Merge branch 'master'

.cloud/.azure/aci_deploy.json

@@ -19,6 +19,7 @@
     "delete_service_after_deployment": false,
     "profiling_enabled": false,
     "profiling_dataset": "<your-dataset-for-model-profiling>",
+    "skip_deployment": false,
     "tags": {"<your-webservice-tag-key>": "<your-webservice-tag-value>"},
     "properties": {"<your-webservice-property-key>": "<your-webservice-property-value>"},
     "description": "<your-webservice-description>",

.cloud/.azure/aks_deploy.json

@@ -21,6 +21,7 @@
     "delete_service_after_deployment": false,
     "profiling_enabled": false,
     "profiling_dataset": "<your-dataset-for-model-profiling>",
+    "skip_deployment": false,
     "tags": {"<your-webservice-tag-key>": "<your-webservice-tag-value>"},
     "properties": {"<your-webservice-property-key>": "<your-webservice-property-value>"},
     "description": "<your-webservice-description>",

.cloud/.azure/deploy.json

@@ -20,6 +20,8 @@
     "delete_service_after_deployment": false,
     "profiling_enabled": false,
     "profiling_dataset": "<your-dataset-for-model-profiling>",
+    "skip_deployment": false,
+    "create_image": "<'docker', 'function_blob', 'function_http' or 'function_service_bus_queue' >",
     "tags": {"<your-webservice-tag-key>": "<your-webservice-tag-value>"},
     "properties": {"<your-webservice-property-key>": "<your-webservice-property-value>"},
     "description": "<your-webservice-description>",

.cloud/.azure/docker.json

@@ -0,0 +1,13 @@
+{
+    "inference_source_directory": "<your-inference-source-directory>",
+    "inference_entry_script": "<your-inference-entry-script>",
+    "conda_file": "<your-conda-environment-file-path>",
+    "extra_docker_file_steps": "<your-extra-docker-steps-file-path>",
+    "runtime": "<'python' or 'spark-py'>",
+    "enable_gpu": false,
+    "cuda_version": "<your-cuda-version>",
+    "custom_base_image": "<your-custom-docker-base-image>",
+    "description": "<your-webservice-description>",
+    "skip_deployment": true,
+    "create_image": "<'docker', 'function_blob', 'function_http' or 'function_service_bus_queue' >"
+}

.github/workflows/python.yml

@@ -25,5 +25,5 @@ jobs:
       - name: Test
         id: python_test
         run: |
-          pip install pytest jsonschema azureml-sdk
+          pip install pytest jsonschema azureml-sdk azureml-contrib-functions
           pytest

Dockerfile

@@ -1,5 +1,5 @@
   
-FROM marvinbuss/aml-docker:1.7.0
+FROM marvinbuss/aml-docker:1.9.0
 
 LABEL maintainer="azure/gh_aml"
 

README.md

@@ -148,6 +148,8 @@ A sample file can be found in this repository in the folder `.cloud/.azure`. The
 | test_file_function_name |          | str   | `"main"` | Name of the function in your python script in your repository in which you define your own tests that you want to run against the webservice endpoint. The function gets the webservice object injected and allows you to run tests against the scoring uri. The GitHub Action fails, if your script fails. |
 | profiling_enabled       |          | bool | false | Whether or not to profile this model for an optimal combination of cpu and memory. To use this functionality, you also have to provide a model profile dataset (`profiling_dataset`). If the parameter is not specified, the Action will try to use the sample input dataset that the model was registered with. Please, note that profiling is a long running operation and can take up to 25 minutes depending on the size of the dataset. More details can be found [here](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb). |
 | profiling_dataset       |          | str   | null | Name of the dataset that should be used for model profiling. |
+| skip_deployment         |          | bool | false | Indicates whether the deployment to ACI or AKS should be skipped. This can be used in combination with `create_image` to only create a Docker image that can be used for further deployment. |
+| create_image            |          | str: `"docker"`, `"function_blob"`, `"function_http"` or `"function_service_bus_queue"`  | null | Indicates whether a Docker image should be created which can be used for further deployment. |
 
 Please visit [this website](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py) and [this website](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model(class)?view=azure-ml-py#deploy-workspace--name--models--inference-config-none--deployment-config-none--deployment-target-none--overwrite-false-) for more details.
 
