def deploy_flow()

in util/deploy_moe.py [0:0]
101 lines of code
9 McCabe index (conditional complexity)

def deploy_flow(endpoint_name, deployment_name):

    # check if endpoint exists, create endpoint object if not
    try:
        endpoint = client.online_endpoints.get(endpoint_name)
    
    except Exception as e:
        endpoint = ManagedOnlineEndpoint(
            name=endpoint_name,
            properties={
              "enforce_access_to_default_secret_stores": "enabled" # if you want secret injection support
            },
            auth_mode="aad_token" # using aad auth instead of key-based auth
        )

    # Get the directory of the current script
    script_dir = os.path.dirname(os.path.abspath(__file__))
    print(f"Script directory: {script_dir}")

    # Define the path to the directory, appending the script directory to the relative path
    flow_path = os.path.abspath(os.path.join(script_dir, "../dist"))
    print(f"Flow path: {flow_path}")

    # Create dummy file in connections folder (promptflow issue #1274)
    connections_path = os.path.join(flow_path, "connections")
    os.makedirs(connections_path, exist_ok=True)
    dummy_file_path = os.path.join(connections_path, "dummy.txt")
    with open(dummy_file_path, 'w') as dummy_file:
        pass

    deployment = ManagedOnlineDeployment(
        name=deployment_name,
        endpoint_name=endpoint_name,
        model=Model(
            name="ragflow",
            path=flow_path,  # path to promptflow folder
            properties=[ # this enables the chat interface in the endpoint test tab
                ["azureml.promptflow.source_flow_id", "ragflow"],
                ["azureml.promptflow.mode", "chat"],
                ["azureml.promptflow.chat_input", "question"],
                ["azureml.promptflow.chat_output", "answer"]
            ]
        ),
        environment=Environment(
            build=BuildContext(
                path=flow_path,
            ),
            inference_config={
                "liveness_route": {
                    "path": "/health",
                    "port": 8080,
                },
                "readiness_route": {
                    "path": "/health",
                    "port": 8080,
                },
                "scoring_route":{
                    "path": "/score",
                    "port": 8080,
                },
            },
        ),
        # instance type comes with associated cost.
        # make sure you have quota for the specified instance type
        # See more details here: https://learn.microsoft.com/azure/machine-learning/reference-managed-online-endpoints-vm-sku-list
        instance_type="Standard_DS3_v2",
        instance_count=1,
        environment_variables={
            "PRT_CONFIG_OVERRIDE": f"deployment.subscription_id={client.subscription_id},deployment.resource_group={client.resource_group_name},deployment.workspace_name={client.workspace_name},deployment.endpoint_name={endpoint_name},deployment.deployment_name={deployment_name}",
            "AZURE_SUBSCRIPTION_ID": os.environ["AZURE_SUBSCRIPTION_ID"],
            "AZURE_RESOURCE_GROUP": os.environ["AZURE_RESOURCE_GROUP"],
            "AZUREAI_PROJECT_NAME": os.environ["AZUREAI_PROJECT_NAME"],
            "AZURE_OPENAI_ENDPOINT": azure_config.aoai_endpoint,
            "AZURE_OPENAI_API_VERSION": azure_config.aoai_api_version,
            "AZURE_SEARCH_ENDPOINT": azure_config.search_endpoint,
            "AZURE_OPENAI_CHAT_DEPLOYMENT": os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT"),
            "AZURE_OPENAI_EMBEDDING_MODEL": os.getenv("AZURE_OPENAI_EMBEDDING_MODEL"),
            "AZURE_OPENAI_EMBEDDING_DEPLOYMENT": os.getenv("AZURE_OPENAI_EMBEDDING_MODEL")  # using the same name for the deployment as the model for simplicity
        }
    )

    # 1. create endpoint
    endpoint = client.begin_create_or_update(endpoint).result() # result() means we wait on this to complete

    # 2. provide endpoint access to Azure Open AI resource
    create_role_assignment(
        scope=f"/subscriptions/{client.subscription_id}/resourceGroups/{azure_config.resource_group}/providers/Microsoft.CognitiveServices/accounts/{azure_config.aoai_account_name}",
        role_name="Cognitive Services OpenAI User",
        principal_id=endpoint.identity.principal_id
        )
    
    create_role_assignment(
        scope=f"/subscriptions/{client.subscription_id}/resourceGroups/{azure_config.resource_group}/providers/Microsoft.CognitiveServices/accounts/{azure_config.aoai_account_name}",
        role_name="Cognitive Services Contributor",
        principal_id=endpoint.identity.principal_id
        )

    create_role_assignment(
            scope=f"/subscriptions/{client.subscription_id}/resourceGroups/{azure_config.resource_group}",
            role_name="Contributor",
            principal_id=endpoint.identity.principal_id
        )


    # 3. provide endpoint access to Azure AI Search resource
    create_role_assignment(
        scope=f"/subscriptions/{client.subscription_id}/resourceGroups/{azure_config.resource_group}/providers/Microsoft.Search/searchServices/{azure_config.search_account_name}",
        role_name="Search Index Data Contributor",
        principal_id=endpoint.identity.principal_id
        )
    
    # 4. provide endpoint access to workspace
    create_role_assignment(
        scope=f"/subscriptions/{azure_config.subscription_id}/resourceGroups/{azure_config.resource_group}/providers/Microsoft.MachineLearningServices/workspaces/{azure_config.workspace_name}",
        role_name="Contributor",
        principal_id=endpoint.identity.principal_id
        )

    # 5. create deployment
    deployment = client.begin_create_or_update(deployment).result()

    # 6. update endpoint traffic for the deployment
    endpoint.traffic = {deployment_name: 100} # 100% of traffic
    endpoint = client.begin_create_or_update(endpoint).result()

    output_deployment_details(
        client=client,
        endpoint_name=endpoint_name,
        deployment_name=deployment_name
        )