14. Scikit Learn hyperdrive.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Azure ML Hyperdrive avec Scikit-Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='https://github.com/retkowsky/images/blob/master/AzureMLservicebanniere.png?raw=true'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Efficiently tune hyperparameters** for your model using Azure Machine Learning.<br>\n",
    "**Hyperparameter tuning** includes the following steps:\n",
    "<br>\n",
    "- Define the parameter search space<br>\n",
    "- Specify a primary metric to optimize<br>\n",
    "- Specify early termination criteria for poorly performing runs<br>\n",
    "- Allocate resources for hyperparameter tuning<br>\n",
    "- Launch an experiment with the above configuration<br>\n",
    "- Visualize the training runs<br>\n",
    "- Select the best performing configuration for your model<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Documentation Hyperdrive avec Azure ML :\n",
    "https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) \\n[GCC 7.3.0]'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-16 13:38:39.771361\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "now = datetime.datetime.now()\n",
    "print(now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Version Azure ML :  1.13.0\n"
     ]
    }
   ],
   "source": [
    "import azureml.core\n",
    "print(\"Version Azure ML : \", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Workspace Azure ML : workshopAML2020\n"
     ]
    }
   ],
   "source": [
    "from azureml.core import Workspace\n",
    "\n",
    "# Load the workspace from the saved config file\n",
    "ws = Workspace.from_config()\n",
    "print('Workspace Azure ML :', ws.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compute Targets:\n",
      "\t cpupipelines : AmlCompute\n",
      "\t gpuclusterstdnc6 : AmlCompute\n",
      "\t AutoML : AmlCompute\n",
      "\t Designer : AmlCompute\n",
      "\t notebooksjupyter : ComputeInstance\n",
      "\t cpucluster : AmlCompute\n",
      "\t AutoMLsdk : AmlCompute\n",
      "\t clustergpuNC6 : AmlCompute\n",
      "Datastores:\n",
      "\t azureml_globaldatasets : AzureBlob\n",
      "\t azureml : AzureBlob\n",
      "\t modelservingdata : AzureBlob\n",
      "\t aiexportdata : AzureBlob\n",
      "\t modeldata : AzureBlob\n",
      "\t teststorageserge : AzureBlob\n",
      "\t workspaceblobstore : AzureBlob\n",
      "\t workspacefilestore : AzureFile\n",
      "Datasets:\n",
      "\t sample_request_data\n",
      "\t diabetes dataset\n",
      "\t diabetes\n",
      "\t OJSales_Validate\n",
      "\t OJSales_Train\n",
      "\t OJSales_All\n",
      "\t titanic\n",
      "\t DefectScores\n",
      "\t MD-Defect-Train_Model-Trained_model-9b4070d1\n",
      "\t Failure\n",
      "\t MD-Maintenance-Train_Model-Trained_model-9b4070d1\n",
      "\t Maintenance\n",
      "\t Transactions\n",
      "\t NOAA-Weather-DS4\n",
      "\t drift-demo-dataset\n",
      "\t target\n",
      "\t dataset\n",
      "\t Iris\n",
      "\t mnist dataset\n"
     ]
    }
   ],
   "source": [
    "from azureml.core import ComputeTarget, Datastore, Dataset\n",
    "\n",
    "print(\"Compute Targets:\")\n",
    "for compute_name in ws.compute_targets:\n",
    "    compute = ws.compute_targets[compute_name]\n",
    "    print(\"\\t\", compute.name, ':', compute.type)\n",
    "    \n",
    "print(\"Datastores:\")\n",
    "for datastore_name in ws.datastores:\n",
    "    datastore = Datastore.get(ws, datastore_name)\n",
    "    print(\"\\t\", datastore.name, ':', datastore.datastore_type)\n",
    "    \n",
    "print(\"Datasets:\")\n",
    "for dataset_name in list(ws.datasets.keys()):\n",
    "    dataset = Dataset.get_by_name(ws, dataset_name)\n",
    "    print(\"\\t\", dataset.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Hyperdrive pour trouver la meilleure combinaison\n",
    "\n",
    "The remote compute you created is a four-node cluster, and you can take advantage of this to execute multiple experiment runs in parallel. One key reason to do this is to try training a model with a range of different hyperparameter values.\n",
    "\n",
