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343 lines
11 KiB
Plaintext
343 lines
11 KiB
Plaintext
{
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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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""
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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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"# Register Model and deploy as Webservice\n",
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"\n",
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"This example shows how to deploy a Webservice in step-by-step fashion:\n",
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"\n",
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" 1. Register Model\n",
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" 2. Deploy Model as Webservice"
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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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"## Prerequisites\n",
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"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."
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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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"# Check core SDK version number\n",
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"import azureml.core\n",
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"\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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"## Initialize Workspace\n",
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"\n",
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"Initialize a workspace object from persisted configuration."
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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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"tags": [
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"create workspace"
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]
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},
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"outputs": [],
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"source": [
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"from azureml.core import Workspace\n",
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"\n",
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"ws = Workspace.from_config()\n",
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"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
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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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"### Register 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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"You can add tags and descriptions to your Models. Note you need to have a `sklearn_regression_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_regression_model.pkl` in the workspace.\n",
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"\n",
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"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."
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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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"tags": [
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"register model from file"
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]
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},
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"outputs": [],
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"source": [
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"from azureml.core.model import Model\n",
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"\n",
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"model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
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" model_name=\"sklearn_regression_model.pkl\",\n",
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" tags={'area': \"diabetes\", 'type': \"regression\"},\n",
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" description=\"Ridge regression model to predict diabetes\",\n",
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" workspace=ws)"
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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 Environment"
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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 now create and/or use an Environment object when deploying a Webservice. The Environment can have been previously registered with your Workspace, or it will be registered with it as a part of the Webservice deployment. Only Environments that were created using azureml-defaults version 1.0.48 or later will work with this new handling however.\n",
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"\n",
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"More information can be found in our [using environments notebook](../training/using-environments/using-environments.ipynb)."
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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 import Environment\n",
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"\n",
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"env = Environment.from_conda_specification(name='deploytocloudenv', file_path='myenv.yml')\n",
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"\n",
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"# This is optional at this point\n",
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"# env.register(workspace=ws)"
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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 Inference Configuration\n",
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"\n",
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"There is now support for a source directory, you can upload an entire folder from your local machine as dependencies for the Webservice.\n",
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"Note: in that case, your entry_script, conda_file, and extra_docker_file_steps paths are relative paths to the source_directory path.\n",
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"\n",
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"Sample code for using a source directory:\n",
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"\n",
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"```python\n",
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"inference_config = InferenceConfig(source_directory=\"C:/abc\",\n",
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" runtime= \"python\", \n",
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" entry_script=\"x/y/score.py\",\n",
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" conda_file=\"env/myenv.yml\", \n",
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" extra_docker_file_steps=\"helloworld.txt\")\n",
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"```\n",
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"\n",
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" - source_directory = holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n",
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" - runtime = Which runtime to use for the image. Current supported runtimes are 'spark-py' and 'python\n",
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" - entry_script = contains logic specific to initializing your model and running predictions\n",
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" - conda_file = manages conda and python package dependencies.\n",
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" - extra_docker_file_steps = optional: any extra steps you want to inject into docker file"
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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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"tags": [
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"create image"
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]
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},
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"outputs": [],
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"source": [
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"from azureml.core.model import InferenceConfig\n",
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"\n",
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"inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)"
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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 Webservice on Azure Container Instance\n",
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"\n",
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"Note that the service creation can take few 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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"tags": [
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"azuremlexception-remarks-sample"
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]
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},
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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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"from azureml.exceptions import WebserviceException\n",
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"\n",
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"deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
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"aci_service_name = 'aciservice1'\n",
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"\n",
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"try:\n",
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" # if you want to get existing service below is the command\n",
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" # since aci name needs to be unique in subscription deleting existing aci if any\n",
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" # we use aci_service_name to create azure aci\n",
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" service = Webservice(ws, name=aci_service_name)\n",
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" if service:\n",
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" service.delete()\n",
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"except WebserviceException as e:\n",
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" print()\n",
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"\n",
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"service = Model.deploy(ws, aci_service_name, [model], inference_config, deployment_config)\n",
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"\n",
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"service.wait_for_deployment(True)\n",
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"print(service.state)"
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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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"#### Test 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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"import json\n",
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"test_sample = json.dumps({'data': [\n",
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" [1,2,3,4,5,6,7,8,9,10], \n",
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" [10,9,8,7,6,5,4,3,2,1]\n",
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"]})\n",
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"\n",
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"test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
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"prediction = service.run(input_data=test_sample_encoded)\n",
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"print(prediction)"
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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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"#### Delete ACI to clean up"
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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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"tags": [
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"deploy service",
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"aci"
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]
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},
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"outputs": [],
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"source": [
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"service.delete()"
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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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"### Model Profiling\n",
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"\n",
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"You can also take advantage of the profiling feature to estimate CPU and memory requirements for models.\n",
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"\n",
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"```python\n",
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"profile = Model.profile(ws, \"profilename\", [model], inference_config, test_sample)\n",
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"profile.wait_for_profiling(True)\n",
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"profiling_results = profile.get_results()\n",
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"print(profiling_results)\n",
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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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"### Model Packaging\n",
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"\n",
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"If you want to build a Docker image that encapsulates your model and its dependencies, you can use the model packaging option. The output image will be pushed to your workspace's ACR.\n",
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"\n",
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"You must include an Environment object in your inference configuration to use `Model.package()`.\n",
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"\n",
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"```python\n",
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"package = Model.package(ws, [model], inference_config)\n",
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"package.wait_for_creation(show_output=True) # Or show_output=False to hide the Docker build logs.\n",
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"package.pull()\n",
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"```\n",
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"\n",
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"Instead of a fully-built image, you can also generate a Dockerfile and download all the assets needed to build an image on top of your Environment.\n",
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"\n",
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"```python\n",
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"package = Model.package(ws, [model], inference_config, generate_dockerfile=True)\n",
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"package.wait_for_creation(show_output=True)\n",
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"package.save(\"./local_context_dir\")\n",
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"```"
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]
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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": "aashishb"
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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": "3.7.0"
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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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} |