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

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{
"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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