sdk/python/endpoints/online/mlflow/online-endpoints-deploy-mlflow-model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Deploy MLflow model to online endpoints\n",
    "Learn how to deploy your [MLflow](https://www.mlflow.org/) model to an [online endpoint](https://docs.microsoft.com/azure/machine-learning/concept-endpoints). When you deploy your MLflow model to an online endpoint, it's a no-code-deployment. It doesn't require scoring script and environment."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Requirements - In order to benefit from this tutorial, you will need:\n",
    "- This sample notebook assumes you're using online endpoints; for more information, see [What are Azure Machine Learning endpoints?](https://docs.microsoft.com/azure/machine-learning/concept-endpoints).\n",
    "- An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F)\n",
    "- An Azure ML workspace with computer cluster - [Configure workspace](../../jobs/configuration.ipynb)\n",
    "- Installed Azure Machine Learning Python SDK v2 - [install instructions](../../README.md) - check the getting started section"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 1. Connect to Azure Machine Learning Workspace\n",
    "The [workspace](https://docs.microsoft.com/azure/machine-learning/concept-workspace) is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. In this section we will connect to the workspace in which the job will be run."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 1.1 Import the required libraries"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "# import required libraries\n",
    "from azure.ai.ml import MLClient\n",
    "from azure.ai.ml.entities import (\n",
    "    ManagedOnlineEndpoint,\n",
    "    ManagedOnlineDeployment,\n",
    "    Model,\n",
    "    Environment,\n",
    "    CodeConfiguration,\n",
    ")\n",
    "from azure.identity import DefaultAzureCredential\n",
    "from azure.ai.ml.constants import AssetTypes"
   ],
   "outputs": [],
   "execution_count": null,
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 1.2 Configure workspace details and get a handle to the workspace\n",
    "\n",
    "To connect to a workspace, we need identifier parameters - a subscription, resource group and workspace name. We will use these details in the `MLClient` from `azure.ai.ml` to get a handle to the required Azure Machine Learning workspace. We use the default [default azure authentication](https://docs.microsoft.com/en-us/python/api/azure-identity/azure.identity.defaultazurecredential?view=azure-python) for this tutorial. Check the [configuration notebook](../../jobs/configuration.ipynb) for more details on how to configure credentials and connect to a workspace."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "# enter details of your AML workspace\n",
    "subscription_id = \"<SUBSCRIPTION_ID>\"\n",
    "resource_group = \"<RESOURCE_GROUP>\"\n",
    "workspace = \"<AML_WORKSPACE_NAME>\""
   ],
   "outputs": [],
   "execution_count": null,
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "# get a handle to the workspace\n",
    "ml_client = MLClient(\n",
    "    DefaultAzureCredential(), subscription_id, resource_group, workspace\n",
    ")"
   ],
   "outputs": [],
   "execution_count": null,
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 2. Create Online Endpoint\n",
    "\n",
    "Online endpoints are endpoints that are used for online (real-time) inferencing. Online endpoints contain deployments that are ready to receive data from clients and can send responses back in real time.\n",
    "\n",
    "To create an online endpoint we will use `ManagedOnlineEndpoint`. This class allows user to configure the following key aspects:\n",
    "\n",
    "- `name` - Name of the endpoint. Needs to be unique at the Azure region level\n",
    "- `auth_mode` - The authentication method for the endpoint. Key-based authentication and Azure ML token-based authentication are supported. Key-based authentication doesn't expire but Azure ML token-based authentication does. Possible values are `key` or `aml_token`.\n",
    "- `identity`- The managed identity configuration for accessing Azure resources for endpoint provisioning and inference.\n",
    "    - `type`- The type of managed identity. Azure Machine Learning supports `system_assigned` or `user_assigned identity`.\n",
    "    - `user_assigned_identities` - List (array) of fully qualified resource IDs of the user-assigned identities. This property is required is `identity.type` is user_assigned.\n",
    "- `description`- Description of the endpoint."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 2.1 Configure the endpoint"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "# Creating a unique endpoint name with current datetime to avoid conflicts\n",
    "import datetime\n",
    "\n",
    "online_endpoint_name = \"endpoint-\" + datetime.datetime.now().strftime(\"%m%d%H%M%f\")\n",
    "\n",
    "# create an online endpoint\n",
    "endpoint = ManagedOnlineEndpoint(\n",
    "    name=online_endpoint_name,\n",
    "    description=\"this is a sample online endpoint for mlflow model\",\n",
    "    auth_mode=\"key\",\n",
    "    tags={\"foo\": \"bar\"},\n",
    ")"
   ],
   "outputs": [],
   "execution_count": null,
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 2.2 Create the endpoint\n",
    "Using the `MLClient` created earlier, we will now create the Endpoint in the workspace. This command will start the endpoint creation and return a confirmation response while the endpoint creation continues."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "ml_client.begin_create_or_update(endpoint).result()"