    "Azure ML includes a feature called *hyperdrive* that enables you to randomly try different values for one or more hyperparameters, and find the best performing trained model based on a metric that you specify - such as *Accuracy* or *Area Under the Curve (AUC)*.\n",
    "\n",
    "> **More Information**: For more information about Hyperdrive, see the [Azure ML documentation](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters).\n",
    "\n",
    "Let's run a Hyperdrive experiment on the remote compute you have provisioned. First, we'll create the experiment and its associated folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from azureml.core import Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Expérimentation\n",
    "hyperdrive_experiment_name = 'Exemple14-Scikit-Learn-HyperDrive'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expérimentation : Exemple14-Scikit-Learn-HyperDrive\n"
     ]
    }
   ],
   "source": [
    "hyperdrive_experiment = Experiment(workspace = ws, name = hyperdrive_experiment_name)\n",
    "\n",
    "hyperdrive_experiment_folder = './' + hyperdrive_experiment_name\n",
    "os.makedirs(hyperdrive_experiment_folder, exist_ok=True)\n",
    "\n",
    "print(\"Expérimentation :\", hyperdrive_experiment.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting ./Exemple14-Scikit-Learn-HyperDrive/diabetes_training.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile $hyperdrive_experiment_folder/diabetes_training.py\n",
    "\n",
    "import argparse\n",
    "import joblib\n",
    "from azureml.core import Workspace, Dataset, Experiment, Run\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "parser = argparse.ArgumentParser()\n",
    "parser.add_argument('--regularization', type=float, dest='reg_rate', default=0.01, help='regularization rate')\n",
    "args = parser.parse_args()\n",
    "reg = args.reg_rate\n",
    "\n",
    "run = Run.get_context()\n",
    "\n",
    "print(\"Chargement des données...\")\n",
    "diabetes = run.input_datasets['diabetes'].to_pandas_dataframe()\n",
    "\n",
    "X, y = diabetes[['Pregnancies','PlasmaGlucose','DiastolicBloodPressure','TricepsThickness','SerumInsulin','BMI',\n",
    "                 'DiabetesPedigree','Age']].values, diabetes['Diabetic'].values\n",
    "\n",
    "# Partitionnement\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0)\n",
    "\n",
    "# Modélisation\n",
    "print('Régression logistique avec taux de régularisation', reg)\n",
    "run.log('Taux de régularisation',  np.float(reg))\n",
    "model = LogisticRegression(C=1/reg, solver=\"liblinear\").fit(X_train, y_train)\n",
    "\n",
    "# Accuracy\n",
    "y_hat = model.predict(X_test)\n",
    "acc = np.average(y_hat == y_test)\n",
    "print('Accuracy =', acc)\n",
    "run.log('Accuracy', np.float(acc))\n",
    "\n",
    "# AUC\n",
    "y_scores = model.predict_proba(X_test)\n",
    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
    "print('AUC =' + str(auc))\n",
    "run.log('AUC', np.float(auc))\n",
    "\n",
    "# Courbe de ROC\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Courbe de ROC')\n",
    "run.log_image(name = \"ROC\", plot = fig)\n",
    "plt.show()\n",
    "\n",
    "os.makedirs('outputs', exist_ok=True)\n",
    "\n",
    "joblib.dump(value=model, filename='outputs/diabetes.pkl')\n",
    "\n",
    "run.complete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rwxrwxrwx 1 root root 1880 Sep 16 13:38 Exemple14-Scikit-Learn-HyperDrive/diabetes_training.py\r\n"
     ]
    }
   ],
   "source": [
    "!ls Exemple14-Scikit-Learn-HyperDrive/diabetes_training.py -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "import argparse\n",
      "import joblib\n",
      "from azureml.core import Workspace, Dataset, Experiment, Run\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from sklearn.model_selection import train_test_split\n",
      "from sklearn.linear_model import LogisticRegression\n",
      "from sklearn.metrics import roc_auc_score\n",
      "from sklearn.metrics import roc_curve\n",
      "\n",
      "parser = argparse.ArgumentParser()\n",
      "parser.add_argument('--regularization', type=float, dest='reg_rate', default=0.01, help='regularization rate')\n",
      "args = parser.parse_args()\n",
      "reg = args.reg_rate\n",
      "\n",
      "run = Run.get_context()\n",
      "\n",
