diabetes_regression_compute_target.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --upgrade azureml-sdk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failure while loading azureml_run_type_providers. Failed to load entrypoint hyperdrive = azureml.train.hyperdrive:HyperDriveRun._from_run_dto with exception (azureml-core 1.5.0.post4 (/anaconda/envs/azureml_py36/lib/python3.6/site-packages), Requirement.parse('azureml-core~=1.3.0')).\n",
      "Failure while loading azureml_run_type_providers. Failed to load entrypoint azureml.PipelineRun = azureml.pipeline.core.run:PipelineRun._from_dto with exception (azureml-core 1.5.0.post4 (/anaconda/envs/azureml_py36/lib/python3.6/site-packages), Requirement.parse('azureml-core~=1.3.0')).\n",
      "Failure while loading azureml_run_type_providers. Failed to load entrypoint azureml.ReusedStepRun = azureml.pipeline.core.run:StepRun._from_reused_dto with exception (azureml-core 1.5.0.post4 (/anaconda/envs/azureml_py36/lib/python3.6/site-packages), Requirement.parse('azureml-core~=1.3.0')).\n",
      "Failure while loading azureml_run_type_providers. Failed to load entrypoint azureml.StepRun = azureml.pipeline.core.run:StepRun._from_dto with exception (azureml-core 1.5.0.post4 (/anaconda/envs/azureml_py36/lib/python3.6/site-packages), Requirement.parse('azureml-core~=1.3.0')).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Azure ML SDK Version:  1.5.0\n"
     ]
    }
   ],
   "source": [
    "import azureml.core\n",
    "from azureml.core import Workspace\n",
    "\n",
    "# check core SDK version number\n",
    "print(\"Azure ML SDK Version: \", azureml.core.VERSION)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Workspace.create(name='eacbmlservicews', subscription_id='f80606e5-788f-4dc3-a9ea-2eb9a7836082', resource_group='adlsgen2')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load workspace configuration from the config.json file in the current folder.\n",
    "ws = Workspace.from_config()\n",
    "\n",
    "ws\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>diabetes_dxd</td><td>eacbmlservicews</td><td><a href=\"https://ml.azure.com/experiments/diabetes_dxd?wsid=/subscriptions/f80606e5-788f-4dc3-a9ea-2eb9a7836082/resourcegroups/adlsgen2/workspaces/eacbmlservicews\" 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: diabetes_dxd,\n",
       "Workspace: eacbmlservicews)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment_name = 'diabetes_dxd'\n",
    "\n",
    "from azureml.core import Experiment\n",
    "exp = Experiment(workspace=ws, name=experiment_name)\n",
    "\n",
    "exp\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upload dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Dataset\n",
    "\n",
    "diabetes_dataset = Dataset.get_by_name(ws, name='diabetes')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes = diabetes_dataset.to_pandas_dataframe().drop(\"Path\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BP</th>\n",
       "      <th>S1</th>\n",
       "      <th>S2</th>\n",
       "      <th>S3</th>\n",
       "      <th>S4</th>\n",
       "      <th>S5</th>\n",
       "      <th>S6</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>32.1</td>\n",
       "      <td>101.0</td>\n",
       "      <td>157</td>\n",
       "      <td>93.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.8598</td>\n",
       "      <td>87</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>21.6</td>\n",
       "      <td>87.0</td>\n",
       "      <td>183</td>\n",
       "      <td>103.2</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.8918</td>\n",
       "      <td>69</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "      <td>30.5</td>\n",
       "      <td>93.0</td>\n",
       "      <td>156</td>\n",
       "      <td>93.6</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.6728</td>\n",
       "      <td>85</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>25.3</td>\n",
       "      <td>84.0</td>\n",
       "      <td>198</td>\n",
       "      <td>131.4</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.8903</td>\n",
       "      <td>89</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>192</td>\n",
       "      <td>125.4</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.2905</td>\n",
       "      <td>80</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE  SEX   BMI     BP   S1     S2    S3   S4      S5  S6    Y\n",
       "0   59    2  32.1  101.0  157   93.2  38.0  4.0  4.8598  87  151\n",
       "1   48    1  21.6   87.0  183  103.2  70.0  3.0  3.8918  69   75\n",
       "2   72    2  30.5   93.0  156   93.6  41.0  4.0  4.6728  85  141\n",
       "3   24    1  25.3   84.0  198  131.4  40.0  5.0  4.8903  89  206\n",
       "4   50    1  23.0  101.0  192  125.4  52.0  4.0  4.2905  80  135"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 442 entries, 0 to 441\n",
      "Data columns (total 11 columns):\n",
      "AGE    442 non-null int64\n",
      "SEX    442 non-null int64\n",
      "BMI    442 non-null float64\n",
      "BP     442 non-null float64\n",
      "S1     442 non-null int64\n",
      "S2     442 non-null float64\n",
      "S3     442 non-null float64\n",
      "S4     442 non-null float64\n",
      "S5     442 non-null float64\n",
      "S6     442 non-null int64\n",
      "Y      442 non-null int64\n",
      "dtypes: float64(6), int64(5)\n",
      "memory usage: 38.1 KB\n"
     ]
    }
   ],
   "source": [
    "diabetes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BP</th>\n",
       "      <th>S1</th>\n",
       "      <th>S2</th>\n",
       "      <th>S3</th>\n",
       "      <th>S4</th>\n",
       "      <th>S5</th>\n",
       "      <th>S6</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>48.518100</td>\n",
       "      <td>1.468326</td>\n",
       "      <td>26.375792</td>\n",
       "      <td>94.647014</td>\n",
       "      <td>189.140271</td>\n",
       "      <td>115.439140</td>\n",
       "      <td>49.788462</td>\n",
       "      <td>4.070249</td>\n",
       "      <td>4.641411</td>\n",
       "      <td>91.260181</td>\n",
       "      <td>152.133484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.109028</td>\n",
       "      <td>0.499561</td>\n",
       "      <td>4.418122</td>\n",
       "      <td>13.831283</td>\n",
       "      <td>34.608052</td>\n",
