hyperparameter_tuning.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Hyperparameter Tuning using HyperDrive\n",
    "\n",
    "### 1. Import Dependencies:"
   ]
  },
  {
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   "execution_count": 29,
   "metadata": {
    "gather": {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SDK version: 1.20.0\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "import os\n",
    "import csv\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "import pkg_resources\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
    "\n",
    "from azureml.widgets import RunDetails\n",
    "from azureml.train.sklearn import SKLearn\n",
    "\n",
    "from azureml.train.hyperdrive.run import PrimaryMetricGoal\n",
    "from azureml.train.hyperdrive import TruncationSelectionPolicy\n",
    "from azureml.train.hyperdrive import BayesianParameterSampling\n",
    "\n",
    "from azureml.train.hyperdrive.runconfig import HyperDriveConfig\n",
    "from azureml.train.hyperdrive.parameter_expressions import uniform, choice\n",
    "import os\n",
    "\n",
    "# Check core SDK version number\n",
    "print(\"SDK version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Initialize Workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick-starts-ws-137441\n",
      "aml-quickstarts-137441\n",
      "southcentralus\n",
      "9b72f9e6-56c5-4c16-991b-19c652994860\n"
     ]
    }
   ],
   "source": [
    "ws = Workspace.from_config()\n",
    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Initialize Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "gather": {
     "logged": 1598531917374
    },
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     "source_hidden": false
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    "nteract": {
     "transient": {
      "deleting": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"width:100%\"><tr><th>Name</th><th>Workspace</th><th>Report Page</th><th>Docs Page</th></tr><tr><td>hyperdrive-experiment</td><td>quick-starts-ws-137441</td><td><a href=\"https://ml.azure.com/experiments/hyperdrive-experiment?wsid=/subscriptions/9b72f9e6-56c5-4c16-991b-19c652994860/resourcegroups/aml-quickstarts-137441/workspaces/quick-starts-ws-137441\" target=\"_blank\" rel=\"noopener\">Link to Azure Machine Learning studio</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment.Experiment?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
      ],
      "text/plain": [
       "Experiment(Name: hyperdrive-experiment,\n",
       "Workspace: quick-starts-ws-137441)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ws = Workspace.from_config()\n",
    "experiment_name = 'hyperdrive-experiment'\n",
    "experiment=Experiment(ws, experiment_name)\n",
    "experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Create Compute Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "cpu_cluster_name = \"cpu-cluster\"\n",
    "vm_size='STANDARD_D2_V2'\n",
    "\n",
    "# Verify that cluster does not exist already\n",
    "try:\n",
    "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
    "    print('Found existing cluster, use it.')\n",
    "except:\n",
    "    compute_config = AmlCompute.provisioning_configuration(vm_size=vm_size, max_nodes=4)\n",
    "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
    "\n",
    "# Can poll for a minimum number of nodes and for a specific timeout. \n",
    "# If no min node count is provided it uses the scale settings for the cluster.\n",
    "compute_target.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(569, 30)\n",
      "['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n",
      " 'mean smoothness' 'mean compactness' 'mean concavity'\n",
      " 'mean concave points' 'mean symmetry' 'mean fractal dimension'\n",
      " 'radius error' 'texture error' 'perimeter error' 'area error'\n",
      " 'smoothness error' 'compactness error' 'concavity error'\n",
      " 'concave points error' 'symmetry error' 'fractal dimension error'\n",
      " 'worst radius' 'worst texture' 'worst perimeter' 'worst area'\n",
      " 'worst smoothness' 'worst compactness' 'worst concavity'\n",
      " 'worst concave points' 'worst symmetry' 'worst fractal dimension']\n",
      ".. _breast_cancer_dataset:\n",
      "\n",
      "Breast cancer wisconsin (diagnostic) dataset\n",
      "--------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 569\n",
      "\n",
      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
      "\n",
      "    :Attribute Information:\n",
      "        - radius (mean of distances from center to points on the perimeter)\n",
      "        - texture (standard deviation of gray-scale values)\n",
      "        - perimeter\n",
      "        - area\n",
      "        - smoothness (local variation in radius lengths)\n",
      "        - compactness (perimeter^2 / area - 1.0)\n",
      "        - concavity (severity of concave portions of the contour)\n",
      "        - concave points (number of concave portions of the contour)\n",
      "        - symmetry \n",
      "        - fractal dimension (\"coastline approximation\" - 1)\n",