@@ -196,8 +198,12 @@ Please visit [this website](https://docs.microsoft.com/en-us/python/api/azureml-
 
 | Output              | Description                     |
 | ------------------- | ------------------------------- |
-| service_scoring_uri | Scoring URI of the webservice that was created (only provided if delete_service_after_test is set to False). |
-| service_swagger_uri | Swagger Uri of the webservice that was created (only provided if delete_service_after_test is set to False). |
+| service_scoring_uri | Scoring URI of the webservice that was created (only provided if `delete_service_after_deployment` is set to False). |
+| service_swagger_uri | Swagger Uri of the webservice that was created (only provided if `delete_service_after_deployment` is set to False). |
+| acr_address         | The DNS name or IP address (e.g. myacr.azurecr.io) of the Azure Container Registry (ACR) (only provided if `create_image` is not None).  |
+| acr_username        | The username for ACR (only provided if `create_image` is not None). |
+| acr_password        | The password for ACR (only provided if `create_image` is not None). |
+| package_location    | Full URI of the docker image (e.g. myacr.azurecr.io/azureml/azureml_*) (only provided if `create_image` is not None). |
 | profiling_details   | Dictionary of details of the model profiling result. This will only be provided, if the model profiling method is used and successfully executed. |
 
 ### Environment variables

action.yml

@@ -17,9 +17,17 @@ inputs:
     default: "deploy.json"
 outputs:
   service_scoring_uri:
-    description: "Scoring URI of the webservice that was created (only provided if delete_service_after_test is set to False)"
+    description: "Scoring URI of the webservice that was created (only provided if delete_service_after_deployment is set to False)"
   service_swagger_uri:
-    description: "Swagger Uri of the webservice that was created (only provided if delete_service_after_test is set to False)"
+    description: "Swagger URI of the webservice that was created (only provided if delete_service_after_deployment is set to False)"
+  acr_address:
+    description: "The DNS name or IP address (e.g. myacr.azurecr.io) of the Azure Container Registry (ACR) (only provided if create_image is not None)"
+  acr_username:
+    description: "The username for ACR (only provided if create_image is not None)"
+  acr_password:
+    description: "The password for ACR (only provided if create_image is not None)"
+  package_location:
+    description: "Full URI of the docker image (e.g. myacr.azurecr.io/azureml/azureml_*) (only provided if create_image is not None)"
   profiling_details:
     description: "Dictionary of details of the model profiling result. This will only be provided, if the model profiling method is used and successfully executed."
 branding:

code/main.py

@@ -3,6 +3,7 @@ import sys
 import json
 import importlib
 
+from azureml.contrib.functions import package_http, package_blob, package_service_bus_queue
 from azureml.core import Workspace, Model, ContainerRegistry
 from azureml.core.compute import ComputeTarget, AksCompute
 from azureml.core.model import InferenceConfig
@@ -76,12 +77,21 @@ def main():
         input_name="PARAMETERS_FILE"
     )
 
+    # Define target cloud
+    if azure_credentials.get("resourceManagerEndpointUrl", "").startswith("https://management.usgovcloudapi.net"):
+        cloud = "AzureUSGovernment"
+    elif azure_credentials.get("resourceManagerEndpointUrl", "").startswith("https://management.chinacloudapi.cn"):
+        cloud = "AzureChinaCloud"
+    else:
+        cloud = "AzureCloud"
+
     # Loading Workspace
     print("::debug::Loading AML Workspace")
     sp_auth = ServicePrincipalAuthentication(
         tenant_id=azure_credentials.get("tenantId", ""),
         service_principal_id=azure_credentials.get("clientId", ""),
-        service_principal_password=azure_credentials.get("clientSecret", "")
+        service_principal_password=azure_credentials.get("clientSecret", ""),
+        cloud=cloud
     )
     config_file_path = os.environ.get("GITHUB_WORKSPACE", default=".cloud/.azure")
     config_file_name = "aml_arm_config.json"
@@ -104,18 +114,6 @@ def main():
         print(f"::error::Workspace authorizationfailed: {exception}")
         raise ProjectSystemException
 