       "      <td>30.413081</td>\n",
       "      <td>12.934202</td>\n",
       "      <td>1.290450</td>\n",
       "      <td>0.522391</td>\n",
       "      <td>11.496335</td>\n",
       "      <td>77.093005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>41.600000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.258100</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>164.250000</td>\n",
       "      <td>96.050000</td>\n",
       "      <td>40.250000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.276700</td>\n",
       "      <td>83.250000</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.620050</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>140.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.275000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>209.750000</td>\n",
       "      <td>134.500000</td>\n",
       "      <td>57.750000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.997200</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>211.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>42.200000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>242.400000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>9.090000</td>\n",
       "      <td>6.107000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>346.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              AGE         SEX         BMI          BP          S1          S2  \\\n",
       "count  442.000000  442.000000  442.000000  442.000000  442.000000  442.000000   \n",
       "mean    48.518100    1.468326   26.375792   94.647014  189.140271  115.439140   \n",
       "std     13.109028    0.499561    4.418122   13.831283   34.608052   30.413081   \n",
       "min     19.000000    1.000000   18.000000   62.000000   97.000000   41.600000   \n",
       "25%     38.250000    1.000000   23.200000   84.000000  164.250000   96.050000   \n",
       "50%     50.000000    1.000000   25.700000   93.000000  186.000000  113.000000   \n",
       "75%     59.000000    2.000000   29.275000  105.000000  209.750000  134.500000   \n",
       "max     79.000000    2.000000   42.200000  133.000000  301.000000  242.400000   \n",
       "\n",
       "               S3          S4          S5          S6           Y  \n",
       "count  442.000000  442.000000  442.000000  442.000000  442.000000  \n",
       "mean    49.788462    4.070249    4.641411   91.260181  152.133484  \n",
       "std     12.934202    1.290450    0.522391   11.496335   77.093005  \n",
       "min     22.000000    2.000000    3.258100   58.000000   25.000000  \n",
       "25%     40.250000    3.000000    4.276700   83.250000   87.000000  \n",
       "50%     48.000000    4.000000    4.620050   91.000000  140.500000  \n",
       "75%     57.750000    5.000000    4.997200   98.000000  211.500000  \n",
       "max     99.000000    9.090000    6.107000  124.000000  346.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_names = ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
    "target = \"Y\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a (remote) target compute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Cluster already exists\n",
      "Succeeded\n",
      "AmlCompute wait for completion finished\n",
      "\n",
      "Minimum number of nodes requested have been provisioned\n"
     ]
    }
   ],
   "source": [
    "# Compute target creation\n",
    "\n",
    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
    "from azureml.core.compute_target import ComputeTargetException\n",
    "\n",
    "cpu_cluster_name = \"myComputeCluster\"\n",
    "\n",
    "# Verify that cluster does not exist already\n",
    "try:\n",
    "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
    "    print(\" Cluster already exists\")\n",
    "except ComputeTargetException:\n",
    "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
    "                                                           min_nodes=0, max_nodes=4)\n",
    "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
    "\n",
    "cpu_cluster.wait_for_completion(show_output=True, min_node_count=0, timeout_in_minutes=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Succeeded\n"
     ]
    }
   ],
   "source": [
    "# Retrieve existing compute target\n",
    "\n",
    "from azureml.core.compute import ComputeTarget\n",
    "\n",
    "\n",
    "compute_target_name = \"myComputeCluster\"\n",
    "compute_target = ComputeTarget(workspace=ws, name=compute_target_name)\n",
    "\n",
    "print(compute_target.provisioning_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model on a remote target compute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "script_folder = os.path.join(os.getcwd(), \"remote_train\")\n",
    "os.makedirs(script_folder, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting /mnt/batch/tasks/shared/LS_root/mounts/clusters/computeinstance/code/Users/diabetes/remote_train/train.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile $script_folder/train.py\n",
    "\n",
    "import argparse\n",
    "import os\n",
    "import numpy as np\n",
    "import glob\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import Ridge\n",
    "import joblib\n",
    "import pickle\n",
    "\n",
    "from azureml.core import Run\n",
    "from azureml.core import Dataset\n",
    "from utils import load_data\n",
    "\n",
    "# get hold of the current run\n",
    "run = Run.get_context()\n",
    "exp = run.experiment\n",
    "ws = run.experiment.workspace\n",
    "\n",
    "parser = argparse.ArgumentParser()\n",
    "parser.add_argument('--regularization', type=float, dest='reg', default=0.5, help='regularization strength')\n",
    "args = parser.parse_args()\n",
    "\n",
    "# load train and test set into numpy arrays\n",
    "diabetes_dataset = Dataset.get_by_name(ws, name='diabetes')\n",
    "diabetes = diabetes_dataset.to_pandas_dataframe().drop(\"Path\", axis=1)\n",
    "target = \"Y\"\n",
    "X = diabetes.drop(target, axis=1)\n",
    "y = diabetes[\"Y\"].values.reshape(-1,1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "print('Train a Ridge regression model with regularization strength of', args.reg)\n",
    "model = Ridge(alpha=args.reg, solver=\"auto\", random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "print('Predict the test set')\n",
    "y_hat = model.predict(X_test)\n",
