Exercise9.ipynb

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      "source": "# Exercise 9. Deploying Model as Web Service"
    },
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      "source": "## Deploy model as web service\nOnce you have your trained model, you can deploy the model on Azure. You can deploy your trained model as a web service on Azure Container Instances (ACI), Azure Kubernetes Service (AKS), IoT edge device, or field programmable gate arrays (FPGAs)\n\nACI is generally cheaper than AKS and can be set up in 4-6 lines of code. ACI is the perfect option for testing deployments. Later, when you're ready to use your models and web services for high-scale, production usage, you can deploy them to AKS.\n\n\nIn this tutorial, we will deploy the model as a web service in [Azure Container Instances](https://docs.microsoft.com/en-us/azure/container-instances/) (ACI). \n\n\nFor more information on deploying models using Azure ML, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-and-where)."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Create environment file\nThen, we will need to create an environment file (`myenv.yml`) that specifies all of the scoring script's package dependencies. This file is used to ensure that all of those dependencies are installed in the Docker image by AML. In this case, we need to specify `torch`, `torchvision`, `pillow`, and `azureml-sdk`."
    },
    {
      "metadata": {
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      "cell_type": "code",
      "source": "%%writefile myenv.yml\nname: myenv\nchannels:\n  - defaults\ndependencies:\n  - pip:\n    - torch\n    - torchvision\n    - pillow\n    - azureml-core",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Configure the container image\nNow configure the Docker image that you will use to build your ACI container."
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": false
      },
      "cell_type": "code",
      "source": "from azureml.core.image import ContainerImage\n\nimage_config = ContainerImage.image_configuration(execution_script='pytorch_score.py', \n                                                  runtime='python', \n                                                  conda_file='myenv.yml',\n                                                  description='Image with dog breed model')",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Configure the ACI container\nWe are almost ready to deploy. Create a deployment configuration file to specify the number of CPUs and gigabytes of RAM needed for your ACI container. While it depends on your model, the default of `1` core and `1` gigabyte of RAM is usually sufficient for many models."
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": false
      },
      "cell_type": "code",
      "source": "from azureml.core.webservice import AciWebservice\n\naciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n                                               memory_gb=1, \n                                               tags={'data': 'dog_breeds',  'method':'transfer learning', 'framework':'pytorch'},\n                                               description='Classify dog breeds using transfer learning with PyTorch')",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Deploy the registered model\nFinally, let's deploy a web service from our registered model. First, retrieve the model from your workspace."
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": false
      },
      "cell_type": "code",
      "source": "from azureml.core.model import Model\n\nmodel = ws.models['model']",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Then, deploy the web service using the ACI config and image config files created in the previous steps. We pass the `model` object in a list to the `models` parameter. If you would like to deploy more than one registered model, append the additional models to this list.\n\n** Please use a unique service name**"
    },
    {
      "metadata": {
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      },
      "cell_type": "code",
      "source": "%%time\nfrom azureml.core.webservice import Webservice\n\nservice_name = 'dog120'\nservice = Webservice.deploy_from_model(workspace=ws,\n                                       name=service_name,\n                                       models=[model],\n                                       image_config=image_config,\n                                       deployment_config=aciconfig,)\n\nservice.wait_for_deployment(show_output=True)\nprint(service.state)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "If your deployment fails for any reason and you need to redeploy, make sure to delete the service before you do so: `service.delete()`"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Tip: If something goes wrong with the deployment, the first thing to look at is the logs from the service by running the following command:**"
    },
    {
      "metadata": {
        "scrolled": false,
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      },
      "cell_type": "code",
      "source": "service.get_logs()\n",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Get the web service's HTTP endpoint, which accepts REST client calls. This endpoint can be shared with anyone who wants to test the web service or integrate it into an application."
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": false
      },
      "cell_type": "code",
      "source": "print(service.scoring_uri)",
      "execution_count": null,
      "outputs": []
    }
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