   ],
   "outputs": [],
   "execution_count": null,
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 3. Create a blue deployment\n",
    "\n",
    "A deployment is a set of resources required for hosting the model that does the actual inferencing. We will create a deployment for our endpoint using the `ManagedOnlineDeployment` class. This class allows user to configure the following key aspects.\n",
    "\n",
    "- `name` - Name of the deployment.\n",
    "- `endpoint_name` - Name of the endpoint to create the deployment under.\n",
    "- `model` - The model to use for the deployment. This value can be either a reference to an existing versioned model in the workspace or an inline model specification.\n",
    "- `instance_type` - The VM size to use for the deployment. For the list of supported sizes, see [Managed online endpoints SKU list](https://docs.microsoft.com/azure/machine-learning/reference-managed-online-endpoints-vm-sku-list).\n",
    "- `instance_count` - The number of instances to use for the deployment"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### No code deployment\n",
    "For MLflow no-code-deployment (NCD) to work, setting `type` to `MLFLOW` is mandatory. \n",
    "When you deploy a MLflow model to managed online endpoint, scoring script and environment is generated for you.\n",
    "\n",
    "Azure ML models consist of the binary file(s) that represent a machine learning model and any corresponding metadata. Models can be created from a local file or directory. The created model will be tracked in the workspace under the specified name and version.\n",
    "\n",
    "The Model class can be used to create a model. It accepts the following parameters:\n",
    "\n",
    "- `name` - Name of the model.\n",
    "- `version` - Version of the model. If omitted, Azure ML will autogenerate a version.\n",
    "- `path` - Local path to the model file(s). This can point to either a file or a directory.\n",
    "- `type` - Storage format of the model. Applicable for no-code deployment scenarios. Allowed values are `CUSTOM`, `MLFLOW` and `TRITON`\n",
    "- `description` - Description of the model."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 3.1 Configure the deployment"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "# create a blue deployment\n",
    "model = Model(\n",
    "    path=\"sklearn-diabetes/model\",\n",
    "    type=AssetTypes.MLFLOW_MODEL,\n",
    "    description=\"my sample mlflow model\",\n",
    ")\n",
    "\n",
    "blue_deployment = ManagedOnlineDeployment(\n",
    "    name=\"blue\",\n",
    "    endpoint_name=online_endpoint_name,\n",
    "    model=model,\n",
    "    instance_type=\"Standard_F4s_v2\",\n",
    "    instance_count=1,\n",
    ")"
   ],
   "outputs": [],
   "execution_count": null,
   "metadata": {
    "name": "blue_deployment"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 3.2 Create the deployment\n",
    "\n",
    "Using the `MLClient` created earlier, we will now create the deployment in the workspace. This command will start the deployment creation and return a confirmation response while the deployment creation continues."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "ml_client.online_deployments.begin_create_or_update(blue_deployment).result()"
   ],
   "outputs": [],
   "execution_count": null,
   "metadata": {
    "name": "ml_client"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# blue deployment takes 100 traffic\n",
    "endpoint.traffic = {\"blue\": 100}\n",
    "ml_client.begin_create_or_update(endpoint).result()"
   ],
   "outputs": [],
   "execution_count": null,
   "metadata": {
    "name": "endpoint.traffic"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 4. Test the deployment\n",
    "\n",
    "Using the `MLClient` created earlier, we will get a handle to the endpoint. The endpoint can be invoked using the invoke command with the following parameters:\n",
    "\n",
    "- `endpoint_name` - Name of the endpoint\n",
    "- `request_file` - File with request data\n",
    "- `deployment_name` - Name of the specific deployment to test in an endpoint\n",
    "\n",
    "We will send a sample request using a [sample-request-lightgbm.json](sample-request-lightgbm.json) file."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "# test the blue deployment with some sample data\n",
    "ml_client.online_endpoints.invoke(\n",
    "    endpoint_name=online_endpoint_name,\n",
    "    deployment_name=\"blue\",\n",
    "    request_file=\"sample-request-sklearn.json\",\n",
    ")"
   ],
   "outputs": [],
   "execution_count": null,
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 5. Get endpoint details"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "# Get the details for online endpoint\n",
    "endpoint = ml_client.online_endpoints.get(name=online_endpoint_name)\n",
    "\n",
    "# existing traffic details\n",
    "print(endpoint.traffic)\n",
    "\n",
    "# Get the scoring URI\n",
    "print(endpoint.scoring_uri)"
   ],
   "outputs": [],
   "execution_count": null,
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 6. Delete the deployment and endopoint"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "ml_client.online_endpoints.begin_delete(name=online_endpoint_name)"
   ],
   "outputs": [],
   "execution_count": null,
   "metadata": {}
  }
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