    "\n",
    "# calculate score on the prediction\n",
    "score = model.score(X_test, y_test)\n",
    "print('Score is ', score)\n",
    "\n",
    "run.log('regularization strength', np.float(args.reg))\n",
    "run.log('score', np.float(score))\n",
    "\n",
    "os.makedirs('outputs', exist_ok=True)\n",
    "# note file saved in the outputs folder is automatically uploaded into experiment record\n",
    "joblib.dump(value=model, filename='outputs/diabetes_reg_remote_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/mnt/batch/tasks/shared/LS_root/mounts/clusters/computeinstance/code/Users/diabetes/remote_train/utils.py'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import shutil\n",
    "shutil.copy('utils.py', script_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'diabetes_env.yml'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set up the (compute target) environnement\n",
    "\n",
    "from azureml.core import Environment\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "\n",
    "env = Environment(\"diabetes_remote_env\")\n",
    "\n",
    "env.docker.enabled = True\n",
    "env.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn',\n",
    "                                                                          'pandas',\n",
    "                                                                          'numpy',\n",
    "                                                                          'joblib',                                                                        \n",
    "                                                                          'matplotlib'\n",
    "                                                                         ])\n",
    "env.python.conda_dependencies.add_pip_package(\"inference-schema[numpy-support]\")\n",
    "\n",
    "env.python.conda_dependencies.save_to_file(\".\", \"diabetes_env.yml\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.estimator import Estimator\n",
    "\n",
    "script_params = {\n",
    "    '--regularization': 0.5\n",
    "}\n",
    "\n",
    "estimator = Estimator(source_directory=script_folder,\n",
    "              script_params=script_params,\n",
    "              compute_target=cpu_cluster_name,\n",
    "              environment_definition=env,\n",
    "              entry_script='train.py')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"width:100%\"><tr><th>Experiment</th><th>Id</th><th>Type</th><th>Status</th><th>Details Page</th><th>Docs Page</th></tr><tr><td>diabetes_dxd</td><td>diabetes_dxd_1590994974_fc193c44</td><td>azureml.scriptrun</td><td>Starting</td><td><a href=\"https://ml.azure.com/experiments/diabetes_dxd/runs/diabetes_dxd_1590994974_fc193c44?wsid=/subscriptions/f80606e5-788f-4dc3-a9ea-2eb9a7836082/resourcegroups/adlsgen2/workspaces/eacbmlservicews\" 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.script_run.ScriptRun?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
      ],
      "text/plain": [
       "Run(Experiment: diabetes_dxd,\n",
       "Id: diabetes_dxd_1590994974_fc193c44,\n",
       "Type: azureml.scriptrun,\n",
       "Status: Starting)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run = exp.submit(config=estimator)\n",
    "run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "38af6667e66746dba15b47715423e9df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_UserRunWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', '…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/aml.mini.widget.v1": "{\"status\": \"Preparing\", \"workbench_run_details_uri\": \"https://ml.azure.com/experiments/diabetes_dxd/runs/diabetes_dxd_1590994974_fc193c44?wsid=/subscriptions/f80606e5-788f-4dc3-a9ea-2eb9a7836082/resourcegroups/adlsgen2/workspaces/eacbmlservicews\", \"run_id\": \"diabetes_dxd_1590994974_fc193c44\", \"run_properties\": {\"run_id\": \"diabetes_dxd_1590994974_fc193c44\", \"created_utc\": \"2020-06-01T07:03:00.261112Z\", \"properties\": {\"_azureml.ComputeTargetType\": \"amlcompute\", \"ContentSnapshotId\": \"e8f45a50-658c-430b-bfbe-f2a797d9e8bd\", \"azureml.git.dirty\": \"True\", \"AzureML.DerivedImageName\": \"azureml/azureml_a9bb5b2e7baa83080a7719e12de22936\"}, \"tags\": {}, \"script_name\": null, \"arguments\": null, \"end_time_utc\": null, \"status\": \"Preparing\", \"log_files\": {\"azureml-logs/20_image_build_log.txt\": \"https://eacbmlservicew2479569759.blob.core.windows.net/azureml/ExperimentRun/dcid.diabetes_dxd_1590994974_fc193c44/azureml-logs/20_image_build_log.txt?sv=2019-02-02&sr=b&sig=T%2BBjOSSa07nGHHKyolTWWlMr%2FZKUQje46bjlpIf627Y%3D&st=2020-06-01T06%3A58%3A10Z&se=2020-06-01T15%3A08%3A10Z&sp=r\"}, \"log_groups\": [[\"azureml-logs/20_image_build_log.txt\"]], \"run_duration\": \"0:05:09\"}, \"child_runs\": [], \"children_metrics\": {}, \"run_metrics\": [], \"run_logs\": \"2020/06/01 07:03:15 Downloading source code...\\r\\n2020/06/01 07:03:16 Finished downloading source code\\r\\n2020/06/01 07:03:17 Creating Docker network: acb_default_network, driver: 'bridge'\\n2020/06/01 07:03:17 Successfully set up Docker network: acb_default_network\\n2020/06/01 07:03:17 Setting up Docker configuration...\\n2020/06/01 07:03:18 Successfully set up Docker configuration\\n2020/06/01 07:03:18 Logging in to registry: eacbmlservicew8493899780.azurecr.io\\n2020/06/01 07:03:19 Successfully logged into eacbmlservicew8493899780.azurecr.io\\n2020/06/01 07:03:19 Executing step ID: acb_step_0. Timeout(sec): 5400, Working directory: '', Network: 'acb_default_network'\\n2020/06/01 07:03:19 Scanning for dependencies...