      "\n",
      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
      "        largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 3 is Mean Radius, field\n",
      "        13 is Radius SE, field 23 is Worst Radius.\n",
      "\n",
      "        - class:\n",
      "                - WDBC-Malignant\n",
      "                - WDBC-Benign\n",
      "\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ===================================== ====== ======\n",
      "                                           Min    Max\n",
      "    ===================================== ====== ======\n",
      "    radius (mean):                        6.981  28.11\n",
      "    texture (mean):                       9.71   39.28\n",
      "    perimeter (mean):                     43.79  188.5\n",
      "    area (mean):                          143.5  2501.0\n",
      "    smoothness (mean):                    0.053  0.163\n",
      "    compactness (mean):                   0.019  0.345\n",
      "    concavity (mean):                     0.0    0.427\n",
      "    concave points (mean):                0.0    0.201\n",
      "    symmetry (mean):                      0.106  0.304\n",
      "    fractal dimension (mean):             0.05   0.097\n",
      "    radius (standard error):              0.112  2.873\n",
      "    texture (standard error):             0.36   4.885\n",
      "    perimeter (standard error):           0.757  21.98\n",
      "    area (standard error):                6.802  542.2\n",
      "    smoothness (standard error):          0.002  0.031\n",
      "    compactness (standard error):         0.002  0.135\n",
      "    concavity (standard error):           0.0    0.396\n",
      "    concave points (standard error):      0.0    0.053\n",
      "    symmetry (standard error):            0.008  0.079\n",
      "    fractal dimension (standard error):   0.001  0.03\n",
      "    radius (worst):                       7.93   36.04\n",
      "    texture (worst):                      12.02  49.54\n",
      "    perimeter (worst):                    50.41  251.2\n",
      "    area (worst):                         185.2  4254.0\n",
      "    smoothness (worst):                   0.071  0.223\n",
      "    compactness (worst):                  0.027  1.058\n",
      "    concavity (worst):                    0.0    1.252\n",
      "    concave points (worst):               0.0    0.291\n",
      "    symmetry (worst):                     0.156  0.664\n",
      "    fractal dimension (worst):            0.055  0.208\n",
      "    ===================================== ====== ======\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
      "\n",
      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
      "\n",
      "    :Donor: Nick Street\n",
      "\n",
      "    :Date: November, 1995\n",
      "\n",
      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
      "https://goo.gl/U2Uwz2\n",
      "\n",
      "Features are computed from a digitized image of a fine needle\n",
      "aspirate (FNA) of a breast mass.  They describe\n",
      "characteristics of the cell nuclei present in the image.\n",
      "\n",
      "Separating plane described above was obtained using\n",
      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
      "pp. 97-101, 1992], a classification method which uses linear\n",
      "programming to construct a decision tree.  Relevant features\n",
      "were selected using an exhaustive search in the space of 1-4\n",
      "features and 1-3 separating planes.\n",
      "\n",
      "The actual linear program used to obtain the separating plane\n",
      "in the 3-dimensional space is that described in:\n",
      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
      "Optimization Methods and Software 1, 1992, 23-34].\n",
      "\n",
      "This database is also available through the UW CS ftp server:\n",
      "\n",
      "ftp ftp.cs.wisc.edu\n",
      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
      "     San Jose, CA, 1993.\n",
      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
      "     July-August 1995.\n",
      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
      "     163-171.\n"
     ]
    }
   ],
   "source": [
    "data = datasets.load_breast_cancer()\n",
    "print(data.data.shape)\n",
    "print(data.feature_names)\n",
    "print(data.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.627417\n",
       "0    0.372583\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(data.target).value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
       "0                 0.07871  ...          17.33           184.60      2019.0   \n",
       "1                 0.05667  ...          23.41           158.80      1956.0   \n",
       "2                 0.05999  ...          25.53           152.50      1709.0   \n",
       "3                 0.09744  ...          26.50            98.87       567.7   \n",
       "4                 0.05883  ...          16.67           152.20      1575.0   \n",
       "\n",
       "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   worst symmetry  worst fractal dimension  target  \n",