-    # Loading deployment target
-    print("::debug::Loading deployment target")
-    try:
-        deployment_target = ComputeTarget(
-            workspace=ws,
-            name=parameters.get("deployment_compute_target", "")
-        )
-    except ComputeTargetException:
-        deployment_target = None
-    except TypeError:
-        deployment_target = None
-
     # Loading model
     print("::debug::Loading model")
     try:
@@ -147,7 +145,7 @@ def main():
             source_directory=parameters.get("inference_source_directory", "code/deploy/"),
             enable_gpu=parameters.get("enable_gpu", None),
             description=parameters.get("description", None),
-            base_image=parameters.get("base_image", None),
+            base_image=parameters.get("custom_base_image", None),
             base_image_registry=container_registry,
             cuda_version=parameters.get("cuda_version", None)
         )
@@ -158,192 +156,269 @@ def main():
         print(f"::debug::Failed to create InferenceConfig. Trying to create no code deployment: {exception}")
         inference_config = None
 
-    # Default service name
-    repository_name = os.environ.get("GITHUB_REPOSITORY").split("/")[-1]
-    branch_name = os.environ.get("GITHUB_REF").split("/")[-1]
-    default_service_name = f"{repository_name}-{branch_name}".lower().replace("_", "-")
-    service_name = parameters.get("name", default_service_name)[:32]
+    # Skip deployment if only Docker image should be created
+    if not parameters.get("skip_deployment", False):
+        # Default service name
+        repository_name = os.environ.get("GITHUB_REPOSITORY").split("/")[-1]
+        branch_name = os.environ.get("GITHUB_REF").split("/")[-1]
+        default_service_name = f"{repository_name}-{branch_name}".lower().replace("_", "-")
+        service_name = parameters.get("name", default_service_name)[:32]
 
-    # Loading resource configuration
-    print("::debug::Loading resource configuration")
-    model_resource_config = model.resource_configuration
-    cpu_cores = get_resource_config(
-        config=parameters.get("cpu_cores", None),
-        resource_config=model_resource_config,
-        config_name="cpu"
-    )
-    memory_gb = get_resource_config(
-        config=parameters.get("memory_gb", None),
-        resource_config=model_resource_config,
-        config_name="memory_in_gb"
-    )
-    gpu_cores = get_resource_config(
-        config=parameters.get("gpu_cores", None),
-        resource_config=model_resource_config,
-        config_name="gpu"
-    )
-
-    # Profiling model
-    print("::debug::Profiling model")
-    if parameters.get("profiling_enabled", False):
-        # Getting profiling dataset
-        profiling_dataset = get_dataset(
-            workspace=ws,
-            name=parameters.get("profiling_dataset", None)
+        # Loading run config
+        print("::debug::Loading run config")
+        model_resource_config = model.resource_configuration
+        cpu_cores = get_resource_config(
+            config=parameters.get("cpu_cores", None),
+            resource_config=model_resource_config,
+            config_name="cpu"
+        )
+        memory_gb = get_resource_config(
+            config=parameters.get("memory_gb", None),
+            resource_config=model_resource_config,
+            config_name="memory_in_gb"
+        )
+        gpu_cores = get_resource_config(
+            config=parameters.get("gpu_cores", None),
+            resource_config=model_resource_config,
+            config_name="gpu"
         )
-        if profiling_dataset is None:
-            profiling_dataset = model.sample_input_dataset
 