\\r\\n2020/06/01 07:03:20 Successfully scanned dependencies\\n2020/06/01 07:03:20 Launching container with name: acb_step_0\\nSending build context to Docker daemon  60.93kB\\r\\r\\nStep 1/15 : FROM mcr.microsoft.com/azureml/base:intelmpi2018.3-ubuntu16.04@sha256:a1b514f3ba884b9a7695cbba5638933ddaf222e8ce3e8c81e8cdf861679abb05\\nsha256:a1b514f3ba884b9a7695cbba5638933ddaf222e8ce3e8c81e8cdf861679abb05: Pulling from azureml/base\\nDigest: sha256:a1b514f3ba884b9a7695cbba5638933ddaf222e8ce3e8c81e8cdf861679abb05\\nStatus: Downloaded newer image for mcr.microsoft.com/azureml/base:intelmpi2018.3-ubuntu16.04@sha256:a1b514f3ba884b9a7695cbba5638933ddaf222e8ce3e8c81e8cdf861679abb05\\n ---> 93a72e6bd1ce\\nStep 2/15 : USER root\\n ---> Running in 9fd08f8c05f8\\nRemoving intermediate container 9fd08f8c05f8\\n ---> 7991a3671d7b\\nStep 3/15 : RUN mkdir -p $HOME/.cache\\r\\n ---> Running in fd81b76ed97d\\nRemoving intermediate container fd81b76ed97d\\n ---> 46ef98f305a5\\nStep 4/15 : WORKDIR /\\n ---> Running in 3a3e3df5f7ae\\nRemoving intermediate container 3a3e3df5f7ae\\n ---> c7434c0932bd\\nStep 5/15 : COPY azureml-environment-setup/99brokenproxy /etc/apt/apt.conf.d/\\r\\n ---> 7fe498f32a83\\nStep 6/15 : RUN if dpkg --compare-versions `conda --version | grep -oE '[^ ]+$'` lt 4.4.11; then conda install conda==4.4.11; fi\\n ---> Running in dff024300d9d\\r\\nRemoving intermediate container dff024300d9d\\n ---> 9784087f854e\\nStep 7/15 : COPY azureml-environment-setup/mutated_conda_dependencies.yml azureml-environment-setup/mutated_conda_dependencies.yml\\r\\n ---> 8e3dd42a663e\\nStep 8/15 : RUN ldconfig /usr/local/cuda/lib64/stubs && conda env create -p /azureml-envs/azureml_022b8d8faf6253082d55b26d46e8a59c -f azureml-environment-setup/mutated_conda_dependencies.yml && rm -rf \\\"$HOME/.cache/pip\\\" && conda clean -aqy && CONDA_ROOT_DIR=$(conda info --root) && rm -rf \\\"$CONDA_ROOT_DIR/pkgs\\\" && find \\\"$CONDA_ROOT_DIR\\\" -type d -name __pycache__ -exec rm -rf {} + && ldconfig\\n ---> Running in 5f01422a6772\\nSolving environment: ...working... \\r\\ndone\\r\\n\\u001b[91m\\n\\n==> WARNING: A newer version of conda exists. <==\\n  current version: 4.5.11\\n  latest version: 4.8.3\\n\\nPlease update conda by running\\n\\n    $ conda update -n base -c defaults conda\\n\\n\\n\\rpcre-8.43            | 260 KB    |            |   0% \\u001b[0m\\u001b[91m\\rpcre-8.43            | 260 KB    | 4          |   5% \\u001b[0m\\u001b[91m\\rpcre-8.43            | 260 KB    | ########## | 100% \\u001b[0m\\u001b[91m\\n\\rlibuuid-1.0.3        | 16 KB     |            |   0% \\u001b[0m\\u001b[91m\\rlibuuid-1.0.3        | 16 KB     | ########## | 100% \\u001b[0m\\u001b[91m\\n\\rlibpng-1.6.37        | 364 KB    |            |   0% \\u001b[0m\\u001b[91m\\rlibpng-1.6.37        | 364 KB    | #######7   |  78% \\u001b[0m\\u001b[91m\\rlibpng-1.6.37        | 364 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requests>=2.17.3\\n  Downloading requests-2.23.0-py2.py3-none-any.whl (58 kB)\\nRequirement already satisfied: pandas>=0.20.2 in /azureml-envs/azureml_022b8d8faf6253082d55b26d46e8a59c/lib/python3.6/site-packages (from azureml-model-management-sdk==1.0.1b6.post1->azureml-defaults->-r /azureml-environment-setup/condaenv.8p5bp2mu.requirements.txt (line 1)) (1.0.3)\\nCollecting adal>=0.4.5\\n  Downloading adal-1.2.3-py2.py3-none-any.whl (53 kB)\\nCollecting liac-arff>=2.1.1\\n  Downloading liac-arff-2.4.0.tar.gz (15 kB)\\nCollecting azure-mgmt-containerregistry>=2.0.0\\n  Downloading azure_mgmt_containerregistry-2.8.0-py2.py3-none-any.whl (718 kB)\\nCollecting jmespath\\n  Downloading jmespath-0.10.0-py2.py3-none-any.whl (24 kB)\\nCollecting azure-mgmt-storage>=1.5.0\\n  Downloading azure_mgmt_storage-10.0.0-py2.py3-none-any.whl (532 kB)\\nCollecting azure-mgmt-keyvault>=0.40.0\\n  Downloading azure_mgmt_keyvault-2.2.0-py2.py3-none-any.whl (89 kB)\\nCollecting azure-mgmt-authorization>=0.40.0\\n  Downloading azure_mgmt_authorization-0.60.0-py2.py3-none-any.whl (82 kB)\\nCollecting docker\\n  Downloading docker-4.2.0-py2.py3-none-any.whl (143 kB)\\nCollecting msrest>=0.5.1\\r\\n  Downloading msrest-0.6.14-py2.py3-none-any.whl (84 kB)\\nCollecting pathspec\\n  Downloading pathspec-0.8.0-py2.py3-none-any.whl (28 kB)\\nCollecting msrestazure>=0.4.33\\n  Downloading msrestazure-0.6.3-py2.py3-none-any.whl (40 kB)\\nCollecting ruamel.yaml>0.16.7\\n  Downloading ruamel.yaml-0.16.10-py2.py3-none-any.whl (111 kB)\\nCollecting ndg-httpsclient\\n  Downloading ndg_httpsclient-0.5.1-py3-none-any.whl (34 kB)\\nCollecting contextlib2\\n  Downloading contextlib2-0.6.0.post1-py2.py3-none-any.whl (9.8 kB)\\nCollecting azure-mgmt-network~=10.0\\n  Downloading azure_mgmt_network-10.2.0-py2.py3-none-any.whl (8.6 MB)\\nCollecting azure-graphrbac>=0.40.0\\r\\n  Downloading azure_graphrbac-0.61.1-py2.py3-none-any.whl (141 kB)\\nCollecting azure-common>=1.1.12\\n  Downloading azure_common-1.1.25-py2.py3-none-any.whl (12 kB)\\nCollecting PyJWT\\n  Downloading PyJWT-1.7.1-py2.py3-none-any.whl (18 kB)\\nCollecting urllib3>=1.23\\n  Downloading urllib3-1.25.9-py2.py3-none-any.whl (126 kB)\\nCollecting cryptography!=1.9,!=2.0.*,!=2.1.*,!=2.2.*\\n  Downloading cryptography-2.9.2-cp35-abi3-manylinux2010_x86_64.whl (2.7 MB)\\nCollecting azure-mgmt-resource>=1.2.1\\n  Downloading azure_mgmt_resource-9.0.0-py2.py3-none-any.whl (807 kB)\\nCollecting jsonpickle\\n  Downloading jsonpickle-1.4.1-py2.py3-none-any.whl (36 kB)\\nCollecting pyopenssl\\n  Downloading pyOpenSSL-19.1.0-py2.py3-none-any.whl (53 kB)\\nCollecting SecretStorage\\n  Downloading SecretStorage-3.1.2-py3-none-any.whl (14 kB)\\nCollecting backports.tempfile\\n  Downloading backports.tempfile-1.0-py2.py3-none-any.whl (4.4 kB)\\nCollecting Jinja2>=2.10\\n  Downloading Jinja2-2.11.2-py2.py3-none-any.whl (125 kB)\\nCollecting click>=5.1\\n  Downloading click-7.1.2-py2.py3-none-any.whl (82 kB)\\nCollecting itsdangerous>=0.24\\n  Downloading itsdangerous-1.1.0-py2.py3-none-any.whl (16 kB)\\nCollecting azure-core<2.0.0,>=1.0.0\\n  Downloading azure_core-1.5.0-py2.py3-none-any.whl (118 kB)\\nCollecting msal<2.0.0,>=1.0.0\\n  Downloading msal-1.3.0-py2.py3-none-any.whl (48 kB)\\nCollecting msal-extensions~=0.1.3\\n  Downloading msal_extensions-0.1.3-py2.py3-none-any.whl (9.0 kB)\\nCollecting distro>=1.2.0\\n  Downloading distro-1.5.0-py2.py3-none-any.whl (18 kB)\\nCollecting chardet<4,>=3.0.2\\n  Downloading chardet-3.0.4-py2.py3-none-any.whl (133 kB)\\r\\nCollecting idna<3,>=2.5\\n  Downloading idna-2.9-py2.py3-none-any.whl (58 kB)\\nRequirement already satisfied: certifi>=2017.4.17 in /azureml-envs/azureml_022b8d8faf6253082d55b26d46e8a59c/lib/python3.6/site-packages (from requests>=2.17.3->azureml-model-management-sdk==1.0.1b6.post1->azureml-defaults->-r /azureml-environment-setup/condaenv.8p5bp2mu.requirements.txt (line 1)) (2020.4.5.1)\\nCollecting websocket-client>=0.32.0\\n  Downloading websocket_client-0.57.0-py2.py3-none-any.whl (200 kB)\\nCollecting requests-oauthlib>=0.5.0\\n  Downloading requests_oauthlib-1.3.0-py2.py3-none-any.whl (23 kB)\\nCollecting isodate>=0.6.0\\n  Downloading isodate-0.6.0-py2.py3-none-any.whl (45 kB)\\nCollecting ruamel.yaml.clib>=0.1.2; platform_python_implementation == \\\"CPython\\\" and python_version < \\\"3.9\\\"\\n  Downloading ruamel.yaml.clib-0.2.0-cp36-cp36m-manylinux1_x86_64.whl (548 kB)\\nCollecting pyasn1>=0.1.1\\n  Downloading pyasn1-0.4.8-py2.py3-none-any.whl (77 kB)\\nCollecting cffi!