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421 lines
11 KiB
Plaintext
421 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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"## 10. Register Model, Create Image and Deploy Service\n",
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"\n",
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"This example shows how to deploy a web service in step-by-step fashion:\n",
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"\n",
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" 1. Register model\n",
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" 2. Query versions of models and select one to deploy\n",
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" 3. Create Docker image\n",
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" 4. Query versions of images\n",
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" 5. Deploy the image as web service\n",
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" \n",
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"**IMPORTANT**:\n",
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" * This notebook requires you to first complete \"01.SDK-101-Train-and-Deploy-to-ACI.ipynb\" Notebook\n",
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" \n",
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"The 101 Notebook 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. "
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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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"Make sure you go through the [00. Installation and Configuration](00.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_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",
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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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"import sklearn\n",
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"\n",
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"library_version = \"sklearn\"+sklearn.__version__.replace(\".\",\"x\")\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\", 'version': library_version},\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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"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."
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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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"regression_models = Model.list(workspace=ws, tags=['area'])\n",
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"for m in regression_models:\n",
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" print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
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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 pick a specific model to deploy"
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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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"print(model.name, model.description, model.version, sep = '\\t')"
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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"
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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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"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."
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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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"%%writefile score.py\n",
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"import pickle\n",
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"import json\n",
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"import numpy\n",
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"from sklearn.externals import joblib\n",
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"from sklearn.linear_model import Ridge\n",
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"from azureml.core.model import Model\n",
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"\n",
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"def init():\n",
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" global model\n",
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" # note here \"sklearn_regression_model.pkl\" is the name of the model registered under\n",
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" # this is a different behavior than before when the code is run locally, even though the code is the same.\n",
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" model_path = Model.get_model_path('sklearn_regression_model.pkl')\n",
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" # deserialize the model file back into a sklearn model\n",
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" model = joblib.load(model_path)\n",
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"\n",
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"# note you can pass in multiple rows for scoring\n",
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"def run(raw_data):\n",
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" try:\n",
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" data = json.loads(raw_data)['data']\n",
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" data = numpy.array(data)\n",
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" result = model.predict(data)\n",
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" # you can return any datatype as long as it is JSON-serializable\n",
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" return result.tolist()\n",
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" except Exception as e:\n",
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" error = str(e)\n",
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" return error"
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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.conda_dependencies import CondaDependencies \n",
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"\n",
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"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
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"\n",
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"with open(\"myenv.yml\",\"w\") as f:\n",
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" f.write(myenv.serialize_to_string())"
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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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"Note that following command can take few minutes. \n",
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"\n",
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"You can add tags and descriptions to images. Also, an image can contain multiple models."
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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.image import Image, ContainerImage\n",
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"\n",
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"image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
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" execution_script=\"score.py\",\n",
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" conda_file=\"myenv.yml\",\n",
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" tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
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" description = \"Image with ridge regression model\")\n",
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"\n",
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"image = Image.create(name = \"myimage1\",\n",
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" # this is the model object \n",
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" models = [model],\n",
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" image_config = image_config, \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": "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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"image.wait_for_creation(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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"List images by tag and find out the detailed build log for debugging."
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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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"for i in Image.list(workspace = ws,tags = [\"area\"]):\n",
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" print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_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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"### Deploy image as web service 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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"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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"from azureml.core.webservice import AciWebservice\n",
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"\n",
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"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
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" memory_gb = 1, \n",
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" tags = {'area': \"diabetes\", 'type': \"regression\"}, \n",
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" description = 'Predict diabetes using regression model')"
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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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"from azureml.core.webservice import Webservice\n",
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"\n",
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"aci_service_name = 'my-aci-service-2'\n",
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"print(aci_service_name)\n",
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"aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
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" image = image,\n",
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" name = aci_service_name,\n",
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" workspace = ws)\n",
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"aci_service.wait_for_deployment(True)\n",
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"print(aci_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": "markdown",
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"metadata": {},
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"source": [
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"Call the web service with some dummy input data to get a prediction."
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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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"import json\n",
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"\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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"test_sample = bytes(test_sample,encoding = 'utf8')\n",
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"\n",
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"prediction = aci_service.run(input_data=test_sample)\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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"aci_service.delete()"
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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": "raymondl"
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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": "3.6.6"
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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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