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319 lines
12 KiB
319 lines
12 KiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# क्लाउडमा डाटा साइन्स: \"Azure ML SDK\" तरिका\n",
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"\n",
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"## परिचय\n",
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"\n",
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"यस नोटबुकमा, हामी Azure ML SDK प्रयोग गरेर कसरी मोडेललाई प्रशिक्षण, परिनियोजन, र उपभोग गर्ने भन्ने कुरा सिक्नेछौं।\n",
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"\n",
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"पूर्व-आवश्यकताहरू:\n",
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"1. तपाईंले Azure ML कार्यक्षेत्र (Workspace) सिर्जना गर्नुभएको छ।\n",
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"2. तपाईंले [Heart Failure dataset](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data) Azure ML मा लोड गर्नुभएको छ।\n",
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"3. तपाईंले यो नोटबुक Azure ML स्टुडियोमा अपलोड गर्नुभएको छ।\n",
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"\n",
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"अर्को चरणहरू:\n",
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"\n",
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"1. अवस्थित कार्यक्षेत्रमा एउटा प्रयोग (Experiment) सिर्जना गर्नुहोस्।\n",
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"2. एउटा कम्प्युट क्लस्टर सिर्जना गर्नुहोस्।\n",
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"3. डाटासेट लोड गर्नुहोस्।\n",
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"4. AutoMLConfig प्रयोग गरेर AutoML कन्फिगर गर्नुहोस्।\n",
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"5. AutoML प्रयोग परीक्षण (Experiment) चलाउनुहोस्।\n",
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"6. नतिजाहरू अन्वेषण गर्नुहोस् र उत्कृष्ट मोडेल प्राप्त गर्नुहोस्।\n",
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"7. उत्कृष्ट मोडेल दर्ता गर्नुहोस्।\n",
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"8. उत्कृष्ट मोडेल परिनियोजन गर्नुहोस्।\n",
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"9. अन्त बिन्दु (Endpoint) उपभोग गर्नुहोस्।\n",
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"\n",
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"## Azure Machine Learning SDK-विशेष आयातहरू\n"
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],
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"metadata": {}
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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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"source": [
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"from azureml.core import Workspace, Experiment\n",
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"from azureml.core.compute import AmlCompute\n",
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"from azureml.train.automl import AutoMLConfig\n",
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"from azureml.widgets import RunDetails\n",
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"from azureml.core.model import InferenceConfig, Model\n",
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"from azureml.core.webservice import AciWebservice"
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## कार्यक्षेत्र आरम्भ गर्नुहोस् \n",
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"सङ्ग्रहित कन्फिगरेसनबाट कार्यक्षेत्र वस्तु आरम्भ गर्नुहोस्। सुनिश्चित गर्नुहोस् कि कन्फिग फाइल .\\config.json मा उपस्थित छ। \n"
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],
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"metadata": {}
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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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"source": [
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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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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Azure ML प्रयोग गरेर एउटा प्रयोग (Experiment) बनाउनुहोस्\n",
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"\n",
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"हामीले भर्खरै आरम्भ गरेको कार्यक्षेत्रमा 'aml-experiment' नामको एउटा प्रयोग बनाउँ।\n"
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],
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"metadata": {}
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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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"source": [
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"experiment_name = 'aml-experiment'\n",
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"experiment = Experiment(ws, experiment_name)\n",
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"experiment"
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## कम्प्युट क्लस्टर बनाउनुहोस् \n",
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"तपाईंको AutoML रनको लागि [कम्प्युट टार्गेट](https://docs.microsoft.com/azure/machine-learning/concept-azure-machine-learning-architecture#compute-target) बनाउन आवश्यक छ। \n"
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],
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"metadata": {}
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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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"source": [
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"aml_name = \"heart-f-cluster\"\n",
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"try:\n",
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" aml_compute = AmlCompute(ws, aml_name)\n",
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" print('Found existing AML compute context.')\n",
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"except:\n",
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" print('Creating new AML compute context.')\n",
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" aml_config = AmlCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\", min_nodes=1, max_nodes=3)\n",
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" aml_compute = AmlCompute.create(ws, name = aml_name, provisioning_configuration = aml_config)\n",
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" aml_compute.wait_for_completion(show_output = True)\n",
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"\n",
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"cts = ws.compute_targets\n",
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"compute_target = cts[aml_name]"
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## डाटा \n",
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"निश्चित गर्नुहोस् कि तपाईंले डाटासेट Azure ML मा अपलोड गर्नुभएको छ र यसको कुञ्जी डाटासेटको नामसँग उस्तै छ। \n"
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],
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"metadata": {}
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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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"source": [
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"key = 'heart-failure-records'\n",
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"dataset = ws.datasets[key]\n",
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"df = dataset.to_pandas_dataframe()\n",
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"df.describe()"
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [],
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"metadata": {}
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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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"source": [
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"automl_settings = {\n",
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" \"experiment_timeout_minutes\": 20,\n",
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" \"max_concurrent_iterations\": 3,\n",
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" \"primary_metric\" : 'AUC_weighted'\n",
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"}\n",
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"\n",
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"automl_config = AutoMLConfig(compute_target=compute_target,\n",
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" task = \"classification\",\n",
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" training_data=dataset,\n",
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" label_column_name=\"DEATH_EVENT\",\n",
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" enable_early_stopping= True,\n",