=1.11.3,>=1.8\\n  Downloading cffi-1.14.0-cp36-cp36m-manylinux1_x86_64.whl (399 kB)\\nCollecting importlib-metadata\\n  Downloading importlib_metadata-1.6.0-py2.py3-none-any.whl (30 kB)\\nCollecting jeepney>=0.4.2\\n  Downloading jeepney-0.4.3-py3-none-any.whl (21 kB)\\nCollecting backports.weakref\\n  Downloading backports.weakref-1.0.post1-py2.py3-none-any.whl (5.2 kB)\\nCollecting MarkupSafe>=0.23\\n  Downloading MarkupSafe-1.1.1-cp36-cp36m-manylinux1_x86_64.whl (27 kB)\\nCollecting portalocker~=1.0\\n  Downloading portalocker-1.7.0-py2.py3-none-any.whl (14 kB)\\nCollecting oauthlib>=3.0.0\\n  Downloading oauthlib-3.1.0-py2.py3-none-any.whl (147 kB)\\nCollecting pycparser\\n  Downloading pycparser-2.20-py2.py3-none-any.whl (112 kB)\\nCollecting zipp>=0.5\\n  Downloading zipp-3.1.0-py3-none-any.whl (4.9 kB)\\nBuilding wheels for collected packages: json-logging-py, wrapt, fusepy, dill, liac-arff\\n  Building wheel for json-logging-py (setup.py): started\\n  Building wheel for json-logging-py (setup.py): finished with status 'done'\\n  Created wheel for json-logging-py: filename=json_logging_py-0.2-py3-none-any.whl size=3923 sha256=485c0055dd38705501abb18d667d25ea70060981c8046d95445690d0acfb6f3a\\n  Stored in directory: /root/.cache/pip/wheels/e2/1d/52/535a274b9c2ce7d4064838f2bdb62013801281ef7d7f21e2ee\\n  Building wheel for wrapt (setup.py): started\\n  Building wheel for wrapt (setup.py): finished with status 'done'\\r\\n  Created wheel for wrapt: filename=wrapt-1.11.1-cp36-cp36m-linux_x86_64.whl size=66707 sha256=a766ed25aabc98ba0af62856d6741108b0ebe336aac154a697961d6aa407f19e\\n  Stored in directory: /root/.cache/pip/wheels/94/0f/ec/66085641573800014bb0c8b657f3366eff641c42df79abbfe9\\n  Building wheel for fusepy (setup.py): started\\n  Building wheel for fusepy (setup.py): finished with status 'done'\\n  Created wheel for fusepy: filename=fusepy-3.0.1-py3-none-any.whl size=10503 sha256=9eff58e76e0a5ea08499caa64632b228fc908fab708bc10416e8695e3841ae7b\\n  Stored in directory: /root/.cache/pip/wheels/21/5c/83/1dd7e8a232d12227e5410120f4374b33adeb4037473105b079\\n  Building wheel for dill (setup.py): started\\n  Building wheel for dill (setup.py): finished with status 'done'\\n  Created wheel for dill: filename=dill-0.3.1.1-py3-none-any.whl size=78530 sha256=d1b47f5224635c6c7a3adb98f3a7502a8fd80e5a92668534024fc51f26c36340\\n  Stored in directory: /root/.cache/pip/wheels/09/84/74/d2b4feb9ac9488bc83c475cb2cbe8e8b7d9cea8320d32f3787\\n  Building wheel for liac-arff (setup.py): started\\n  Building wheel for liac-arff (setup.py): finished with status 'done'\\n  Created wheel for liac-arff: filename=liac_arff-2.4.0-py3-none-any.whl size=13333 sha256=86dfcef46e7e224d3e9d686a1ea7d8da58dde497940c98d8ed6dc9de8cece0c4\\n  Stored in directory: /root/.cache/pip/wheels/ba/2a/e1/6f7be2e2ea150e2486bff64fd6f0670f4f35f4c8f31c819fb8\\nSuccessfully built json-logging-py wrapt fusepy dill liac-arff\\nInstalling collected packages: azureml-dataprep-native, urllib3, chardet, idna, requests, azure-core, PyJWT, msal, pycparser, cffi, cryptography, portalocker, msal-extensions, azure-identity, distro, dotnetcore2, cloudpickle, fusepy, azureml-dataprep, configparser, dill, adal, liac-arff, azureml-model-management-sdk, json-logging-py, applicationinsights, gunicorn, oauthlib, requests-oauthlib, isodate, msrest, msrestazure, azure-common, azure-mgmt-containerregistry, jmespath, azure-mgmt-storage, azure-mgmt-keyvault, azure-mgmt-authorization, websocket-client, docker, pathspec, ruamel.yaml.clib, ruamel.yaml, pyopenssl, pyasn1, ndg-httpsclient, contextlib2, azure-mgmt-network, azure-graphrbac, azure-mgmt-resource, zipp, importlib-metadata, jsonpickle, jeepney, SecretStorage, backports.weakref, backports.tempfile, azureml-core, MarkupSafe, Jinja2, click, werkzeug, itsdangerous, flask, azureml-defaults, wrapt, inference-schema\\n\\r\\nSuccessfully installed Jinja2-2.11.2 MarkupSafe-1.1.1 PyJWT-1.7.1 SecretStorage-3.1.2 adal-1.2.3 applicationinsights-0.11.9 azure-common-1.1.25 azure-core-1.5.0 azure-graphrbac-0.61.1 azure-identity-1.2.0 azure-mgmt-authorization-0.60.0 azure-mgmt-containerregistry-2.8.0 azure-mgmt-keyvault-2.2.0 azure-mgmt-network-10.2.0 azure-mgmt-resource-9.0.0 azure-mgmt-storage-10.0.0 azureml-core-1.6.0 azureml-dataprep-1.6.3 azureml-dataprep-native-14.1.0 azureml-defaults-1.6.0 azureml-model-management-sdk-1.0.1b6.post1 backports.tempfile-1.0 backports.weakref-1.0.post1 cffi-1.14.0 chardet-3.0.4 click-7.1.2 cloudpickle-1.4.1 configparser-3.7.4 contextlib2-0.6.0.post1 cryptography-2.9.2 dill-0.3.1.1 distro-1.5.0 docker-4.2.0 dotnetcore2-2.1.14 flask-1.0.3 fusepy-3.0.1 gunicorn-19.9.0 idna-2.9 importlib-metadata-1.6.0 inference-schema-1.0.2 isodate-0.6.0 itsdangerous-1.1.0 jeepney-0.4.3 jmespath-0.10.0 json-logging-py-0.2 jsonpickle-1.4.1 liac-arff-2.4.0 msal-1.3.0 msal-extensions-0.1.3 msrest-0.6.14 msrestazure-0.6.3 ndg-httpsclient-0.5.1 oauthlib-3.1.0 pathspec-0.8.0 portalocker-1.7.0 pyasn1-0.4.8 pycparser-2.20 pyopenssl-19.1.0 requests-2.23.0 requests-oauthlib-1.3.0 ruamel.yaml-0.16.10 ruamel.yaml.clib-0.2.0 urllib3-1.25.9 websocket-client-0.57.0 werkzeug-0.16.1 wrapt-1.11.1 zipp-3.1.0\\r\\n#\\n# To activate this environment, use:\\n# > source activate /azureml-envs/azureml_022b8d8faf6253082d55b26d46e8a59c\\n#\\n# To deactivate an active environment, use:\\n# > source deactivate\\n#\\n\\n\\u001b[91m\\n\\u001b[0m\\r\\nRemoving intermediate container 5f01422a6772\\n ---> f87854dff705\\nStep 9/15 : ENV PATH /azureml-envs/azureml_022b8d8faf6253082d55b26d46e8a59c/bin:$PATH\\r\\n ---> Running in c37dbe86d622\\nRemoving intermediate container c37dbe86d622\\n ---> ab99ac6d3be8\\nStep 10/15 : ENV AZUREML_CONDA_ENVIRONMENT_PATH /azureml-envs/azureml_022b8d8faf6253082d55b26d46e8a59c\\r\\n\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.5.0\"}, \"loading\": false}"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(run).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RunId: diabetes_dxd_1590994974_fc193c44\n",