         # Profiling model
-        try:
-            model_profile = Model.profile(
+        print("::debug::Profiling model")
+        if parameters.get("profiling_enabled", False):
+            # Getting profiling dataset
+            profiling_dataset = get_dataset(
                 workspace=ws,
-                profile_name=f"{service_name}-profile"[:32],
+                name=parameters.get("profiling_dataset", None)
+            )
+            if profiling_dataset is None:
+                profiling_dataset = model.sample_input_dataset
+
+            # Profiling model
+            try:
+                model_profile = Model.profile(
+                    workspace=ws,
+                    profile_name=f"{service_name}-profile"[:32],
+                    models=[model],
+                    inference_config=inference_config,
+                    input_dataset=profiling_dataset
+                )
+                model_profile.wait_for_completion(show_output=True)
+
+                # Overwriting resource configuration
+                cpu_cores = model_profile.recommended_cpu
+                memory_gb = model_profile.recommended_memory
+
+                # Setting output
+                profiling_details = model_profile.get_details()
+                print(f"::set-output name=profiling_details::{profiling_details}")
+            except Exception as exception:
+                print(f"::warning::Failed to profile model. Skipping profiling and moving on to deployment: {exception}")
+
+        # Loading deployment target
+        print("::debug::Loading deployment target")
+        try:
+            deployment_target = ComputeTarget(
+                workspace=ws,
+                name=parameters.get("deployment_compute_target", "")
+            )
+        except ComputeTargetException:
+            deployment_target = None
+        except TypeError:
+            deployment_target = None
+
+        # Creating deployment config
+        print("::debug::Creating deployment config")
+        if type(deployment_target) is AksCompute:
+            deployment_config = AksWebservice.deploy_configuration(
+                autoscale_enabled=parameters.get("autoscale_enabled", None),
+                autoscale_min_replicas=parameters.get("autoscale_min_replicas", None),
+                autoscale_max_replicas=parameters.get("autoscale_max_replicas", None),
+                autoscale_refresh_seconds=parameters.get("autoscale_refresh_seconds", None),
+                autoscale_target_utilization=parameters.get("autoscale_target_utilization", None),
+                collect_model_data=parameters.get("model_data_collection_enabled", None),
+                auth_enabled=parameters.get("authentication_enabled", None),
+                cpu_cores=cpu_cores,
+                memory_gb=memory_gb,
+                enable_app_insights=parameters.get("app_insights_enabled", None),
+                scoring_timeout_ms=parameters.get("scoring_timeout_ms", None),
+                replica_max_concurrent_requests=parameters.get("replica_max_concurrent_requests", None),
+                max_request_wait_time=parameters.get("max_request_wait_time", None),
+                num_replicas=parameters.get("num_replicas", None),
+                primary_key=os.environ.get("PRIMARY_KEY", None),
+                secondary_key=os.environ.get("SECONDARY_KEY", None),
+                tags=parameters.get("tags", None),
+                properties=parameters.get("properties", None),
+                description=parameters.get("description", None),
+                gpu_cores=gpu_cores,
+                period_seconds=parameters.get("period_seconds", None),
+                initial_delay_seconds=parameters.get("initial_delay_seconds", None),
+                timeout_seconds=parameters.get("timeout_seconds", None),
+                success_threshold=parameters.get("success_threshold", None),
+                failure_threshold=parameters.get("failure_threshold", None),
+                namespace=parameters.get("namespace", None),
+                token_auth_enabled=parameters.get("token_auth_enabled", None)
+            )
+        else:
+            deployment_config = AciWebservice.deploy_configuration(
+                cpu_cores=cpu_cores,
+                memory_gb=memory_gb,
+                tags=parameters.get("tags", None),
+                properties=parameters.get("properties", None),
+                description=parameters.get("description", None),
+                location=parameters.get("location", None),
+                auth_enabled=parameters.get("authentication_enabled", None),
+                ssl_enabled=parameters.get("ssl_enabled", None),
+                enable_app_insights=parameters.get("app_insights_enabled", None),
+                ssl_cert_pem_file=parameters.get("ssl_cert_pem_file", None),
+                ssl_key_pem_file=parameters.get("ssl_key_pem_file", None),
+                ssl_cname=parameters.get("ssl_cname", None),
+                dns_name_label=parameters.get("dns_name_label", None),
+                primary_key=os.environ.get("PRIMARY_KEY", None),
+                secondary_key=os.environ.get("SECONDARY_KEY", None),
+                collect_model_data=parameters.get("model_data_collection_enabled", None),
+                cmk_vault_base_url=os.environ.get("CMK_VAULT_BASE_URL", None),
+                cmk_key_name=os.environ.get("CMK_KEY_NAME", None),
+                cmk_key_version=os.environ.get("CMK_KEY_VERSION", None)
+            )
+
+        # Deploying model
+        print("::debug::Deploying model")
+        try:
+            # Default service name
+            repository_name = os.environ.get("GITHUB_REPOSITORY").split("/")[-1]
+            branch_name = os.environ.get("GITHUB_REF").split("/")[-1]
+            default_service_name = f"{repository_name}-{branch_name}".lower().replace("_", "-")[:32]
+
+            service = Model.deploy(
+                workspace=ws,
+                name=service_name,
                 models=[model],
                 inference_config=inference_config,
-                input_dataset=profiling_dataset
+                deployment_config=deployment_config,
+                deployment_target=deployment_target,
+                overwrite=True
             )
-            model_profile.wait_for_completion(show_output=True)
+            service.wait_for_deployment(show_output=True)
+        except WebserviceException as exception:
+            print(f"::error::Model deployment failed with exception: {exception}")
+            service_logs = service.get_logs()
+            raise AMLDeploymentException(f"Model deployment failed logs: {service_logs} \nexception: {exception}")
 