       "0          0.4601                  0.11890       0  \n",
       "1          0.2750                  0.08902       0  \n",
       "2          0.3613                  0.08758       0  \n",
       "3          0.6638                  0.17300       0  \n",
       "4          0.2364                  0.07678       0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data.data, columns = data.feature_names)\n",
    "df['target']=data.target\n",
    "df.head(5)"
   ]
  },
  {
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   "source": [
    "## Hyperdrive Configuration\n",
    "\n",
    "### Why Bayesian Sampling: \n",
    "\n",
    "Bayesian sampling is based on the Bayesian optimization algorithm. It picks samples based on how previous samples performed, so that new samples improve the primary metric.\n",
    "\n",
    "Bayesian sampling is recommended if you have enough budget to explore the hyperparameter space. For best results, we recommend a maximum number of runs greater than or equal to 20 times the number of hyperparameters being tuned.\n",
    "\n",
    "The number of concurrent runs has an impact on the effectiveness of the tuning process. A smaller number of concurrent runs may lead to better sampling convergence, since the smaller degree of parallelism increases the number of runs that benefit from previously completed runs.\n",
    "\n",
    "### Early Stopping: \n",
    "Early stopping Policy is not implemented for Bayesian Sampling is not implemented for Hyperdrive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:azureml.train.sklearn:'SKLearn' estimator is deprecated. Please use 'ScriptRunConfig' from 'azureml.core.script_run_config' with your own defined environment or the AzureML-Tutorial curated environment.\n"
     ]
    }
   ],
   "source": [
    "# Specify parameter sampler\n",
    "param_sampling = BayesianParameterSampling(\n",
    "    parameter_space ={\n",
    "        '--n_estimators' : choice(1,10,20,50,100,200,500),\n",
    "        '--max_depth': choice(1, 5, 10, 20, 30, 50, 100),\n",
    "        '--learning_rate': choice(1, 0.1, 0.01, 0.001)\n",
    "        }\n",
    ")\n",
    "\n",
    "if \"training\" not in os.listdir():\n",
    "    os.mkdir(\"./training\")\n",
    "\n",
    "# Create a SKLearn estimator for use with train.py\n",
    "estimator = SKLearn(source_directory = \"./\",\n",
    "            compute_target=compute_target,\n",
    "            entry_script=\"train.py\")\n",
    "\n",
    "# Create a HyperDriveConfig using the estimator, hyperparameter sampler, and policy.\n",
    "hyperdrive_run_config = HyperDriveConfig(hyperparameter_sampling=param_sampling, \n",
    "                                     primary_metric_name='Accuracy',\n",
    "                                     primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n",
    "                                     estimator=estimator,\n",
    "                                     max_total_runs=80,\n",
    "                                     max_concurrent_runs=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
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     "logged": 1598544898497
    },
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     "source_hidden": false
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   },
   "source": [
    "## Run Details\n",
    "\n",
    "* We trained `GradientBoostingClassifier` model from sklearn with different values of parameters mentioned in above code. GradientBoosting based classifier has been shown significant results for many classfication problem. It is considered as powerful algorithm for classfication. It is build on top of DecisionTree Algorithm.\n",
    "\n",
    "* Model is suppose to give different results for all the combination of parameters. We will select best performing model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root: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": "856beac0d5d447e3be87c7e3b36f8ab9",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "_HyperDriveWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\"HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_50\", \"HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_56\", \"HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_56\"], \"metric_value\": [0.916083916083916, 0.9230769230769231, 0.958041958041958, 0.965034965034965, 0.9790209790209791, 0.9790209790209791], \"final\": [false, false, false, false, false, true]}]}]}], \"run_logs\": \"[2021-02-06T11:15:14.000735][API][INFO]Experiment created\\r\\n[2021-02-06T11:15:14.506990][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\r\\n[2021-02-06T11:15:14.794366][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:15:15.1344551Z][SCHEDULER][INFO]The execution environment is being prepared. Please be patient as it can take a few minutes.\\r\\n[2021-02-06T11:15:45.9378920Z][SCHEDULER][INFO]The execution environment was successfully prepared.