MachineLearningNotebooks/how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Register Model, Create Image and Deploy Service\n",
        "\n",
        "This example shows how to deploy a web service in step-by-step fashion:\n",
        "\n",
        " 1. Register model\n",
        " 2. Query versions of models and select one to deploy\n",
        " 3. Create Docker image\n",
        " 4. Query versions of images\n",
        " 5. Deploy the image as web service\n",
        " \n",
        "**IMPORTANT**:\n",
        " * This notebook requires you to first complete [train-within-notebook](../../training/train-within-notebook/train-within-notebook.ipynb) example\n",
        " \n",
        "The train-within-notebook example taught you how to deploy a web service directly from model in one step. This Notebook shows a more advanced approach that gives you more control over model versions and Docker image versions.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Prerequisites\n",
        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Check core SDK version number\n",
        "import azureml.core\n",
        "\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Initialize Workspace\n",
        "\n",
        "Initialize a workspace object from persisted configuration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "create workspace"
        ]
      },
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Register Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can add tags and descriptions to your models. Note you need to have a `sklearn_linreg_model.pkl` file in the current directory. This file is generated by the 01 notebook. The below call registers that file as a model with the same name `sklearn_linreg_model.pkl` in the workspace.\n",
        "\n",
        "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "register model from file"
        ]
      },
      "outputs": [],
      "source": [
        "from azureml.core.model import Model\n",
        "import sklearn\n",
        "\n",
        "library_version = \"sklearn\"+sklearn.__version__.replace(\".\",\"x\")\n",
        "\n",
        "model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
        "                       model_name = \"sklearn_regression_model.pkl\",\n",
        "                       tags = {'area': \"diabetes\", 'type': \"regression\", 'version': library_version},\n",
        "                       description = \"Ridge regression model to predict diabetes\",\n",
        "                       workspace = ws)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can explore the registered models within your workspace and query by tag. Models are versioned. If you call the register_model command many times with same model name, you will get multiple versions of the model with increasing version numbers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "register model from file"
        ]
      },
      "outputs": [],
      "source": [
        "regression_models = Model.list(workspace=ws, tags=['area'])\n",
        "for m in regression_models:\n",
        "    print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can pick a specific model to deploy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(model.name, model.description, model.version, sep = '\\t')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create Docker Image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Show `score.py`. Note that the `sklearn_regression_model.pkl` in the `get_model_path` call is referring to a model named `sklearn_linreg_model.pkl` registered under the workspace. It is NOT referenceing the local file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%writefile score.py\n",
        "import os\n",
        "import pickle\n",
        "import json\n",
        "import numpy\n",
        "from sklearn.externals import joblib\n",
        "from sklearn.linear_model import Ridge\n",
        "\n",
        "def init():\n",
        "    global model\n",
        "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
        "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
        "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
        "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
        "    # deserialize the model file back into a sklearn model\n",
        "    model = joblib.load(model_path)\n",
        "\n",
        "# note you can pass in multiple rows for scoring\n",
        "def run(raw_data):\n",
        "    try:\n",
        "        data = json.loads(raw_data)['data']\n",
        "        data = numpy.array(data)\n",
        "        result = model.predict(data)\n",
        "        # you can return any datatype as long as it is JSON-serializable\n",
        "        return result.tolist()\n",
        "    except Exception as e:\n",
        "        error = str(e)\n",
        "        return error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.conda_dependencies import CondaDependencies \n",
        "\n",
        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
        "\n",
        "with open(\"myenv.yml\",\"w\") as f:\n",
        "    f.write(myenv.serialize_to_string())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Note that following command can take few minutes. \n",
        "\n",
        "You can add tags and descriptions to images. Also, an image can contain multiple models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "create image",
          "sample-image-create"
        ]
      },
      "outputs": [],
      "source": [
        "from azureml.core.image import Image, ContainerImage\n",
        "\n",
        "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
        "                                 execution_script=\"score.py\",\n",
        "                                 conda_file=\"myenv.yml\",\n",
        "                                 tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
        "                                 description = \"Image with ridge regression model\")\n",
        "\n",
        "image = Image.create(name = \"myimage1\",\n",
        "                     # this is the model object. note you can pass in 0-n models via this list-type parameter\n",
        "                     # in case you need to reference multiple models, or none at all, in your scoring script.\n",
        "                     models = [model],\n",
        "                     image_config = image_config, \n",
        "                     workspace = ws)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "create image"
        ]
      },
      "outputs": [],
      "source": [
        "image.wait_for_creation(show_output = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Use a custom Docker image\n",
        "\n",
        "You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n",
        "\n",
        "Only Supported for `ContainerImage`(from azureml.core.image) with `python` runtime.\n",
        "```python\n",
        "# use an image available in public Container Registry without authentication\n",
        "image_config.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n",
        "\n",
        "# or, use an image available in a private Container Registry\n",
        "image_config.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n",
        "image_config.base_image_registry.address = \"myregistry.azurecr.io\"\n",
        "image_config.base_image_registry.username = \"username\"\n",
        "image_config.base_image_registry.password = \"password\"\n",
        "\n",
        "# or, use an image built during training.\n",
        "image_config.base_image = run.properties[\"AzureML.DerivedImageName\"]\n",
        "```\n",
        "You can get the address of training image from the properties of a Run object. Only new runs submitted with azureml-sdk>=1.0.22 to AMLCompute targets will have the 'AzureML.DerivedImageName' property. Instructions on how to get a Run can be found in [manage-runs](../../training/manage-runs/manage-runs.ipynb). \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "List images by tag and find out the detailed build log for debugging."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "create image"
        ]
      },
      "outputs": [],
      "source": [
        "for i in Image.list(workspace = ws,tags = [\"area\"]):\n",
        "    print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Deploy image as web service on Azure Container Instance\n",
        "\n",
        "Note that the service creation can take few minutes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "deploy service",
          "aci",
          "sample-aciwebservice-deploy-config"
        ]
      },
      "outputs": [],
      "source": [
        "from azureml.core.webservice import AciWebservice\n",
        "\n",
        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
        "                                               memory_gb = 1, \n",
        "                                               tags = {'area': \"diabetes\", 'type': \"regression\"}, \n",
        "                                               description = 'Predict diabetes using regression model')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "deploy service",
          "aci",
          "sample-aciwebservice-deploy-from-image"
        ]
      },
      "outputs": [],
      "source": [
        "from azureml.core.webservice import Webservice\n",
        "\n",
        "aci_service_name = 'my-aci-service-2'\n",
        "print(aci_service_name)\n",
        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
        "                                           image = image,\n",
        "                                           name = aci_service_name,\n",
        "                                           workspace = ws)\n",
        "aci_service.wait_for_deployment(True)\n",
        "print(aci_service.state)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Test web service"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Call the web service with some dummy input data to get a prediction."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "deploy service",
          "aci"
        ]
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "test_sample = json.dumps({'data': [\n",
        "    [1,2,3,4,5,6,7,8,9,10], \n",
        "    [10,9,8,7,6,5,4,3,2,1]\n",
        "]})\n",
        "test_sample = bytes(test_sample,encoding = 'utf8')\n",
        "\n",
        "prediction = aci_service.run(input_data=test_sample)\n",
        "print(prediction)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Delete ACI to clean up"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "deploy service",
          "aci"
        ]
      },
      "outputs": [],
      "source": [
        "aci_service.delete()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "aashishb"
      }
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