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refactoring to use CLI
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‎articles/machine-learning/service/how-to-deploy-app-service.md

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@@ -9,7 +9,7 @@ ms.topic: conceptual
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ms.author: aashishb
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author: aashishb
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ms.reviewer: larryfr
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ms.date: 07/01/2019
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ms.date: 08/27/2019
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---
@@ -35,6 +35,7 @@ For more information on features provided by Azure App Service, see the [App Ser
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## Prerequisites
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* An Azure Machine Learning service workspace. For more information, see the [Create a workspace](how-to-manage-workspace.md) article.
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* The [Azure CLI](https://docs.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest).
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* A trained machine learning model registered in your workspace. If you do not have a model, use the [Image classification tutorial: train model](tutorial-train-models-with-aml.md) to train and register one.
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> [!IMPORTANT]
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To create the Docker image that is deployed to Azure App Service, use [Model.package](https://docs.microsoft.com//python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#package-workspace--models--inference-config--generate-dockerfile-false-). The following code snippet demonstrates how to build a new image from the model and inference configuration:
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> [!NOTE]
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> The code snippet assumes that `model` contains a registered model, and that `inference_config` contains the configuration for the inference environment. For more information, see [Deploy models with the Azure Machine Learning service](how-to-deploy-and-where.md).
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```python
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from azureml.core import Model
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package = Model.package(ws, [model], inference_config)
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package.wait_for_creation(show_output=True)
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# Display the package location/ACR path
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print(package.location)
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```
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When `show_output=True`, the output of the Docker build process is shown. Once the process finishes, the image has been created in the Azure Container Registry for your workspace.
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When `show_output=True`, the output of the Docker build process is shown. Once the process finishes, the image has been created in the Azure Container Registry for your workspace. Once the image has been built, the location in your Azure Container Registry is displayed. The location returned is in the format `<acrinstance>.azurecr.io/package:<imagename>`. For example, `myml08024f78fd10.azurecr.io/package:20190827151241`.
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> [!IMPORTANT]
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> Save the location information, as it is used when deploying the image.
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## Deploy image as a web app
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1. From the [Azure portal](https://portal.azure.com), select your Azure Machine Learning workspace. From the __Overview__ section, use the __Registry__ link to access the Azure Container Registry for the workspace.
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1. Use the following command to get the login credentials for the Azure Container Registry that contains the image. Replace `<acrinstance>` with th e value returned previously from `package.location`:
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[![Screenshot of the overview for the workspace](media/how-to-deploy-app-service/workspace-overview.png)](media/how-to-deploy-app-service/workspace-overview-expanded.png)
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```azurecli-interactive
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az acr credential show --name <myacr>
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```
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2. From the Azure Container Registry, select __Repositories__, and then select the __image name__ that you want to deploy. For the version that you want to deploy, select the __...__ entry, and then __Deploy to web app__.
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The output of this command is similar to the following JSON document:
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[![Screenshot of deploying from ACR to a web app](media/how-to-deploy-app-service/deploy-to-web-app.png)](media/how-to-deploy-app-service/deploy-to-web-app-expanded.png)
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```json
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{
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"passwords": [
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{
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"name": "password",
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"value": "Iv0lRZQ9762LUJrFiffo3P4sWgk4q+nW"
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},
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{
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"name": "password2",
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"value": "=pKCxHatX96jeoYBWZLsPR6opszr==mg"
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}
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],
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"username": "myml08024f78fd10"
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}
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```
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3. To create the Web App, provide a site name, subscription, resource group, and select the App service plan/location. Finally, select __Create__.
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Save the value for __username__ and one of the __passwords__.
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![Screenshot of the new web app dialog](media/how-to-deploy-app-service/web-app-for-containers.png)
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1. If you do not already have a resource group or app service plan to deploy the service, the following commands demonstrate how to create both:
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## Use the Web App
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```azurecli-interactive
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az group create --name myresourcegroup --location "West Europe"
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az appservice plan create --name myplanname --resource-group myresourcegroup --sku B1 --is-linux
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```
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From the [Azure portal](https://portal.azure.com), select the Web App created in the previous step. From the __Overview__ section, copy the __URL__. This value is the __base URL__ of the service.
