Data-Science-For-Beginners/translations/zh/5-Data-Science-In-Cloud/19-Azure/notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# 云端数据科学：\"Azure ML SDK\" 方法\n",
    "\n",
    "## 介绍\n",
    "\n",
    "在本笔记本中，我们将学习如何使用 Azure ML SDK，通过 Azure ML 训练、部署和使用一个模型。\n",
    "\n",
    "前置条件：\n",
    "1. 您已创建一个 Azure ML 工作区。\n",
    "2. 您已将 [心力衰竭数据集](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data) 加载到 Azure ML 中。\n",
    "3. 您已将此笔记本上传到 Azure ML Studio。\n",
    "\n",
    "接下来的步骤是：\n",
    "\n",
    "1. 在现有工作区中创建一个实验。\n",
    "2. 创建一个计算集群。\n",
    "3. 加载数据集。\n",
    "4. 使用 AutoMLConfig 配置 AutoML。\n",
    "5. 运行 AutoML 实验。\n",
    "6. 探索结果并获取最佳模型。\n",
    "7. 注册最佳模型。\n",
    "8. 部署最佳模型。\n",
    "9. 使用端点。\n",
    "\n",
    "## Azure Machine Learning SDK 的特定导入\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "from azureml.core import Workspace, Experiment\n",
    "from azureml.core.compute import AmlCompute\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.widgets import RunDetails\n",
    "from azureml.core.model import InferenceConfig, Model\n",
    "from azureml.core.webservice import AciWebservice"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 初始化工作区\n",
    "从已保存的配置中初始化一个工作区对象。请确保配置文件存在于 .\\config.json\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "ws = Workspace.from_config()\n",
    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 创建一个 Azure ML 实验\n",
    "\n",
    "让我们在刚刚初始化的工作区中创建一个名为“aml-experiment”的实验。\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "experiment_name = 'aml-experiment'\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "experiment"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 创建计算群集\n",
    "您需要为您的 AutoML 运行创建一个[计算目标](https://docs.microsoft.com/azure/machine-learning/concept-azure-machine-learning-architecture#compute-target)。\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "aml_name = \"heart-f-cluster\"\n",
    "try:\n",
    "    aml_compute = AmlCompute(ws, aml_name)\n",
    "    print('Found existing AML compute context.')\n",
    "except:\n",
    "    print('Creating new AML compute context.')\n",
    "    aml_config = AmlCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\", min_nodes=1, max_nodes=3)\n",
    "    aml_compute = AmlCompute.create(ws, name = aml_name, provisioning_configuration = aml_config)\n",
    "    aml_compute.wait_for_completion(show_output = True)\n",
    "\n",
    "cts = ws.compute_targets\n",
    "compute_target = cts[aml_name]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 数据\n",
    "请确保您已将数据集上传到 Azure ML，并且密钥名称与数据集名称相同。\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "key = 'heart-failure-records'\n",
    "dataset = ws.datasets[key]\n",
    "df = dataset.to_pandas_dataframe()\n",
    "df.describe()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 自动化机器学习配置\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "automl_settings = {\n",
    "    \"experiment_timeout_minutes\": 20,\n",
    "    \"max_concurrent_iterations\": 3,\n",
    "    \"primary_metric\" : 'AUC_weighted'\n",
    "}\n",
    "\n",
    "automl_config = AutoMLConfig(compute_target=compute_target,\n",
    "                             task = \"classification\",\n",
    "                             training_data=dataset,\n",
    "                             label_column_name=\"DEATH_EVENT\",\n",
    "                             enable_early_stopping= True,\n",
    "                             featurization= 'auto',\n",
    "                             debug_log = \"automl_errors.log\",\n",
    "                             **automl_settings\n",
    "                            )"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 自动化机器学习运行\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "remote_run = experiment.submit(automl_config)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "RunDetails(remote_run).show()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "best_run, fitted_model = remote_run.get_output()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "best_run.get_properties()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "model_name = best_run.properties['model_name']\n",
    "script_file_name = 'inference/score.py'\n",
    "best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py')\n",
    "description = \"aml heart failure project sdk\"\n",
    "model = best_run.register_model(model_name = model_name,\n",
    "                                description = description,\n",
    "                                tags = None)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 部署最佳模型\n",
    "\n",
    "运行以下代码以部署最佳模型。您可以在 Azure ML 门户中查看部署状态。此步骤可能需要几分钟时间。\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "inference_config = InferenceConfig(entry_script=script_file_name, environment=best_run.get_environment())\n",
    "\n",
    "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1,\n",
    "                                               memory_gb = 1,\n",
    "                                               tags = {'type': \"automl-heart-failure-prediction\"},\n",
    "                                               description = 'Sample service for AutoML Heart Failure Prediction')\n",
    "\n",
    "aci_service_name = 'automl-hf-sdk'\n",
    "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
    "aci_service.wait_for_deployment(True)\n",
    "print(aci_service.state)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 使用端点\n",
    "您可以向以下输入示例添加输入内容。\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "data = {\n",
    "    \"data\":\n",
    "    [\n",
    "        {\n",
    "            'age': \"60\",\n",
    "            'anaemia': \"false\",\n",
    "            'creatinine_phosphokinase': \"500\",\n",
    "            'diabetes': \"false\",\n",
    "            'ejection_fraction': \"38\",\n",
    "            'high_blood_pressure': \"false\",\n",
    "            'platelets': \"260000\",\n",
    "            'serum_creatinine': \"1.40\",\n",
    "            'serum_sodium': \"137\",\n",
    "            'sex': \"false\",\n",
    "            'smoking': \"false\",\n",
    "            'time': \"130\",\n",
    "        },\n",
    "    ],\n",
    "}\n",
    "\n",
    "test_sample = str.encode(json.dumps(data))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "response = aci_service.run(input_data=test_sample)\n",
    "response"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。对于因使用本翻译而引起的任何误解或误读，我们概不负责。\n"
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