      "print(\"Chargement des données...\")\n",
      "diabetes = run.input_datasets['diabetes'].to_pandas_dataframe()\n",
      "\n",
      "X, y = diabetes[['Pregnancies','PlasmaGlucose','DiastolicBloodPressure','TricepsThickness','SerumInsulin','BMI',\n",
      "                 'DiabetesPedigree','Age']].values, diabetes['Diabetic'].values\n",
      "\n",
      "# Partitionnement\n",
      "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0)\n",
      "\n",
      "# Modélisation\n",
      "print('Régression logistique avec taux de régularisation', reg)\n",
      "run.log('Taux de régularisation',  np.float(reg))\n",
      "model = LogisticRegression(C=1/reg, solver=\"liblinear\").fit(X_train, y_train)\n",
      "\n",
      "# Accuracy\n",
      "y_hat = model.predict(X_test)\n",
      "acc = np.average(y_hat == y_test)\n",
      "print('Accuracy =', acc)\n",
      "run.log('Accuracy', np.float(acc))\n",
      "\n",
      "# AUC\n",
      "y_scores = model.predict_proba(X_test)\n",
      "auc = roc_auc_score(y_test,y_scores[:,1])\n",
      "print('AUC =' + str(auc))\n",
      "run.log('AUC', np.float(auc))\n",
      "\n",
      "# Courbe de ROC\n",
      "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
      "fig = plt.figure(figsize=(6, 4))\n",
      "\n",
      "plt.plot([0, 1], [0, 1], 'k--')\n",
      "plt.plot(fpr, tpr)\n",
      "plt.xlabel('False Positive Rate')\n",
      "plt.ylabel('True Positive Rate')\n",
      "plt.title('Courbe de ROC')\n",
      "run.log_image(name = \"ROC\", plot = fig)\n",
      "plt.show()\n",
      "\n",
      "os.makedirs('outputs', exist_ok=True)\n",
      "\n",
      "joblib.dump(value=model, filename='outputs/diabetes.pkl')\n",
      "\n",
      "run.complete()\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Viewing the yml file\n",
    "with open(os.path.join('./Exemple14-Scikit-Learn-HyperDrive/diabetes_training.py'), 'r') as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating\n",
      "Succeeded.................\n",
      "AmlCompute wait for completion finished\n",
      "\n",
      "Minimum number of nodes requested have been provisioned\n"
     ]
    }
   ],
   "source": [
    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
    "from azureml.core.compute_target import ComputeTargetException\n",
    "\n",
    "cluster_name = \"cpu-standardd4\"\n",
    "\n",
    "try:\n",
    "    compute1 = ComputeTarget(workspace=ws, name=cluster_name)\n",
    "    print('Found existing cluster, use it.')\n",
    "except ComputeTargetException:\n",
    "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D4', min_nodes=1, max_nodes=10)\n",
    "    compute1 = ComputeTarget.create(ws, cluster_name, compute_config)\n",
    "\n",
    "compute1.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ressources du workspace : cpupipelines\n",
      "Ressources du workspace : gpuclusterstdnc6\n",
      "Ressources du workspace : AutoML\n",
      "Ressources du workspace : Designer\n",
      "Ressources du workspace : notebooksjupyter\n",
      "Ressources du workspace : cpucluster\n",
      "Ressources du workspace : AutoMLsdk\n",
      "Ressources du workspace : clustergpuNC6\n",
      "Ressources du workspace : cpu-standardd4\n",
      "Ressources du workspace : gpuclustNC6\n"
     ]
    }
   ],
   "source": [
    "# Liste des clusters\n",
    "liste = ws.compute_targets\n",
    "for liste in liste:\n",
    "    print(\"Ressources du workspace :\", liste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Définition de tags pour le run\n",
    "tagsdurun = {\"Type\": \"test\" , \"Langage\" : \"Python\" , \"Framework\" : \"Scikit-Learn\" , \"Hyperdrive\" : \"Gridsearch\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.hyperdrive import GridParameterSampling, BanditPolicy, HyperDriveConfig, PrimaryMetricGoal\n",
    "from azureml.train.hyperdrive import choice\n",
    "from azureml.widgets import RunDetails\n",
    "from azureml.train.sklearn import SKLearn\n",
    "\n",
    "# Grid Search\n",
    "params = GridParameterSampling(\n",
    "    {\n",
    "        # Différentes valeurs du paramétre de régularisation à tester\n",
    "        '--regularization': choice(0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5)\n",
    "    }\n",
