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mlflow integration preview
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rastala
322
how-to-use-azureml/using-mlflow/deploy-model/deploy-model.ipynb
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322
how-to-use-azureml/using-mlflow/deploy-model/deploy-model.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Copyright (c) Microsoft Corporation. All rights reserved.\n",
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"\n",
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"Licensed under the MIT License."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Deploy Model as Azure Machine Learning Web Service using MLflow\n",
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"\n",
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"This example shows you how to use mlflow together with Azure Machine Learning services for deploying a model as a web service. You'll learn how to:\n",
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"\n",
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" 1. Retrieve a previously trained scikit-learn model\n",
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" 2. Create a Docker image from the model\n",
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" 3. Deploy the model as a web service on Azure Container Instance\n",
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" 4. Make a scoring request against the web service.\n",
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"\n",
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"## Prerequisites and Set-up\n",
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"\n",
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"This notebook requires you to first complete the [Use MLflow with Azure Machine Learning for Local Training Run](../train-local/train-local.ipnyb) or [Use MLflow with Azure Machine Learning for Remote Training Run](../train-remote/train-remote.ipnyb) notebook, so as to have an experiment run with uploaded model in your Azure Machine Learning Workspace.\n",
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"\n",
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"Also install following packages if you haven't already\n",
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"\n",
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"```\n",
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"pip install azureml-mlflow pandas\n",
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"```\n",
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"\n",
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"Then, import necessary packages:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import mlflow\n",
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"import azureml.mlflow\n",
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"import azureml.core\n",
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"from azureml.core import Workspace\n",
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"\n",
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"# Check core SDK version number\n",
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"print(\"SDK version:\", azureml.core.VERSION)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Connect to workspace and set MLflow tracking URI\n",
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"\n",
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"Setting the tracking URI is required for retrieving the model and creating an image using the MLflow APIs."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ws = Workspace.from_config()\n",
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"\n",
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"mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Retrieve model from previous run\n",
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"\n",
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"Let's retrieve the experiment from training notebook, and list the runs within that experiment."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"experiment_name = \"experiment-with-mlflow\"\n",
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"exp = ws.experiments[experiment_name]\n",
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"\n",
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"runs = list(exp.get_runs())\n",
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"runs"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Then, let's select the most recent training run and find its ID. You also need to specify the path in run history where the model was saved. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"runid = runs[0].id\n",
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"model_save_path = \"model\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Create Docker image\n",
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"\n",
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"To create a Docker image with Azure Machine Learning for Model Management, use ```mlflow.azureml.build_image``` method. Specify the model path, your workspace, run ID and other parameters.\n",
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"\n",
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"MLflow automatically recognizes the model framework as scikit-learn, and creates the scoring logic and includes library dependencies for you.\n",
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"\n",
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"Note that the image creation can take several minutes."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import mlflow.azureml\n",
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"\n",
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"azure_image, azure_model = mlflow.azureml.build_image(model_uri=\"runs:/{}/{}\".format(runid, model_save_path),\n",
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" workspace=ws,\n",
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" model_name='diabetes-sklearn-model',\n",
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" image_name='diabetes-sklearn-image',\n",
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" synchronous=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Deploy web service\n",
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"\n",
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"Let's use Azure Machine Learning SDK to deploy the image as a web service. \n",
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"\n",
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"First, specify the deployment configuration. Azure Container Instance is a suitable choice for a quick dev-test deployment, while Azure Kubernetes Service is suitable for scalable production deployments.\n",
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"\n",
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"Then, deploy the image using Azure Machine Learning SDK's ```deploy_from_image``` method.\n",
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"\n",
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"Note that the deployment can take several minutes."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from azureml.core.webservice import AciWebservice, Webservice\n",
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"\n",
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"\n",
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"aci_config = AciWebservice.deploy_configuration(cpu_cores=1, \n",
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" memory_gb=1, \n",
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" tags={\"method\" : \"sklearn\"}, \n",
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" description='Diabetes model',\n",
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" location='eastus2')\n",
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"\n",
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"\n",
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"# Deploy the image to Azure Container Instances (ACI) for real-time serving\n",
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"webservice = Webservice.deploy_from_image(\n",
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" image=azure_image, workspace=ws, name=\"diabetes-model-1\", deployment_config=aci_config)\n",
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"\n",
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"\n",
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"webservice.wait_for_deployment(show_output=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Make a scoring request\n",
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"\n",
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"Let's take the first few rows of test data and score them using the web service"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"test_rows = [\n",
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" [0.01991321, 0.05068012, 0.10480869, 0.07007254, -0.03596778,\n",
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" -0.0266789 , -0.02499266, -0.00259226, 0.00371174, 0.04034337],\n",
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" [-0.01277963, -0.04464164, 0.06061839, 0.05285819, 0.04796534,\n",
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" 0.02937467, -0.01762938, 0.03430886, 0.0702113 , 0.00720652],\n",
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" [ 0.03807591, 0.05068012, 0.00888341, 0.04252958, -0.04284755,\n",
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" -0.02104223, -0.03971921, -0.00259226, -0.01811827, 0.00720652]]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"MLflow-based web service for scikit-learn model requires the data to be converted to Pandas DataFrame, and then serialized as JSON. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import pandas as pd\n",
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"\n",
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"test_rows_as_json = pd.DataFrame(test_rows).to_json(orient=\"split\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let's pass the conveted and serialized data to web service to get the predictions."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"predictions = webservice.run(test_rows_as_json)\n",
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"\n",
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"print(predictions)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can use the web service's scoring URI to make a raw HTTP request"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"webservice.scoring_uri"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can diagnose the web service using ```get_logs``` method."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"webservice.get_logs()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Next Steps\n",
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"\n",
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"Learn about [model management and inferencing in Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-model-management-and-deployment)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"authors": [
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{
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"name": "rastala"
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}
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],
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"kernelspec": {
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"display_name": "Python 3.6",
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"language": "python",
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"name": "python36"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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