-            # Overwriting resource configuration
-            cpu_cores = model_profile.recommended_cpu
-            memory_gb = model_profile.recommended_memory
+        # Checking status of service
+        print("::debug::Checking status of service")
+        if service.state != "Healthy":
+            service_logs = service.get_logs()
+            print(f"::error::Model deployment failed with state '{service.state}': {service_logs}")
+            raise AMLDeploymentException(f"Model deployment failed with state '{service.state}': {service_logs}")
 
-            # Setting output
-            profiling_details = model_profile.get_details()
-            print(f"::set-output name=profiling_details::{profiling_details}")
-        except Exception as exception:
-            print(f"::warning::Failed to profile model. Skipping profiling and moving on to deployment: {exception}")
+        if parameters.get("test_enabled", False):
+            # Testing service
+            print("::debug::Testing service")
+            root = os.environ.get("GITHUB_WORKSPACE", default=None)
+            test_file_path = parameters.get("test_file_path", "code/test/test.py")
+            test_file_function_name = parameters.get("test_file_function_name", "main")
 
-    # Creating deployment config
-    print("::debug::Creating deployment config")
-    if type(deployment_target) is AksCompute:
-        deployment_config = AksWebservice.deploy_configuration(
-            autoscale_enabled=parameters.get("autoscale_enabled", None),
-            autoscale_min_replicas=parameters.get("autoscale_min_replicas", None),
-            autoscale_max_replicas=parameters.get("autoscale_max_replicas", None),
-            autoscale_refresh_seconds=parameters.get("autoscale_refresh_seconds", None),
-            autoscale_target_utilization=parameters.get("autoscale_target_utilization", None),
-            collect_model_data=parameters.get("model_data_collection_enabled", None),
-            auth_enabled=parameters.get("authentication_enabled", None),
-            cpu_cores=cpu_cores,
-            memory_gb=memory_gb,
-            enable_app_insights=parameters.get("app_insights_enabled", None),
-            scoring_timeout_ms=parameters.get("scoring_timeout_ms", None),
-            replica_max_concurrent_requests=parameters.get("replica_max_concurrent_requests", None),
-            max_request_wait_time=parameters.get("max_request_wait_time", None),
-            num_replicas=parameters.get("num_replicas", None),
-            primary_key=os.environ.get("PRIMARY_KEY", None),
-            secondary_key=os.environ.get("SECONDARY_KEY", None),
-            tags=parameters.get("tags", None),
-            properties=parameters.get("properties", None),
-            description=parameters.get("description", None),
-            gpu_cores=gpu_cores,
-            period_seconds=parameters.get("period_seconds", None),
-            initial_delay_seconds=parameters.get("initial_delay_seconds", None),
-            timeout_seconds=parameters.get("timeout_seconds", None),
-            success_threshold=parameters.get("success_threshold", None),
-            failure_threshold=parameters.get("failure_threshold", None),
-            namespace=parameters.get("namespace", None),
-            token_auth_enabled=parameters.get("token_auth_enabled", None)
-        )
-    else:
-        deployment_config = AciWebservice.deploy_configuration(
-            cpu_cores=cpu_cores,
-            memory_gb=memory_gb,
-            tags=parameters.get("tags", None),
-            properties=parameters.get("properties", None),
-            description=parameters.get("description", None),
-            location=parameters.get("location", None),