    ")\n",
    "\n",
    "# Policy Bandit is an early termination policy based on slack factor/slack amount and evaluation interval. \n",
    "# The policy early terminates any runs where the primary metric is not within the specified slack factor/slack amount \n",
    "# with respect to the best performing training run.\n",
    "\n",
    "policy = BanditPolicy(evaluation_interval=2, slack_factor=0.1)\n",
    "\n",
    "# Données\n",
    "diabetes_ds = ws.datasets.get(\"diabetes\")\n",
    "\n",
    "# Définition estimateur\n",
    "hyper_estimator = SKLearn(source_directory=hyperdrive_experiment_folder,\n",
    "                           inputs=[diabetes_ds.as_named_input('diabetes')], # Données en entrée\n",
    "                           compute_target = compute1, # Compute server\n",
    "                           conda_packages=['pandas','ipykernel','matplotlib'], #Dépendances\n",
    "                           pip_packages=['azureml-sdk','argparse','pyarrow'], \n",
    "                           entry_script='diabetes_training.py')  # script Python\n",
    "\n",
    "# Configuration hyperdrive\n",
    "hyperdrive = HyperDriveConfig(estimator=hyper_estimator, \n",
    "                          hyperparameter_sampling=params, # Paramétres\n",
    "                          policy=policy, #Policy\n",
    "                          primary_metric_name='Accuracy', #Métrique\n",
    "                          primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, #Optimisation de la métrique\n",
    "                          max_total_runs=10,\n",
    "                          max_concurrent_runs=8)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Documentation:\n",
    "    https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.banditpolicy?view=azure-ml-py\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING - If 'script' has been provided here and a script file name has been specified in 'run_config', 'script' provided in ScriptRunConfig initialization will take precedence.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81d49d20cd1d4be5b0b5a9d03f55d7aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_HyperDriveWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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229, 230], \"mode\": \"lines\", \"name\": \"Taux de r\\u00e9gularisation_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [0.15, 0.15, 0.2, 0.2, 0.4, 0.4, 0.4, 0.5]}], \"Accuracy\": [{\"categories\": [222, 223, 224, 226, 227, 228, 229, 230], \"mode\": \"markers\", \"name\": \"Accuracy\", \"stepped\": false, \"type\": \"scatter\", \"data\": [0.7736666666666666, 0.7736666666666666, 0.7733333333333333, 0.774, 0.7743333333333333, 0.7743333333333333, 0.774, 0.7733333333333333]}, {\"categories\": [222, 223, 224, 226, 227, 228, 229, 230], \"mode\": \"lines\", \"name\": \"Accuracy_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [0.7736666666666666, 0.7736666666666666, 0.7736666666666666, 0.774, 0.7743333333333333, 0.7743333333333333, 0.7743333333333333, 0.7743333333333333]}], \"AUC\": [{\"categories\": [222, 223, 224, 226, 227, 229, 230], \"mode\": \"markers\", \"name\": \"AUC\", \"stepped\": false, \"type\": \"scatter\", \"data\": [0.848360614955968, 0.8483800189064132, 0.8482019005920691, 0.8484382307577492, 0.8482780237822777, 0.8483596198815861, 0.848203393203642]}, {\"categories\": [222, 223, 224, 226, 227, 229, 230], \"mode\": \"lines\", \"name\": \"AUC_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [0.848360614955968, 0.8483800189064132, 0.8483800189064132, 0.8484382307577492, 0.8484382307577492, 0.8484382307577492, 0.8484382307577492]}]}, \"metricName\": null, \"primaryMetricName\": \"Accuracy\", \"showLegend\": false}, \"run_metrics\": [{\"name\": \"best_child_by_primary_metric\", \"run_id\": \"HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64\", \"categories\": [0], \"series\": [{\"data\": [{\"metric_name\": [\"Accuracy\", \"Accuracy\"], \"timestamp\": [\"2020-09-16 13:44:28.296718+00:00\", \"2020-09-16 13:44:28.296718+00:00\"], \"run_id\": [\"HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_5\", \"HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_5\"], \"metric_value\": [0.7743333333333333, 0.7743333333333333], \"final\": [false, true]}]}]}], \"run_logs\": \"[2020-09-16T13:41:21.445576][API][INFO]Experiment created\\r\\n[2020-09-16T13:41:22.298745][GENERATOR][INFO]Trying to sample '8' jobs from the hyperparameter space\\r\\n[2020-09-16T13:41:22.596492][GENERATOR][INFO]Successfully sampled '8' jobs, they will soon be submitted to the execution target.