\\r\\n[2021-02-06T11:15:45.9380189Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_0'\\r\\n[2021-02-06T11:15:45.9756270Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_3'\\r\\n[2021-02-06T11:15:45.9920913Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_2'\\r\\n[2021-02-06T11:15:45.9540980Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_1'\\r\\n[2021-02-06T11:15:46.5912500Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_1'\\r\\n[2021-02-06T11:15:47.0010603Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_0'\\r\\n[2021-02-06T11:15:47.0876425Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_3'\\r\\n[2021-02-06T11:15:48.3971499Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_2'\\r\\n[2021-02-06T11:21:45.801182][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\r\\n[2021-02-06T11:21:46.108896][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:21:56.2798214Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_4'\\r\\n[2021-02-06T11:21:56.2827682Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_6'\\r\\n[2021-02-06T11:21:56.2815983Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_5'\\r\\n[2021-02-06T11:21:56.8814254Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_5'\\r\\n[2021-02-06T11:21:56.2986628Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_7'\\r\\n[2021-02-06T11:21:57.3580976Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_6'\\r\\n[2021-02-06T11:21:57.8332879Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_7'\\r\\n[2021-02-06T11:21:59.8386247Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_4'\\r\\n[2021-02-06T11:22:46.014754][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\r\\n[2021-02-06T11:22:46.921477][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:23:01.4180642Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_8'\\r\\n[2021-02-06T11:23:01.4239439Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_9'\\r\\n[2021-02-06T11:23:01.4226720Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_11'\\r\\n[2021-02-06T11:23:01.4233261Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_10'\\r\\n[2021-02-06T11:23:01.9639933Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_11'\\r\\n[2021-02-06T11:23:02.0724208Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_8'\\r\\n[2021-02-06T11:23:02.0711321Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_9'\\r\\n[2021-02-06T11:23:03.2497839Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_10'\\r\\n[2021-02-06T11:23:48.360012][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2021-02-06T11:23:49.892607][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:24:04.6443252Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_13'\\r\\n[2021-02-06T11:24:04.6431684Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_12'\\r\\n[2021-02-06T11:24:05.2856534Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_12'\\r\\n[2021-02-06T11:24:06.1118068Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_13'\\r\\n[2021-02-06T11:24:20.302253][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2021-02-06T11:24:21.909631][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:24:36.9215151Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_14'\\r\\n[2021-02-06T11:24:36.9227430Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_15'\\r\\n[2021-02-06T11:24:37.8502941Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_15'\\r\\n[2021-02-06T11:24:38.0434764Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_14'\\r\\n[2021-02-06T11:24:52.316739][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2021-02-06T11:24:53.384818][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:25:08.9114092Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_16'\\r\\n[2021-02-06T11:25:08.9132325Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_17'\\r\\n[2021-02-06T11:25:09.7953882Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_16'\\r\\n[2021-02-06T11:25:09.9569906Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_17'\\r\\n[2021-02-06T11:25:55.371010][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2021-02-06T11:25:58.364805][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:26:11.9959414Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_20'\\r\\n[2021-02-06T11:26:11.9965546Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_19'\\r\\n[2021-02-06T11:26:12.0124211Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_18'\\r\\n[2021-02-06T11:26:12.8722183Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_18'\\r\\n[2021-02-06T11:26:13.1012215Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_20'\\r\\n[2021-02-06T11:26:13.1199666Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_19'\\r\\n[2021-02-06T11:26:29.175656][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-02-06T11:26:38.081240][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:26:44.1948701Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_21'\\r\\n[2021-02-06T11:26:45.5439951Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_21'\\r\\n[2021-02-06T11:27:08.445420][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2021-02-06T11:27:12.322335][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:27:16.4061369Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_24'\\r\\n[2021-02-06T11:27:16