      "Web View: https://ml.azure.com/experiments/diabetes_dxd/runs/diabetes_dxd_1590994974_fc193c44?wsid=/subscriptions/f80606e5-788f-4dc3-a9ea-2eb9a7836082/resourcegroups/adlsgen2/workspaces/eacbmlservicews\n",
      "\n",
      "Streaming azureml-logs/20_image_build_log.txt\n",
      "=============================================\n",
      "\n",
      "2020/06/01 07:03:15 Downloading source code...\n",
      "2020/06/01 07:03:16 Finished downloading source code\n",
      "2020/06/01 07:03:17 Creating Docker network: acb_default_network, driver: 'bridge'\n",
      "2020/06/01 07:03:17 Successfully set up Docker network: acb_default_network\n",
      "2020/06/01 07:03:17 Setting up Docker configuration...\n",
      "2020/06/01 07:03:18 Successfully set up Docker configuration\n",
      "2020/06/01 07:03:18 Logging in to registry: eacbmlservicew8493899780.azurecr.io\n",
      "2020/06/01 07:03:19 Successfully logged into eacbmlservicew8493899780.azurecr.io\n",
      "2020/06/01 07:03:19 Executing step ID: acb_step_0. Timeout(sec): 5400, Working directory: '', Network: 'acb_default_network'\n",
      "2020/06/01 07:03:19 Scanning for dependencies...\n",
      "2020/06/01 07:03:20 Successfully scanned dependencies\n",
      "2020/06/01 07:03:20 Launching container with name: acb_step_0\n",
      "Sending build context to Docker daemon  60.93kB\n",
      "\n",
      "Step 1/15 : FROM mcr.microsoft.com/azureml/base:intelmpi2018.3-ubuntu16.04@sha256:a1b514f3ba884b9a7695cbba5638933ddaf222e8ce3e8c81e8cdf861679abb05\n",
      "sha256:a1b514f3ba884b9a7695cbba5638933ddaf222e8ce3e8c81e8cdf861679abb05: Pulling from azureml/base\n",
      "Digest: sha256:a1b514f3ba884b9a7695cbba5638933ddaf222e8ce3e8c81e8cdf861679abb05\n",
      "Status: Downloaded newer image for mcr.microsoft.com/azureml/base:intelmpi2018.3-ubuntu16.04@sha256:a1b514f3ba884b9a7695cbba5638933ddaf222e8ce3e8c81e8cdf861679abb05\n",
      " ---> 93a72e6bd1ce\n",
      "Step 2/15 : USER root\n",
      " ---> Running in 9fd08f8c05f8\n",
      "Removing intermediate container 9fd08f8c05f8\n",
      " ---> 7991a3671d7b\n",
      "Step 3/15 : RUN mkdir -p $HOME/.cache\n",
      " ---> Running in fd81b76ed97d\n",
      "Removing intermediate container fd81b76ed97d\n",
      " ---> 46ef98f305a5\n",
      "Step 4/15 : WORKDIR /\n",
      " ---> Running in 3a3e3df5f7ae\n",
      "Removing intermediate container 3a3e3df5f7ae\n",
      " ---> c7434c0932bd\n",
      "Step 5/15 : COPY azureml-environment-setup/99brokenproxy /etc/apt/apt.conf.d/\n",
      " ---> 7fe498f32a83\n",
      "Step 6/15 : RUN if dpkg --compare-versions `conda --version | grep -oE '[^ ]+$'` lt 4.4.11; then conda install conda==4.4.11; fi\n",
      " ---> Running in dff024300d9d\n",
      "Removing intermediate container dff024300d9d\n",
      " ---> 9784087f854e\n",
      "Step 7/15 : COPY azureml-environment-setup/mutated_conda_dependencies.yml azureml-environment-setup/mutated_conda_dependencies.yml\n",
      " ---> 8e3dd42a663e\n",
      "Step 8/15 : RUN ldconfig /usr/local/cuda/lib64/stubs && conda env create -p /azureml-envs/azureml_022b8d8faf6253082d55b26d46e8a59c -f azureml-environment-setup/mutated_conda_dependencies.yml && rm -rf \"$HOME/.cache/pip\" && conda clean -aqy && CONDA_ROOT_DIR=$(conda info --root) && rm -rf \"$CONDA_ROOT_DIR/pkgs\" && find \"$CONDA_ROOT_DIR\" -type d -name __pycache__ -exec rm -rf {} + && ldconfig\n",
      " ---> Running in 5f01422a6772\n",
      "Solving environment: ...working... \n",
      "done\n",
      "\u001b[91m\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.5.11\n",
      "  latest version: 4.8.3\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n",
      "\n",
      "pcre-8.43            | 260 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "pcre-8.43            | 260 KB    | 4          |   5% \u001b[0m\u001b[91m\n",
      "pcre-8.43            | 260 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "libuuid-1.0.3        | 16 KB     |            |   0% \u001b[0m\u001b[91m\n",
      "libuuid-1.0.3        | 16 KB     | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "libpng-1.6.37        | 364 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "libpng-1.6.37        | 364 KB    | #######7   |  78% \u001b[0m\u001b[91m\n",
      "libpng-1.6.37        | 364 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "scipy-1.4.1          | 18.9 MB   |            |   0% \u001b[0m\u001b[91m\n",
      "scipy-1.4.1          | 18.9 MB   | ##8        |  29% \u001b[0m\u001b[91m\n",
      "scipy-1.4.1          | 18.9 MB   | #######5   |  75% \u001b[0m\u001b[91m\n",
      "scipy-1.4.1          | 18.9 MB   | #########4 |  95% \u001b[0m\u001b[91m\n",
      "scipy-1.4.1          | 18.9 MB   | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "python-3.6.2         | 27.0 MB   |            |   0% \u001b[0m\u001b[91m\n",
      "python-3.6.2         | 27.0 MB   | ##         |  20% \u001b[0m\u001b[91m\n",
      "python-3.6.2         | 27.0 MB   | #####3     |  53% \u001b[0m\u001b[91m\n",
      "python-3.6.2         | 27.0 MB   | #######5   |  75% \u001b[0m\u001b[91m\n",
      "python-3.6.2         | 27.0 MB   | ########8  |  88% \u001b[0m\u001b[91m\n",
      "python-3.6.2         | 27.0 MB   | #########7 |  98% \u001b[0m\u001b[91m\n",
      "python-3.6.2         | 27.0 MB   | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "six-1.14.0           | 27 KB     |            |   0% \u001b[0m\u001b[91m\n",
      "six-1.14.0           | 27 KB     | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "numpy-1.18.1         | 5 KB      |            |   0% \u001b[0m\u001b[91m\n",
      "numpy-1.18.1         | 5 KB      | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "setuptools-46.4.0    | 646 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "setuptools-46.4.0    | 646 KB    | ########8  |  89% \u001b[0m\u001b[91m\n",
      "setuptools-46.4.0    | 646 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "qt-5.9.6             | 86.7 MB   |            |   0% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | 4          |   4% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | #3         |  14% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | ##3        |  24% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | ###3       |  34% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | ####3      |  44% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | #####3     |  53% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | ######3    |  63% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | #######2   |  72% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | ########1  |  81% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | ########7  |  87% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | #########1 |  92% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | #########5 |  95% \u001b[0m\n",