\\r\\n[2020-09-16T13:41:23.2042613Z][SCHEDULER][INFO]The execution environment is being prepared. Please be patient as it can take a few minutes.\\r\\n[2020-09-16T13:41:54.1473583Z][SCHEDULER][INFO]The execution environment was successfully prepared.\\r\\n[2020-09-16T13:41:54.1487548Z][SCHEDULER][INFO]Scheduling job, id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_0'\\r\\n[2020-09-16T13:41:54.2213009Z][SCHEDULER][INFO]Scheduling job, id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_6'\\r\\n[2020-09-16T13:41:54.1495754Z][SCHEDULER][INFO]Scheduling job, id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_1'\\r\\n[2020-09-16T13:41:54.2031350Z][SCHEDULER][INFO]Scheduling job, id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_3'\\r\\n[2020-09-16T13:41:54.2202548Z][SCHEDULER][INFO]Scheduling job, id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_5'\\r\\n[2020-09-16T13:41:54.1594482Z][SCHEDULER][INFO]Scheduling job, id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_2'\\r\\n[2020-09-16T13:41:54.2035598Z][SCHEDULER][INFO]Scheduling job, id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_4'\\r\\n[2020-09-16T13:41:54.1664354Z][SCHEDULER][INFO]Scheduling job, id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_7'\\r\\n[2020-09-16T13:41:55.3218502Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_4'\\r\\n[2020-09-16T13:41:55.3663214Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_1'\\r\\n[2020-09-16T13:41:55.4369301Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_0'\\r\\n[2020-09-16T13:41:55.5301678Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_2'\\r\\n[2020-09-16T13:41:55.5850801Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_7'\\r\\n[2020-09-16T13:41:55.7726435Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_3'\\r\\n[2020-09-16T13:41:55.8721162Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_5'\\r\\n[2020-09-16T13:41:55.8912185Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_6'\\r\\n[2020-09-16T13:42:22.991790][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_5] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:42:53.039065][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_5] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:43:23.608623][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_5] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:43:54.458742][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_5] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:44:25.633656][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_5] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:44:58.560311][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_2] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:45:29.162577][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_0, https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_2] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:45:30.675728][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2020-09-16T13:45:31.110278][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2020-09-16T13:45:59.434358][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_0, https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_2] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:46:01.4260631Z][SCHEDULER][INFO]Scheduling job, id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_8'\\r\\n[2020-09-16T13:46:02.4182854Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_8'\\r\\n[2020-09-16T13:46:30.241145][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_0, https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_2] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:47:01.722903][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_0, https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_2] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:47:32.353374][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_0] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:48:03.141617][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_0] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:48:32.918835][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_0] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:49:03.235645][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_0, https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_4] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:49:07.164664][GENERATOR][WARNING]Could not sample any more jobs from the space.