.4169942Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_22'\\r\\n[2021-02-06T11:27:16.4067636Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_23'\\r\\n[2021-02-06T11:27:16.8556980Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_22'\\r\\n[2021-02-06T11:27:17.3482906Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_23'\\r\\n[2021-02-06T11:27:17.5882421Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_24'\\r\\n[2021-02-06T11:27:43.529863][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-02-06T11:27:48.065387][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:28:18.381559][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2021-02-06T11:28:20.0782128Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_25'\\r\\n[2021-02-06T11:28:20.7836281Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_25'\\r\\n[2021-02-06T11:28:28.007191][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:28:51.7773400Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_26'\\r\\n[2021-02-06T11:28:51.7935615Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_28'\\r\\n[2021-02-06T11:28:51.7786708Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_27'\\r\\n[2021-02-06T11:28:52.3514164Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_27'\\r\\n[2021-02-06T11:28:52.6934280Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_26'\\r\\n[2021-02-06T11:28:52.9679477Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_28'\\r\\n[2021-02-06T11:29:29.760603][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-02-06T11:29:35.634645][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:29:56.4663933Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_29'\\r\\n[2021-02-06T11:29:58.0856705Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_29'\\r\\n[2021-02-06T11:30:06.591614][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2021-02-06T11:30:16.480554][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:30:29.2622326Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_31'\\r\\n[2021-02-06T11:30:29.2611177Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_30'\\r\\n[2021-02-06T11:30:29.2632615Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_32'\\r\\n[2021-02-06T11:30:29.9906477Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_31'\\r\\n[2021-02-06T11:30:30.1532546Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_30'\\r\\n[2021-02-06T11:30:30.5516611Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_32'\\r\\n[2021-02-06T11:30:47.881481][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-02-06T11:30:52.163047][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:31:02.1839110Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_33'\\r\\n[2021-02-06T11:31:03.3827212Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_33'\\r\\n[2021-02-06T11:31:23.664141][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2021-02-06T11:31:32.794776][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:31:34.6835240Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_35'\\r\\n[2021-02-06T11:31:34.6822151Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_34'\\r\\n[2021-02-06T11:31:34.6848832Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_36'\\r\\n[2021-02-06T11:31:35.3373596Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_36'\\r\\n[2021-02-06T11:31:35.6246849Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_34'\\r\\n[2021-02-06T11:31:35.7965713Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_35'\\r\\n[2021-02-06T11:32:04.539735][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-02-06T11:32:08.540159][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:32:38.7174553Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_37'\\r\\n[2021-02-06T11:32:39.4511523Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_37'\\r\\n[2021-02-06T11:32:40.150275][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2021-02-06T11:32:43.733975][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:33:11.0158906Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_38'\\r\\n[2021-02-06T11:33:11.0345945Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_40'\\r\\n[2021-02-06T11:33:11.0170089Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_39'\\r\\n[2021-02-06T11:33:11.7625950Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_38'\\r\\n[2021-02-06T11:33:12.0990681Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_39'\\r\\n[2021-02-06T11:33:12.4864108Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_40'\\r\\n[2021-02-06T11:33:46.181308][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-02-06T11:33:49.704125][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:34:15.9757502Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_41'\\r\\n[2021-02-06T11:34:16.7383945Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_41'\\r\\n[2021-02-06T11:34:21.087081][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2021-02-06T11:34:24.751842][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:34:48.3490697Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_44'\\r\\n[2021-02-06T11:34:48.3461021Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