      "\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | #########7 |  97% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | #########8 |  99% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "numpy-base-1.18.1    | 5.2 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "numpy-base-1.18.1    | 5.2 MB    | #######5   |  76% \u001b[0m\u001b[91m\n",
      "numpy-base-1.18.1    | 5.2 MB    | #########7 |  98% \u001b[0m\u001b[91m\n",
      "numpy-base-1.18.1    | 5.2 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "gst-plugins-base-1.1 | 6.3 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "gst-plugins-base-1.1 | 6.3 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "gst-plugins-base-1.1 | 6.3 MB    | #######5   |  75% \u001b[0m\u001b[91m\n",
      "gst-plugins-base-1.1 | 6.3 MB    | #########  |  90% \u001b[0m\u001b[91m\n",
      "gst-plugins-base-1.1 | 6.3 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "python-dateutil-2.8. | 224 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "python-dateutil-2.8. | 224 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "readline-7.0         | 387 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "readline-7.0         | 387 KB    | #########1 |  91% \u001b[0m\u001b[91m\n",
      "readline-7.0         | 387 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "mkl-service-2.3.0    | 208 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "mkl-service-2.3.0    | 208 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "pandas-1.0.3         | 11.1 MB   |            |   0% \u001b[0m\u001b[91m\n",
      "pandas-1.0.3         | 11.1 MB   | ###9       |  40% \u001b[0m\u001b[91m\n",
      "pandas-1.0.3         | 11.1 MB   | #######5   |  75% \u001b[0m\u001b[91m\n",
      "pandas-1.0.3         | 11.1 MB   | #########1 |  92% \u001b[0m\u001b[91m\n",
      "pandas-1.0.3         | 11.1 MB   | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "icu-58.2             | 22.7 MB   |            |   0% \u001b[0m\u001b[91m\n",
      "icu-58.2             | 22.7 MB   | #5         |  15% \u001b[0m\u001b[91m\n",
      "icu-58.2             | 22.7 MB   | ###8       |  38% \u001b[0m\u001b[91m\n",
      "icu-58.2             | 22.7 MB   | #####7     |  57% \u001b[0m\u001b[91m\n",
      "icu-58.2             | 22.7 MB   | #######5   |  75% \u001b[0m\u001b[91m\n",
      "icu-58.2             | 22.7 MB   | ########8  |  89% \u001b[0m\u001b[91m\n",
      "icu-58.2             | 22.7 MB   | #########8 |  98% \u001b[0m\u001b[91m\n",
      "icu-58.2             | 22.7 MB   | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "intel-openmp-2020.1  | 940 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "intel-openmp-2020.1  | 940 KB    | ########1  |  82% \u001b[0m\u001b[91m\n",
      "intel-openmp-2020.1  | 940 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "jpeg-9b              | 247 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "jpeg-9b              | 247 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "libxml2-2.9.9        | 2.0 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "libxml2-2.9.9        | 2.0 MB    | #######6   |  76% \u001b[0m\u001b[91m\n",
      "libxml2-2.9.9        | 2.0 MB    | #########  |  91% \u001b[0m\u001b[91m\n",
      "libxml2-2.9.9        | 2.0 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "cycler-0.10.0        | 13 KB     |            |   0% \u001b[0m\u001b[91m\n",
      "cycler-0.10.0        | 13 KB     | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "openssl-1.0.2u       | 3.1 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "openssl-1.0.2u       | 3.1 MB    | #######6   |  76% \u001b[0m\u001b[91m\n",
      "openssl-1.0.2u       | 3.1 MB    | #########2 |  93% \u001b[0m\u001b[91m\n",
      "openssl-1.0.2u       | 3.1 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "pyqt-5.9.2           | 5.6 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "pyqt-5.9.2           | 5.6 MB    | #######5   |  75% \u001b[0m\u001b[91m\n",
      "pyqt-5.9.2           | 5.6 MB    | ########5  |  85% \u001b[0m\u001b[91m\n",
      "pyqt-5.9.2           | 5.6 MB    | #########2 |  93% \u001b[0m\u001b[91m\n",
      "pyqt-5.9.2           | 5.6 MB    | #########8 |  99% \u001b[0m\u001b[91m\n",
      "pyqt-5.9.2           | 5.6 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "libffi-3.2.1         | 43 KB     |            |   0% \u001b[0m\u001b[91m\n",
      "libffi-3.2.1         | 43 KB     | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "glib-2.56.2          | 5.0 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "glib-2.56.2          | 5.0 MB    | #######5   |  75% \u001b[0m\u001b[91m\n",
      "glib-2.56.2          | 5.0 MB    | #########7 |  98% \u001b[0m\u001b[91m\n",
      "glib-2.56.2          | 5.0 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "freetype-2.9.1       | 822 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "freetype-2.9.1       | 822 KB    | ########   |  81% \u001b[0m\u001b[91m\n",
      "freetype-2.9.1       | 822 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "kiwisolver-1.2.0     | 91 KB     |            |   0% \u001b[0m\u001b[91m\n",
      "kiwisolver-1.2.0     | 91 KB     | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "blas-1.0             | 6 KB      |            |   0% \u001b[0m\u001b[91m\n",
      "blas-1.0             | 6 KB      | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "fontconfig-2.13.0    | 291 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "fontconfig-2.13.0    | 291 KB    | #########7 |  98% \u001b[0m\u001b[91m\n",
      "fontconfig-2.13.0    | 291 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "scikit-learn-0.22.1  | 7.1 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "scikit-learn-0.22.1  | 7.1 MB    | #######3   |  74% \u001b[0m\u001b[91m\n",
      "scikit-learn-0.22.1  | 7.1 MB    | #########6 |  96% \u001b[0m\u001b[91m\n",
