sign-On for Machine Learning Platforms with Auth0

    Python

    Single Sign-On (SSO) is an authentication process that allows a user to access multiple applications with one set of login credentials. To set up SSO for Machine Learning platforms using Auth0 with Pulumi, we would typically follow these steps:

    1. Define an Auth0 Tenant: A tenant represents a single instance of Auth0 where your users, applications, and settings reside.

    2. Create an Auth0 Application: An application in Auth0 represents the application (in this case, your Machine Learning platform) that will use Auth0 for authentication.

    3. Set up SSO: Enable SSO in your Auth0 application settings to allow users to log in once and access all permitted applications.

    4. Configure User Authentication: Define how users will authenticate (e.g., username and password, social logins, etc.).

    5. Define User Roles and Permissions: Depending on the complexity of your setup, you may need to define specific roles and permissions within Auth0.

    Below is a program written in Python using Pulumi to set up single sign-on for a machine learning platform using Auth0. This program assumes you have already set up Pulumi and are using the Auth0 provider.

    import pulumi import pulumi_auth0 as auth0 # Step 1: Create an Auth0 Tenant (this is generally pre-existing and might not need to be defined in the Pulumi program) tenant = auth0.Tenant("machine_learning_tenant", default_directory="Username-Password-Authentication", # This defines the default connection you have set up in Auth0 friendly_name="Machine Learning Platform", picture_url="<URL-to-tenant-icon>", # Replace with your tenant's icon support_email="support@machinelearningplatform.com", support_url="https://support.machinelearningplatform.com", ) # Step 2: Create an Auth0 Application for your Machine Learning platform ml_app = auth0.Client("machine_learning_app", name="Machine Learning Platform", description="This application uses Auth0 for secure SSO authentication.", app_type="regular_web", # Assuming it's a standard web app; change as needed for your type of app grant_types=["authorization_code", "refresh_token"], # The desired grant types callbacks=["https://yourmlplatform.com/callback"], # Replace with your actual callback URL allowed_origins=["https://yourmlplatform.com"], # Replace with the allowed origins for CORS web_origins=["https://yourmlplatform.com"], # The web origins for the application sso=True, # Enable SSO ) # Step 3: Enable SSO and define settings - For this step, most configurations are done through the Auth0 dashboard # The sso property is set to True when defining the Auth0 application as shown above. # Step 4: Configure user authentication - assuming the default directory is being used which is already set up for user/password authentication # Step 5: Define User Roles and Permissions # This would be specific to your setup, as roles and permissions are highly application-specific. # These would likely be defined in the Auth0 dashboard but can be managed via Pulumi if needed. # Output the details needed to configure your machine learning platform to use the Auth0 application for SSO pulumi.export("tenant", tenant.friendly_name) pulumi.export("machine_learning_app_client_id", ml_app.client_id) pulumi.export("machine_learning_app_client_secret", ml_app.client_secret) pulumi.export("machine_learning_app_sso", ml_app.sso)

    Understanding the Program

    • We create an Auth0 Tenant, which is representative of your logistical grouping within Auth0 containing settings, users, and applications.

    • Next, we configure an Auth0 Client, which in our case is the Machine Learning platform application that will be using SSO.

    • We enable SSO directly in the application creation step by setting sso=True.

    • We leave the user authentication method and the definition of user roles and permissions to be configured directly on the Auth0 dashboard because these are dependent on individual application requirements, which might be too complex to manage in this program. However, Auth0 does provide Pulumi resources to define these programmatically if needed.

    • Lastly, we export the application's client ID and client secret which you'll need to configure your machine learning platform to integrate with Auth0. The tenant and machine_learning_app_sso are exported to verify that the tenant is set up correctly, and SSO is enabled for the application.

    Remember to replace placeholder URL strings like <URL-to-tenant-icon> and https://yourmlplatform.com with actual values specific to your situation. The callback URL should match the one registered in your Auth0 application settings, and the origins should reflect where your application is hosted.

    After setting up your Auth0 application using this Pulumi program, you will need to follow Auth0's documentation to configure your machine learning platform to authenticate using the Auth0 SSO.


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