-            auth_enabled=parameters.get("authentication_enabled", None),
-            ssl_enabled=parameters.get("ssl_enabled", None),
-            enable_app_insights=parameters.get("app_insights_enabled", None),
-            ssl_cert_pem_file=parameters.get("ssl_cert_pem_file", None),
-            ssl_key_pem_file=parameters.get("ssl_key_pem_file", None),
-            ssl_cname=parameters.get("ssl_cname", None),
-            dns_name_label=parameters.get("dns_name_label", None),
-            primary_key=os.environ.get("PRIMARY_KEY", None),
-            secondary_key=os.environ.get("SECONDARY_KEY", None),
-            collect_model_data=parameters.get("model_data_collection_enabled", None),
-            cmk_vault_base_url=os.environ.get("CMK_VAULT_BASE_URL", None),
-            cmk_key_name=os.environ.get("CMK_KEY_NAME", None),
-            cmk_key_version=os.environ.get("CMK_KEY_VERSION", None)
-        )
+            print("::debug::Adding root to system path")
+            sys.path.insert(1, f"{root}")
 
-    # Deploying model
-    print("::debug::Deploying model")
-    try:
-        service = Model.deploy(
-            workspace=ws,
-            name=service_name,
-            models=[model],
-            inference_config=inference_config,
-            deployment_config=deployment_config,
-            deployment_target=deployment_target,
-            overwrite=True
-        )
-        service.wait_for_deployment(show_output=True)
-    except WebserviceException as exception:
-        print(f"::error::Model deployment failed with exception: {exception}")
-        service_logs = service.get_logs()
-        raise AMLDeploymentException(f"Model deployment failedlogs: {service_logs} \nexception: {exception}")
+            print("::debug::Importing module")
+            test_file_path = f"{test_file_path}.py" if not test_file_path.endswith(".py") else test_file_path
+            try:
+                test_spec = importlib.util.spec_from_file_location(
+                    name="testmodule",
+                    location=test_file_path
+                )
+                test_module = importlib.util.module_from_spec(spec=test_spec)
+                test_spec.loader.exec_module(test_module)
+                test_function = getattr(test_module, test_file_function_name, None)
+            except ModuleNotFoundError as exception:
+                print(f"::error::Could not load python script in your repository which defines theweb service tests (Script: /{test_file_path}, Function: {test_file_function_name}()): {exception}")
+                raise AMLConfigurationException(f"Could not load python script in your repository which defines the web service tests (Script: /{test_file_path}, Function: {test_file_function_name}()): {exception}")
+            except FileNotFoundError as exception:
+                print(f"::error::Could not load python script or function in your repository which defines the web service tests (Script: /{test_file_path}, Function: {test_file_function_name}()): {exception}")
+                raise AMLConfigurationException(f"Could not load python script or function in your repository which defines the web service tests (Script: /{test_file_path}, Function: {test_file_function_name}()): {exception}")
+            except AttributeError as exception:
+                print(f"::error::Could not load python script or function in your repository which defines the web service tests (Script: /{test_file_path}, Function: {test_file_function_name}()): {exception}")
+                raise AMLConfigurationException(f"Could not load python script or function in your repository which defines the web service tests (Script: /{test_file_path}, Function: {test_file_function_name}()): {exception}")
 