\\r\\n[2020-09-16T13:49:07.150590][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2020-09-16T13:49:34.323827][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_0, https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_4] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:50:05.381788][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_1] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:50:36.425786][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_1, https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_3] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:51:07.603162][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_6] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:51:38.360535][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_6, https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_7] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:52:09.305687][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_8] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:52:40.331524][ENFORCER][INFO]Jobs [https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_8] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2020-09-16T13:52:53.176556][CONTROLLER][WARNING]User errors were found in at least one of the child runs.\\r\\n[2020-09-16T13:52:55.708582][CONTROLLER][INFO]Experiment was 'ExperimentStatus.RUNNING', is 'ExperimentStatus.FINISHED'.\\n\\nError occurred: User errors were found in at least one of the child runs.\\n\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.13.0\"}, \"loading\": false}"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run\n",
    "hyperdrive_run = hyperdrive_experiment.submit(config=hyperdrive, tags=tagsdurun)\n",
    "RunDetails(hyperdrive_run).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job status: Process is Completed\n"
     ]
    }
   ],
   "source": [
    "print(\"Job status: Process is\", hyperdrive_run.get_status())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'runId': 'HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64',\n",
       " 'target': 'cpu-standardd4',\n",
       " 'status': 'Completed',\n",
       " 'startTimeUtc': '2020-09-16T13:41:21.180337Z',\n",
       " 'endTimeUtc': '2020-09-16T13:52:55.054325Z',\n",
       " 'error': {'error': {'code': 'UserError',\n",
       "   'message': 'User errors were found in at least one of the child runs.',\n",
       "   'details': [],\n",
       "   'debugInfo': {'message': '[(https://westeurope.experiments.azureml.net/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshopAML2020-rg/providers/Microsoft.MachineLearningServices/workspaces/workshopAML2020/experiments/**SCRUBBED**/runs/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_0, {\\'error\\': {\\n    \"additional_properties\": {\\n        \"componentName\": null\\n    },\\n    \"error\": {\\n        \"additional_properties\": {\\n            \"messageParameters\": {},\\n            \"referenceCode\": null,\\n            \"severity\": null,\\n            \"messageFormat\": null\\n        },\\n        \"code\": \"UserError\",\\n        \"message\": \"User program failed with DatasetExecutionError: in operation \\'to_pandas_dataframe\\' for Dataset(id=\\'ce98a2c5-a7e3-4fed-8f9a-8434305dc23b\\', name=\\'diabetes\\', version=1)\\\\nError Code: ScriptExecution.DatastoreResolution.Unexpected\\\\nFailed Step: dc5a13d5-f5d0-402f-b497-5ae284cda8c4\\\\nError Message: ScriptExecutionException was caused by DatastoreResolutionException.\\\\n  DatastoreResolutionException was caused by UnexpectedException.