_42'\\r\\n[2021-02-06T11:34:48.3475418Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_43'\\r\\n[2021-02-06T11:34:49.2215056Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_43'\\r\\n[2021-02-06T11:34:49.3517289Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_44'\\r\\n[2021-02-06T11:34:49.4113623Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_42'\\r\\n[2021-02-06T11:35:28.016674][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-02-06T11:35:30.771026][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:35:52.9702573Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_45'\\r\\n[2021-02-06T11:35:54.3713523Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_45'\\r\\n[2021-02-06T11:36:02.063727][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2021-02-06T11:36:09.411207][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:36:26.0157967Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_46'\\r\\n[2021-02-06T11:36:26.0214876Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_48'\\r\\n[2021-02-06T11:36:26.0171625Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_47'\\r\\n[2021-02-06T11:36:26.9608814Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_47'\\r\\n[2021-02-06T11:36:26.9791159Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_48'\\r\\n[2021-02-06T11:36:27.5212844Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_46'\\r\\n[2021-02-06T11:36:41.331526][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-02-06T11:36:45.679075][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:36:59.4459295Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_49'\\r\\n[2021-02-06T11:37:00.9697023Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_49'\\r\\n[2021-02-06T11:37:17.470612][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2021-02-06T11:37:25.902044][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:37:33.0929622Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_52'\\r\\n[2021-02-06T11:37:33.1325002Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_50'\\r\\n[2021-02-06T11:37:33.1198565Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_51'\\r\\n[2021-02-06T11:37:33.8787136Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_50'\\r\\n[2021-02-06T11:37:34.2965900Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_52'\\r\\n[2021-02-06T11:37:34.5087548Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_51'\\r\\n[2021-02-06T11:37:57.536393][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-02-06T11:38:02.653867][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:38:06.4566249Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_53'\\r\\n[2021-02-06T11:38:07.6539747Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_53'\\r\\n[2021-02-06T11:38:35.238983][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2021-02-06T11:38:42.178694][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:39:12.9334166Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_55'\\r\\n[2021-02-06T11:39:12.9446534Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_56'\\r\\n[2021-02-06T11:39:12.9323299Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_54'\\r\\n[2021-02-06T11:39:13.4425345Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_55'\\r\\n[2021-02-06T11:39:13.597845][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-02-06T11:39:13.8057494Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_56'\\r\\n[2021-02-06T11:39:14.2210143Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_54'\\r\\n[2021-02-06T11:39:21.384736][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:39:46.2368995Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_57'\\r\\n[2021-02-06T11:39:46.7236770Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_57'\\r\\n[2021-02-06T11:40:25.332959][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2021-02-06T11:40:36.366980][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:40:52.6850175Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_59'\\r\\n[2021-02-06T11:40:52.6861844Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_60'\\r\\n[2021-02-06T11:40:52.6838242Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_58'\\r\\n[2021-02-06T11:40:53.5205697Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_59'\\r\\n[2021-02-06T11:40:53.5599132Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_58'\\r\\n[2021-02-06T11:40:54.1984040Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_60'\\r\\n[2021-02-06T11:41:08.394465][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-02-06T11:41:15.360533][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:41:26.6022239Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_61'\\r\\n[2021-02-06T11:41:27.9030040Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_61'\\r\\n[2021-02-06T11:41:47.377644][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2021-02-06T11:41:54.355601][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:42:00.1434428Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_62'\\r\\n[2021-02-06T11:42:00.1451679Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_63'\\r\\n[2021-02-06T11:42:00.6256251Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_63'\\r\\n[2021-02-06T11:42:01.0262375Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_62'\\r\\n[2021-02-06T11:42:26.716908][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2021-02-06T11:42:30.635284][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:42:33.5300500Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_64'\\r\\n[2021-02-06T11:42:33.5293860Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_65'\\r\\n[2021-02-06T11:42:34.4418604Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_64'\\r\\n[2021-02-06T11:42:34.5620141Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_65'\\r\\n[2021-02-06T11:43:02.631619][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2021-02-06T11:43:10.008095][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:43:40.0827654Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_66'\\r\\n[2021-02-06T11:43:40.0821093Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_67'\\r\\n[2021-02-06T11:43:40.9192997Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_67'\\r\\n[2021-02-06T11:43:41.0327892Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_66'\\r\\n[2021-02-06T11:43:41.824349][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2021-02-06T11:43:45.822565][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:44:14.8285210Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_68'\\r\\n[2021-02-06T11:44:14.8394173Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_69'\\r\\n[2021-02-06T11:44:16.4442193Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_68'\\r\\n[2021-02-06T11:44:16.7552905Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_69'\\r\\n[2021-02-06T11:44:50.7544661Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_18'\\r\\n[2021-02-06T11:44:50.4261297Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_18'\\r\\n[2021-02-06T11:44:51.178936][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2021-02-06T11:45:05.976211][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:45:23.4621994Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_60'\\r\\n[2021-02-06T11:45:23.4632910Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_70'\\r\\n[2021-02-06T11:45:23.4661194Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_71'\\r\\n[2021-02-06T11:45:23.7127566Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_60'\\r\\n[2021-02-06T11:45:24.2413351Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_70'\\r\\n[2021-02-06T11:45:25.2468618Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_71'\\r\\n[2021-02-06T11:45:38.129736][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2021-02-06T11:45:43.555611][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:45:58.0080739Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_72'\\r\\n[2021-02-06T11:45:58.0094446Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_73'\\r\\n[2021-02-06T11:45:58.5373224Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_72'\\r\\n[2021-02-06T11:45:59.0322057Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_73'\\r\\n[2021-02-06T11:46:16.675912][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2021-02-06T11:46:23.328571][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:46:32.0469434Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_74'\\r\\n[2021-02-06T11:46:32.0458112Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_18'\\r\\n[2021-02-06T11:46:32.0482888Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_75'\\r\\n[2021-02-06T11:46:32.3129975Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_18'\\r\\n[2021-02-06T11:46:32.8099273Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_75'\\r\\n[2021-02-06T11:46:33.7592161Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_74'\\r\\n[2021-02-06T11:46:56.205986][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2021-02-06T11:47:02.014239][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:47:06.4575635Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_76'\\r\\n[2021-02-06T11:47:06.4569757Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_77'\\r\\n[2021-02-06T11:47:07.3824588Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_77'\\r\\n[2021-02-06T11:47:07.4631913Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_76'\\r\\n[2021-02-06T11:47:34.202837][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2021-02-06T11:47:44.841581][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-06T11:48:14.0243681Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_79'\\r\\n[2021-02-06T11:48:14.0251048Z][SCHEDULER][INFO]Scheduling job, id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_78'\\r\\n[2021-02-06T11:48:14.5413933Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_78'\\r\\n[2021-02-06T11:48:15.0096953Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_79'\\r\\n[2021-02-06T11:48:17.368890][GENERATOR][INFO]Max number of jobs '80' reached for experiment.\\r\\n[2021-02-06T11:48:17.685019][GENERATOR][INFO]All jobs generated.\\r\\n[2021-02-06T11:50:07.203355][CONTROLLER][INFO]Experiment was 'ExperimentStatus.RUNNING', is 'ExperimentStatus.FINISHED'.\\n\\nRun is completed.\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.20.0\"}, \"loading\": false}"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Submit your hyperdrive run to the experiment and show run details with the widget.\n",