-    # Checking status of service
-    print("::debug::Checking status of service")
-    if service.state != "Healthy":
-        service_logs = service.get_logs()
-        print(f"::error::Model deployment failed with state '{service.state}': {service_logs}")
-        raise AMLDeploymentException(f"Model deployment failed with state '{service.state}': {service_logs}")
+            # Load experiment config
+            print("::debug::Loading experiment config")
+            try:
+                test_function(service)
+            except TypeError as exception:
+                print(f"::error::Could not load experiment config from your module (Script: /{test_file_path}, Function: {test_file_function_name}()): {exception}")
+                raise AMLConfigurationException(f"Could not load experiment config from your module (Script: /{test_file_path}, Function: {test_file_function_name}()): {exception}")
+            except Exception as exception:
+                print(f"::error::The webservice tests did not complete successfully: {exception}")
+                raise AMLDeploymentException(f"The webservice tests did not complete successfully: {exception}")
 
-    if parameters.get("test_enabled", False):
-        # Testing service
-        print("::debug::Testing service")
-        root = os.environ.get("GITHUB_WORKSPACE", default=None)
-        test_file_path = parameters.get("test_file_path", "code/test/test.py")
-        test_file_function_name = parameters.get("test_file_function_name", "main")
+        # Deleting service if desired
+        if parameters.get("delete_service_after_deployment", False):
+            service.delete()
+        else:
+            # Creating outputs
+            print("::debug::Creating outputs")
+            print(f"::set-output name=service_scoring_uri::{service.scoring_uri}")
+            print(f"::set-output name=service_swagger_uri::{service.swagger_uri}")
 
-        print("::debug::Adding root to system path")
-        sys.path.insert(1, f"{root}")
-
-        print("::debug::Importing module")
-        test_file_path = f"{test_file_path}.py" if not test_file_path.endswith(".py") else test_file_path
+    # Creating Docker image
+    if parameters.get("create_image", None) is not None:
         try:
-            test_spec = importlib.util.spec_from_file_location(
-                name="testmodule",
-                location=test_file_path
-            )
-            test_module = importlib.util.module_from_spec(spec=test_spec)
-            test_spec.loader.exec_module(test_module)
-            test_function = getattr(test_module, test_file_function_name, None)
-        except ModuleNotFoundError as exception:
-            print(f"::error::Could not load python script in your repository which defines theweb service tests (Script: /{test_file_path}, Function: {test_file_function_name}()): {exception}")
-            raise AMLConfigurationException(f"Could not load python script in your repository which defines the web service tests (Script: /{test_file_path}, Function: {test_file_function_name}()): {exception}")
-        except FileNotFoundError as exception:
-            print(f"::error::Could not load python script or function in your repository which defines the web service tests (Script: /{test_file_path}, Function: {test_file_function_name}()): {exception}")
-            raise AMLConfigurationException(f"Could not load python script or function in your repository which defines the web service tests (Script: /{test_file_path}, Function: {test_file_function_name}()): {exception}")
-        except AttributeError as exception:
-            print(f"::error::Could not load python script or function in your repository which defines the web service tests (Script: /{test_file_path}, Function: {test_file_function_name}()): {exception}")
-            raise AMLConfigurationException(f"Could not load python script or function in your repository which defines the web service tests (Script: /{test_file_path}, Function: {test_file_function_name}()): {exception}")
+            # Packaging model
+            if parameters.get("create_image", None) == "docker":
+                package = Model.package(
+                    workspace=ws,
+                    models=[model],
+                    inference_config=inference_config,
+                    generate_dockerfile=False
+                )
+            if parameters.get("create_image", None) == "function_blob":
+                package = package_blob(
+                    workspace=ws,
+                    models=[model],
+                    inference_config=inference_config,
+                    generate_dockerfile=False,
+                    input_path=os.environ.get("FUNCTION_BLOB_INPUT"),
+                    output_path=os.environ.get("FUNCTION_BLOB_OUTPUT")
+                )
+            if parameters.get("create_image", None) == "function_http":
+                package = package_http(
+                    workspace=ws,
+                    models=[model],
+                    inference_config=inference_config,
+                    generate_dockerfile=False,
+                    auth_level=os.environ.get("FUNCTION_HTTP_AUTH_LEVEL")
+                )
+            if parameters.get("create_image", None) == "function_service_bus_queue":
+                package = package_service_bus_queue(
+                    workspace=ws,
+                    models=[model],
+                    inference_config=inference_config,
+                    generate_dockerfile=False,
+                    input_queue_name=os.environ.get("FUNCTION_SERVICE_BUS_QUEUE_INPUT"),
+                    output_queue_name=os.environ.get("FUNCTION_SERVICE_BUS_QUEUE_OUTPUT")
+                )
 
