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

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9.7 KiB

{
"cells": [
{
"cell_type": "markdown",
"source": [
"# Duomenų mokslas debesyje: „Azure ML SDK“ būdas\n",
"\n",
"## Įvadas\n",
"\n",
"Šiame užrašų knygelėje išmoksime, kaip naudoti „Azure ML SDK“, kad galėtume treniruoti, diegti ir naudoti modelį per „Azure ML“.\n",
"\n",
"Būtinos sąlygos:\n",
"1. Jūs sukūrėte „Azure ML“ darbo sritį.\n",
"2. Jūs įkėlėte [Širdies nepakankamumo duomenų rinkinį](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data) į „Azure ML“.\n",
"3. Jūs įkėlėte šią užrašų knygelę į „Azure ML Studio“.\n",
"\n",
"Tolimesni žingsniai:\n",
"\n",
"1. Sukurti eksperimentą esamoje darbo srityje.\n",
"2. Sukurti skaičiavimo klasterį.\n",
"3. Įkelti duomenų rinkinį.\n",
"4. Konfigūruoti AutoML naudojant AutoMLConfig.\n",
"5. Paleisti AutoML eksperimentą.\n",
"6. Išnagrinėti rezultatus ir gauti geriausią modelį.\n",
"7. Užregistruoti geriausią modelį.\n",
"8. Diegti geriausią modelį.\n",
"9. Naudoti galutinį tašką.\n",
"\n",
"## Specifiniai „Azure Machine Learning SDK“ importai\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": [
"## Inicializuoti Darbo Aplinką\n",
"Inicializuokite darbo aplinkos objektą iš išsaugotos konfigūracijos. Įsitikinkite, kad konfigūracijos failas yra .\\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": [
"## Sukurkite Azure ML eksperimentą\n",
"\n",
"Sukurkime eksperimentą, pavadintą 'aml-experiment', darbo aplinkoje, kurią ką tik inicializavome.\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": [
"## Sukurkite skaičiavimo klasterį \n",
"Jums reikės sukurti [skaičiavimo tikslą](https://docs.microsoft.com/azure/machine-learning/concept-azure-machine-learning-architecture#compute-target) savo AutoML vykdymui.\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": [
"## Duomenys\n",
"Įsitikinkite, kad įkėlėte duomenų rinkinį į Azure ML ir kad raktas turi tą patį pavadinimą kaip duomenų rinkinys.\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": [
"## AutoML konfigūracija\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": [
"## AutoML vykdymas\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": [
"## Įdiegti geriausią modelį\n",
"\n",
"Paleiskite šį kodą, kad įdiegtumėte geriausią modelį. Diegimo būseną galite peržiūrėti Azure ML portale. Šis žingsnis gali užtrukti kelias minutes.\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": [
"## Naudokite galinį tašką\n",
"Galite pridėti įvesties duomenis prie pateikto įvesties pavyzdžio.\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**Atsakomybės apribojimas**: \nŠis dokumentas buvo išverstas naudojant AI vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors siekiame tikslumo, prašome atkreipti dėmesį, kad automatiniai vertimai gali turėti klaidų ar netikslumų. Originalus dokumentas jo gimtąja kalba turėtų būti laikomas autoritetingu šaltiniu. Kritinei informacijai rekomenduojama naudoti profesionalų žmogaus vertimą. Mes neprisiimame atsakomybės už nesusipratimus ar klaidingus interpretavimus, atsiradusius dėl šio vertimo naudojimo.\n"
]
}
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