\\\\n    Unexpected failure while fetching info for Datastore \\'workspaceblobstore\\' in subscription: \\'70b8f39e-8863-49f7-b6ba-34a80799550c\\', resource group: \\'workshopAML2020-rg\\', workspace: \\'workshopAML2020\\'. Using base service url: https://westeurope.experiments.azureml.net.\\\\n      The operation was canceled.\\\\n| session_id=dfa612d7-e4ac-48d0-bb34-e0b1c6d6e201\",\\n        \"details_uri\": \"https://aka.ms/azureml-known-errors\",\\n        \"target\": null,\\n        \"details\": [],\\n        \"inner_error\": null,\\n        \"debug_info\": {\\n            \"additional_properties\": {},\\n            \"type\": \"DatasetExecutionError\",\\n            \"message\": \"in operation \\'to_pandas_dataframe\\' for Dataset(id=\\'ce98a2c5-a7e3-4fed-8f9a-8434305dc23b\\', name=\\'diabetes\\', version=1)\\\\nError Code: ScriptExecution.DatastoreResolution.Unexpected\\\\nFailed Step: dc5a13d5-f5d0-402f-b497-5ae284cda8c4\\\\nError Message: ScriptExecutionException was caused by DatastoreResolutionException.\\\\n  DatastoreResolutionException was caused by UnexpectedException.\\\\n    Unexpected failure while fetching info for Datastore \\'workspaceblobstore\\' in subscription: \\'70b8f39e-8863-49f7-b6ba-34a80799550c\\', resource group: \\'workshopAML2020-rg\\', workspace: \\'workshopAML2020\\'. Using base service url: https://westeurope.experiments.azureml.net.\\\\n      The operation was canceled.\\\\n| session_id=dfa612d7-e4ac-48d0-bb34-e0b1c6d6e201\",\\n            \"stack_trace\": \"  File \\\\\"/mnt/batch/tasks/shared/LS_root/jobs/workshopaml2020/azureml/hd_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_0/mounts/workspaceblobstore/azureml/HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_0/azureml-setup/context_manager_injector.py\\\\\", line 166, in execute_with_context\\\\n    runpy.run_path(sys.argv[0], globals(), run_name=\\\\\"__main__\\\\\")\\\\n  File \\\\\"/azureml-envs/azureml_0aaf110227fc8460da3fec9a83981489/lib/python3.6/runpy.py\\\\\", line 263, in run_path\\\\n    pkg_name=pkg_name, script_name=fname)\\\\n  File \\\\\"/azureml-envs/azureml_0aaf110227fc8460da3fec9a83981489/lib/python3.6/runpy.py\\\\\", line 96, in _run_module_code\\\\n    mod_name, mod_spec, pkg_name, script_name)\\\\n  File \\\\\"/azureml-envs/azureml_0aaf110227fc8460da3fec9a83981489/lib/python3.6/runpy.py\\\\\", line 85, in _run_code\\\\n    exec(code, run_globals)\\\\n  File \\\\\"diabetes_training.py\\\\\", line 21, in <module>\\\\n    diabetes = run.input_datasets[\\'diabetes\\'].to_pandas_dataframe()\\\\n  File \\\\\"/azureml-envs/azureml_0aaf110227fc8460da3fec9a83981489/lib/python3.6/site-packages/azureml/data/_loggerfactory.py\\\\\", line 126, in wrapper\\\\n    return func(*args, **kwargs)\\\\n  File \\\\\"/azureml-envs/azureml_0aaf110227fc8460da3fec9a83981489/lib/python3.6/site-packages/azureml/data/tabular_dataset.py\\\\\", line 169, in to_pandas_dataframe\\\\n    None if self.id is None else {\\'id\\': self.id, \\'name\\': self.name, \\'version\\': self.version})\\\\n  File \\\\\"/azureml-envs/azureml_0aaf110227fc8460da3fec9a83981489/lib/python3.6/site-packages/azureml/data/dataset_error_handling.py\\\\\", line 109, in _try_execute\\\\n    raise DatasetExecutionError(message, e)\\\\n\",\\n            \"inner_exception\": null,\\n            \"data\": null,\\n            \"error_response\": null\\n        }\\n    },\\n    \"correlation\": null,\\n    \"environment\": null,\\n    \"location\": null,\\n    \"time\": {}\\n}})]'},\n",
       "   'messageParameters': {}},\n",
       "  'time': '0001-01-01T00:00:00.000Z'},\n",
       " 'properties': {'primary_metric_config': '{\"name\": \"Accuracy\", \"goal\": \"maximize\"}',\n",
       "  'resume_from': 'null',\n",
       "  'runTemplate': 'HyperDrive',\n",
       "  'azureml.runsource': 'hyperdrive',\n",
       "  'platform': 'AML',\n",
       "  'ContentSnapshotId': 'b95d4ec3-1503-46aa-ac6a-d1cc07ecfbac',\n",
       "  'score': '0.7743333333333333',\n",
       "  'best_child_run_id': 'HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_5',\n",
       "  'best_metric_status': 'Succeeded'},\n",
       " 'inputDatasets': [],\n",
       " 'outputDatasets': [],\n",