    "\n",
    "# Start the HyperDrive run\n",
    "hyperdrive_run = experiment.submit(hyperdrive_run_config)\n",
    "\n",
    "# Monitor HyperDrive runs You can monitor the progress of the runs with the following Jupyter widget\n",
    "RunDetails(hyperdrive_run).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RunId: HD_0012b2d3-6a85-4b13-b71b-51329253e8b6\n",
      "Web View: https://ml.azure.com/experiments/hyperdrive-experiment/runs/HD_0012b2d3-6a85-4b13-b71b-51329253e8b6?wsid=/subscriptions/9b72f9e6-56c5-4c16-991b-19c652994860/resourcegroups/aml-quickstarts-137441/workspaces/quick-starts-ws-137441\n",
      "\n",
      "Streaming azureml-logs/hyperdrive.txt\n",
      "=====================================\n",
      "\n",
      "\"<START>[2021-02-06T11:15:14.000735][API][INFO]Experiment created<END>\\n\"\"<START>[2021-02-06T11:15:14.506990][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space<END>\\n\"\"<START>[2021-02-06T11:15:14.794366][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.<END>\\n\"<START>[2021-02-06T11:15:15.1344551Z][SCHEDULER][INFO]The execution environment is being prepared. Please be patient as it can take a few minutes.<END>\n",
      "\n",
      "Execution Summary\n",
      "=================\n",
      "RunId: HD_0012b2d3-6a85-4b13-b71b-51329253e8b6\n",
      "Web View: https://ml.azure.com/experiments/hyperdrive-experiment/runs/HD_0012b2d3-6a85-4b13-b71b-51329253e8b6?wsid=/subscriptions/9b72f9e6-56c5-4c16-991b-19c652994860/resourcegroups/aml-quickstarts-137441/workspaces/quick-starts-ws-137441\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'runId': 'HD_0012b2d3-6a85-4b13-b71b-51329253e8b6',\n",
       " 'target': 'cpu-cluster',\n",
       " 'status': 'Completed',\n",
       " 'startTimeUtc': '2021-02-06T11:15:13.873747Z',\n",
       " 'endTimeUtc': '2021-02-06T11:50:07.024471Z',\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': 'ecd88f96-9e30-4de6-864d-7cf857ca4da9',\n",
       "  'score': '0.9790209790209791',\n",
       "  'best_child_run_id': 'HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_56',\n",
       "  'best_metric_status': 'Succeeded'},\n",
       " 'inputDatasets': [],\n",
       " 'outputDatasets': [],\n",
       " 'logFiles': {'azureml-logs/hyperdrive.txt': 'https://mlstrg137441.blob.core.windows.net/azureml/ExperimentRun/dcid.HD_0012b2d3-6a85-4b13-b71b-51329253e8b6/azureml-logs/hyperdrive.txt?sv=2019-02-02&sr=b&sig=4SxyBrRT5RK0YR%2Ftm2mbLqq1bzE4MrHgQNuauryA4U4%3D&st=2021-02-06T11%3A40%3A14Z&se=2021-02-06T19%3A50%3A14Z&sp=r'},\n",
       " 'submittedBy': 'ODL_User 137441'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hyperdrive_run.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best Model\n",
    "\n",
    "Get the best model from the hyperdrive experiments and display all the properties of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "gather": {
     "logged": 1598546650307
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Run Id:  HD_0012b2d3-6a85-4b13-b71b-51329253e8b6_56\n",
      "Accuracy: 0.9790209790209791\n",
      "learning_rate: 0.1\n",
      "max_depth: 1\n",
      "n_estimators: 500\n"
     ]
    }
   ],
   "source": [
    "from azureml.core.model import Model\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "best_run = hyperdrive_run.get_best_run_by_primary_metric()\n",
    "best_run_metrics = best_run.get_metrics()['Accuracy']\n",
    "parameter_values = best_run.get_details()['runDefinition']['arguments']\n",
    "\n",
    "print('Best Run Id: ', best_run.id)\n",
    "print('Accuracy:', best_run_metrics)\n",
    "print('learning_rate:',parameter_values[1])\n",
    "print('max_depth:',parameter_values[3])\n",
    "print('n_estimators:',parameter_values[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['--learning_rate', '0.1', '--max_depth', '1', '--n_estimators', '500']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameter_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "gather": {
     "logged": 1598546657829
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [],
   "source": [
    "#TODO: Save the best model\n",
    "best_run.download_file(\"/outputs/model.joblib\", \"Hyperdrive.joblib\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "source": [
    "## Model Deployment\n",
    "\n",
    "Remember you have to deploy only one of the two models you trained.. Perform the steps in the rest of this notebook only if you wish to deploy this model.\n",
    "\n",
    "TODO: In the cell below, register the model, create an inference config and deploy the model as a web service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: In the cell below, send a request to the web service you deployed to test it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: In the cell below, print the logs of the web service and delete the service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
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