1. Token-Based Machine Learning API Access via Keycloak

    Python

    To set up token-based access for a machine learning API via Keycloak, we will approach this in two steps:

    1. Create a Keycloak Realm and Client: First, we need to set up a realm in Keycloak for our environment. A realm in Keycloak is like a space that is used to manage a set of users, credentials, roles, and groups. Then, we'll create a client within the realm that will be used to authenticate and generate tokens for API access.

    2. Configure Client and User: We'll then configure the client to allow access to the machine learning API and add a user that will request the access token. Keycloak supports various types of clients for different use cases; for a machine learning API, we typically use a bearer-only client.

    The setup below assumes the following:

    • You're familiar with basic Pythons syntax.
    • You have Pulumi and its Keycloak provider installed and configured.
    • You have a running instance of Keycloak where you can create realms and clients.

    Here is a Pulumi program written in Python to set up token-based machine learning API access via Keycloak:

    import pulumi import pulumi_keycloak as keycloak # Create a new Keycloak realm my_realm = keycloak.Realm("myRealm", realm="my-machine-learning-realm", enabled=True) # Create a new Keycloak client for machine learning API within the realm # Assuming the API is a confidential application where client secret is needed. ml_api_client = keycloak.openid.Client("mlApiClient", realm_id=my_realm.id, client_id="machine-learning-api", name="Machine Learning API", access_type="CONFIDENTIAL", standard_flow_enabled=False, # Typical for machine learning and service-to-service APIs implicit_flow_enabled=False, direct_access_grants_enabled=True, # Allows obtaining access token directly service_accounts_enabled=True, # We are using service account for the token-based access client_auth_method="client_secret", # Client authenticates with a client secret secret=pulumi.Output.secret("your-client-secret-here"), # Replace with an actual secret or preferably use Pulumi config secrets use_refresh_tokens=True, # Allows a refresh token to be used to obtain a new access token web_origins=["*"], # Define the origin for CORS redirect_uris=["http://localhost/callback"]) # Set up the URI for after authentication redirects # Assuming you want to create a keycloak user for the service account ml_api_user = keycloak.User("mlApiUser", realm_id=my_realm.id, username="machine_learn_user", enabled=True, email_verified=True) # After creating the client, you can use Keycloak's token endpoint to obtain tokens. # With the client's id and secret, one can authenticate and obtain an access token # which will be used to authorize access to the ML API. # The actual API endpoint depends on your Keycloak deployment. pulumi.export('machine_learning_api_client_id', ml_api_client.id)

    Explanation of the resources being used:

    • keycloak.Realm: This Pulumi Keycloak resource is used to create a new Keycloak realm which provides a separate namespace for your configuration. Realms manage a set of users, credentials, roles, and groups. A user belongs to and logs into a realm.

    • keycloak.openid.Client: This resource is used to create a client in the realm. A client in Keycloak terms is an entity that can request Keycloak to authenticate a user. In our case, it will authenticate the machine learning API. The configuration details like access_type, direct_access_grants_enabled, and others are specific to the client type and your requirements for the API.

    • keycloak.User: This represents a user in a Keycloak realm that you can create for testing or actual use. Remember not to hardcode secrets and use either configuration or environment variables where needed.

    After you run this Pulumi program, you'll have a Keycloak client set up ready to issue tokens to access your machine learning API. You can then fetch a token using an HTTP client by providing the client ID and client secret and send it as the Authorization header in the requests to your machine learning API.

    Lastly, to apply this configuration, run pulumi up. It will execute the script, which uses Pulumi's Keycloak provider to set up the realm, client, and user. Make sure to replace placeholders with actual values that match your setup (like client secret and redirect URIs).


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