       " 'logFiles': {'azureml-logs/hyperdrive.txt': 'https://workshopaml2027584246021.blob.core.windows.net/azureml/ExperimentRun/dcid.HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64/azureml-logs/hyperdrive.txt?sv=2019-02-02&sr=b&sig=of5ps3EvMcSBwyGbTvpFSGcCP9DBC3I1O6imnMLqYzo%3D&st=2020-09-16T13%3A43%3A11Z&se=2020-09-16T21%3A53%3A11Z&sp=r'}}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Progression du run\n",
    "hyperdrive_run.get_details()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### On récupère le best run :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_hyperdrive_run = hyperdrive_run.get_best_run_by_primary_metric()\n",
    "best_hyperdrive_run_metrics = best_hyperdrive_run.get_metrics()\n",
    "hyperdrive_parameter_values = best_hyperdrive_run.get_details() ['runDefinition']['arguments']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Résultats du best run de l'hyperparameter Tuning :\n",
      "\n",
      "Best Run ID = HD_e4e76bb2-bb21-43f4-86c5-318be8e6fa64_5\n",
      "\n",
      "Regularization Rate optimal = ['--regularization', '0.25']\n",
      "\n",
      "Métriques :\n",
      " - AUC = 0.8483337479476591\n",
      " - Accuracy = 0.7743333333333333\n"
     ]
    }
   ],
   "source": [
    "print(\"Résultats du best run de l'hyperparameter Tuning :\")\n",
    "print()\n",
    "print('Best Run ID =', best_hyperdrive_run.id)\n",
    "print()\n",
    "print('Regularization Rate optimal =', hyperdrive_parameter_values)\n",
    "print()\n",
    "print('Métriques :')\n",
    "print(' - AUC =', best_hyperdrive_run_metrics['AUC'])\n",
    "print(' - Accuracy =', best_hyperdrive_run_metrics['Accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### On référence le meilleur modèle :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Model(workspace=Workspace.create(name='workshopAML2020', subscription_id='70b8f39e-8863-49f7-b6ba-34a80799550c', resource_group='workshopAML2020-rg'), name=Diabetes, id=Diabetes:23, version=23, tags={'Training context': 'Hyperdrive'}, properties={'AUC': '0.8483337479476591', 'Accuracy': '0.7743333333333333'})"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from azureml.core import Model\n",
    "\n",
    "best_hyperdrive_run.register_model(model_path='outputs/diabetes.pkl', \n",
    "                                   model_name='Diabetes',\n",
    "                                   tags={'Training context':'Hyperdrive'},\n",
    "                                   properties={'AUC': best_hyperdrive_run_metrics['AUC'],\n",
    "                                               'Accuracy': best_hyperdrive_run_metrics['Accuracy']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Le modèle est disponible dans le section **Models** d'Azure ML Studio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suppression du compute cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AutoML ( AmlCompute ) : Succeeded\n",
      "Designer ( AmlCompute ) : Succeeded\n",
      "notebooksjupyter ( ComputeInstance ) : Succeeded\n",
      "cpucluster ( AmlCompute ) : Succeeded\n",
      "AutoMLsdk ( AmlCompute ) : Succeeded\n",
      "clustergpuNC6 ( AmlCompute ) : Succeeded\n",
      "cpu-standardd4 ( AmlCompute ) : Succeeded\n",
      "gpuclustNC6 ( AmlCompute ) : Succeeded\n"
     ]
    }
   ],
   "source": [
    "compute_targets = ws.compute_targets\n",
    "for name, ct in compute_targets.items():\n",
    "    print(name, \"(\" , ct.type, \") :\", ct.provisioning_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Suppression du cluster\n",
    "#compute1.delete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AutoML ( AmlCompute ) : Succeeded\n",
      "Designer ( AmlCompute ) : Succeeded\n",
      "notebooksjupyter ( ComputeInstance ) : Succeeded\n",
      "cpucluster ( AmlCompute ) : Succeeded\n",
      "AutoMLsdk ( AmlCompute ) : Succeeded\n",
      "clustergpuNC6 ( AmlCompute ) : Succeeded\n",
      "cpu-standardd4 ( AmlCompute ) : Succeeded\n",
      "gpuclustNC6 ( AmlCompute ) : Succeeded\n"
     ]
    }
   ],
   "source": [
    "compute_targets = ws.compute_targets\n",
    "for name, ct in compute_targets.items():\n",
    "    print(name, \"(\" , ct.type, \") :\", ct.provisioning_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://github.com/retkowsky/images/blob/master/Powered-by-MS-Azure-logo-v2.png?raw=true\" height=\"300\" width=\"300\">"
   ]
  }
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