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" featurization= 'auto',\n",
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" debug_log = \"automl_errors.log\",\n",
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" **automl_settings\n",
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" )"
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [],
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"metadata": {}
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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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"source": [
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"remote_run = experiment.submit(automl_config)"
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],
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"outputs": [],
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"metadata": {}
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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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"source": [
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"RunDetails(remote_run).show()"
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [],
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"metadata": {}
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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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"source": [
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"best_run, fitted_model = remote_run.get_output()"
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],
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"outputs": [],
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"metadata": {}
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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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"source": [
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"best_run.get_properties()"
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],
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"outputs": [],
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"metadata": {}
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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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"source": [
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"model_name = best_run.properties['model_name']\n",
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"script_file_name = 'inference/score.py'\n",
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"best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py')\n",
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"description = \"aml heart failure project sdk\"\n",
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"model = best_run.register_model(model_name = model_name,\n",
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" description = description,\n",
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" tags = None)"
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## उत्कृष्ट मोडेल परिनियोजन गर्नुहोस्\n",
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"\n",
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"उत्कृष्ट मोडेल परिनियोजन गर्न निम्न कोड चलाउनुहोस्। तपाईंले Azure ML पोर्टलमा परिनियोजनको अवस्था हेर्न सक्नुहुन्छ। यो चरण पूरा हुन केही मिनेट लाग्न सक्छ।\n"
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],
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"metadata": {}
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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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"source": [
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"inference_config = InferenceConfig(entry_script=script_file_name, environment=best_run.get_environment())\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 = {'type': \"automl-heart-failure-prediction\"},\n",
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" description = 'Sample service for AutoML Heart Failure Prediction')\n",
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"\n",
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"aci_service_name = 'automl-hf-sdk'\n",
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"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\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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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## अन्त बिन्दु प्रयोग गर्नुहोस्\n",
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"तपाईं तलको इनपुट नमुनामा इनपुटहरू थप्न सक्नुहुन्छ।\n"
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],
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"metadata": {}
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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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"source": [
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"data = {\n",
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" \"data\":\n",
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" [\n",
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" {\n",
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" 'age': \"60\",\n",
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" 'anaemia': \"false\",\n",
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" 'creatinine_phosphokinase': \"500\",\n",
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" 'diabetes': \"false\",\n",
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" 'ejection_fraction': \"38\",\n",
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" 'high_blood_pressure': \"false\",\n",
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" 'platelets': \"260000\",\n",
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" 'serum_creatinine': \"1.40\",\n",
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" 'serum_sodium': \"137\",\n",
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" 'sex': \"false\",\n",
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" 'smoking': \"false\",\n",
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" 'time': \"130\",\n",
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" },\n",
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" ],\n",
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"}\n",
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"\n",
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"test_sample = str.encode(json.dumps(data))"
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],
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"outputs": [],
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"metadata": {}
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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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"source": [
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"response = aci_service.run(input_data=test_sample)\n",
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"response"
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],
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"outputs": [],
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"metadata": {}
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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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"\n---\n\n**अस्वीकरण**: \nयो दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) प्रयोग गरी अनुवाद गरिएको हो। हामी यथासम्भव सटीकता सुनिश्चित गर्न प्रयास गर्छौं, तर कृपया ध्यान दिनुहोस् कि स्वचालित अनुवादहरूमा त्रुटि वा अशुद्धता हुन सक्छ। यसको मूल भाषामा रहेको मूल दस्तावेज़लाई आधिकारिक स्रोत मानिनुपर्छ। महत्त्वपूर्ण जानकारीका लागि, व्यावसायिक मानव अनुवाद सिफारिस गरिन्छ। यस अनुवादको प्रयोगबाट उत्पन्न हुने कुनै पनि गलतफहमी वा गलत व्याख्याका लागि हामी जिम्मेवार हुने छैनौं।\n"
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]
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}
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],
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"orig_nbformat": 4,
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"language_info": {
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"coopTranslator": {
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"original_hash": "af42669556d5dc19fc4cc3866f7d2597",
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"translation_date": "2025-09-02T05:37:37+00:00",
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"source_file": "5-Data-Science-In-Cloud/19-Azure/notebook.ipynb",
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"language_code": "ne"
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