-        # Load experiment config
-        print("::debug::Loading experiment config")
-        try:
-            test_function(service)
-        except TypeError as exception:
-            print(f"::error::Could not load experiment config from your module (Script: /{test_file_path}, Function: {test_file_function_name}()): {exception}")
-            raise AMLConfigurationException(f"Could not load experiment config from your module (Script: /{test_file_path}, Function: {test_file_function_name}()): {exception}")
-        except Exception as exception:
-            print(f"::error::The webservice tests did not complete successfully: {exception}")
-            raise AMLDeploymentException(f"The webservice tests did not complete successfully: {exception}")
+            # Getting container registry details
+            acr = package.get_container_registry()
+            mask_parameter(parameter=acr.address)
+            mask_parameter(parameter=acr.username)
+            mask_parameter(parameter=acr.password)
 
-    # Deleting service if desired
-    if parameters.get("delete_service_after_deployment", False):
-        service.delete()
-    else:
-        # Create outputs
-        print("::debug::Creating outputs")
-        print(f"::set-output name=service_scoring_uri::{service.scoring_uri}")
-        print(f"::set-output name=service_swagger_uri::{service.swagger_uri}")
+            # Wait for completion and pull image
+            package.wait_for_creation(show_output=True)
+
+            # Creating additional outputs
+            print("::debug::Creating outputs")
+            print(f"::set-output name=acr_address::{acr.address}")
+            print(f"::set-output name=acr_username::{acr.username}")
+            print(f"::set-output name=acr_password::{acr.password}")
+            print(f"::set-output name=package_location::{package.location}")
+        except WebserviceException as exception:
+            print(f"::error::Image creation failed with exception: {exception}")
+            package_logs = package.get_logs()
+            raise AMLDeploymentException(f"Image creation failed with logs: {package_logs}")
     print("::debug::Successfully finished Azure Machine Learning Deploy Action")
 
 

code/schemas.py

@@ -121,6 +121,15 @@ parameters_schema = {
             "type": "boolean",
             "description": "Indicates whether the service gets deleted after the deployment completed successfully."
         },
+        "skip_deployment": {
+            "type": "boolean",
+            "description": "Indicates whether the deployment to ACI or AKS should be skipped. This can be used in combination with `create_image` to only create a Docker image that can be used for further deployment."
+        },
+        "create_image": {
+            "type": "string",
+            "description": "Indicates whether a Docker image should be created which can be used for further deployment.",
+            "pattern": "docker|function_blob|function_http|function_service_bus_queue"
+        },
         "tags": {
             "type": "object",
             "description": "Dictionary of key value tags to give this Webservice."