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In this example, a __Basic__ pricing tier (`--sku B1`) is used.
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[![Screenshot of the overview for the web app](media/how-to-deploy-app-service/web-app-overview.png)](media/how-to-deploy-app-service/web-app-overview-expanded.png)
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> [!IMPORTANT]
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> Images created by the Azure Machine Learning service use Linux, so you must use the `--is-linux` parameter.
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1. To create the web app, use the following command. Replace `<app-name>` with the name you want to use. Replace `<acrinstance>` and `<imagename>` with the values from returned `package.location` earlier:
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```azurecli-interactive
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az webapp create --resource-group myresourcegroup --plan myplanname --name <app-name> --deployment-container-image-name <acrinstance>.azurecr.io/package:<imagename>
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```
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This command returns information similar to the following JSON document:
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```json
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{
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"adminSiteName": null,
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"appServicePlanName": "myplanname",
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"geoRegion": "West Europe",
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"hostingEnvironmentProfile": null,
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"id": "/subscriptions/0000-0000/resourceGroups/myResourceGroup/providers/Microsoft.Web/serverfarms/myplanname",
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"kind": "linux",
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"location": "West Europe",
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"maximumNumberOfWorkers": 1,
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"name": "myplanname",
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< JSON data removed for brevity. >
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"targetWorkerSizeId": 0,
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"type": "Microsoft.Web/serverfarms",
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"workerTierName": null
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}
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```
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1. To provide the web app with the credentials needed to access the container registry, use the following command. Replace `<app-name>` with the name you want to use. Replace `<acrinstance>` and `<imagename>` with the values from returned `package.location` earlier. Replace `<username>` and `<password>` with the ACR login information retrieved earlier:
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```azurecli-interactive
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az webapp config container set --name <app-name> --resource-group myresourcegroup --docker-custom-image-name <acrinstance>.azurecr.io/package:<imagename> --docker-registry-server-url https://<acrinstance>.azurecr.io --docker-registry-server-user <username> --docker-registry-server-password <password>
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```
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This command returns information similar to the following JSON document:
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```json
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[
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{
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"name": "WEBSITES_ENABLE_APP_SERVICE_STORAGE",
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"slotSetting": false,
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"value": "false"
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},
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{
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"name": "DOCKER_REGISTRY_SERVER_URL",
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"slotSetting": false,
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"value": "https://myml08024f78fd10.azurecr.io"
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},
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{
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"name": "DOCKER_REGISTRY_SERVER_USERNAME",
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"slotSetting": false,
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"value": "myml08024f78fd10"
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},
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{
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"name": "DOCKER_REGISTRY_SERVER_PASSWORD",
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"slotSetting": false,
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"value": null
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},
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{
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"name": "DOCKER_CUSTOM_IMAGE_NAME",
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"value": "DOCKER|myml08024f78fd10.azurecr.io/package:20190827195524"
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}
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]
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```
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At this point, the web app begins loading the image.
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The web service that passes requests to the model is located at `{baseurl}/score`. For example, `https://mywebapp.azurewebsites.net/score`. The following Python code demonstrates how to submit data to the URL and display the response:
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> [!IMPORTANT]
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> It may take several minutes before the image has loaded. To monitor progress, use the following command:
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>
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> ```azurecli-interactive
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> az webapp log tail --name <app-name> --resource-group myresourcegroup
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> ```
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Once the image is deployed, you can find the hostname by using the following command:
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```azurecli-interactive
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az webapp show --name <app-name> --resource-group myresourcegroup
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```
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This command returns information similar to the following hostname - `<app-name>.azurewebsites.net`. Use this value as part of the __base url__ for the service.
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## Use the Web App
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The web service that passes requests to the model is located at `{baseurl}/score`. For example, `https://<app-name>.azurewebsites.net/score`. The following Python code demonstrates how to submit data to the URL and display the response:
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```python
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import requests
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scoring_uri = "https://mywebapp.azurewebsites.net/score"
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headers = {'Content-Type':'application/json'}
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print(headers)
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test_sample = json.dumps({'data': [
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[1,2,3,4,5,6,7,8,9,10],

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