      "scikit-learn-0.22.1  | 7.1 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "expat-2.2.6          | 187 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "expat-2.2.6          | 187 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "sqlite-3.23.1        | 1.5 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "sqlite-3.23.1        | 1.5 MB    | ########   |  80% \u001b[0m\u001b[91m\n",
      "sqlite-3.23.1        | 1.5 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "tk-8.6.8             | 3.1 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "tk-8.6.8             | 3.1 MB    | #######6   |  77% \u001b[0m\u001b[91m\n",
      "tk-8.6.8             | 3.1 MB    | ########9  |  90% \u001b[0m\u001b[91m\n",
      "tk-8.6.8             | 3.1 MB    | #########9 | 100% \u001b[0m\u001b[91m\n",
      "tk-8.6.8             | 3.1 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "sip-4.19.13          | 293 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "sip-4.19.13          | 293 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "pyparsing-2.4.7      | 64 KB     |            |   0% \u001b[0m\u001b[91m\n",
      "pyparsing-2.4.7      | 64 KB     | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "tornado-6.0.4        | 650 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "tornado-6.0.4        | 650 KB    | ########4  |  85% \u001b[0m\u001b[91m\n",
      "tornado-6.0.4        | 650 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "pytz-2020.1          | 239 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "pytz-2020.1          | 239 KB    | #########3 |  93% \u001b[0m\u001b[91m\n",
      "pytz-2020.1          | 239 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "libedit-3.1          | 171 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "libedit-3.1          | 171 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "ca-certificates-2020 | 132 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "ca-certificates-2020 | 132 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "certifi-2020.4.5.1   | 159 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "certifi-2020.4.5.1   | 159 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "libgcc-ng-9.1.0      | 8.1 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "libgcc-ng-9.1.0      | 8.1 MB    | #######5   |  75% \u001b[0m\u001b[91m\n",
      "libgcc-ng-9.1.0      | 8.1 MB    | #########7 |  97% \u001b[0m\u001b[91m\n",
      "libgcc-ng-9.1.0      | 8.1 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "xz-5.2.5             | 438 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "xz-5.2.5             | 438 KB    | #########  |  90% \u001b[0m\u001b[91m\n",
      "xz-5.2.5             | 438 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "dbus-1.13.12         | 611 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "dbus-1.13.12         | 611 KB    | #########1 |  91% \u001b[0m\u001b[91m\n",
      "dbus-1.13.12         | 611 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "wheel-0.34.2         | 49 KB     |            |   0% \u001b[0m\u001b[91m\n",
      "wheel-0.34.2         | 49 KB     | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "matplotlib-3.1.3     | 21 KB     |            |   0% \u001b[0m\u001b[91m\n",
      "matplotlib-3.1.3     | 21 KB     | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "libgfortran-ng-7.3.0 | 1.3 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "libgfortran-ng-7.3.0 | 1.3 MB    | #######9   |  79% \u001b[0m\u001b[91m\n",
      "libgfortran-ng-7.3.0 | 1.3 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "joblib-0.15.1        | 207 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "joblib-0.15.1        | 207 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "libxcb-1.13          | 502 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "libxcb-1.13          | 502 KB    | ########2  |  82% \u001b[0m\u001b[91m\n",
      "libxcb-1.13          | 502 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "mkl-2019.4           | 204.1 MB  |            |   0% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | 2          |   3% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | 6          |   6% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | 9          |  10% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | #3         |  14% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | #7         |  17% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ##1        |  21% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ##5        |  25% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ##9        |  29% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ###2       |  33% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ###6       |  37% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ####       |  40% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ####4      |  44% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ####7      |  48% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | #####2     |  52% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | #####5     |  56% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | #####9     |  60% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ######3    |  64% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ######7    |  68% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | #######1   |  72% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | #######5   |  75% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | #######8   |  78% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ########   |  80% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ########1  |  81% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ########2  |  83% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ########3  |  83% \u001b[0m\n",
      "\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ########3  |  84% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ########4  |  84% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ########4  |  84% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ########4  |  85% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ########4  |  85% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ########4  |  85% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ########5  |  85% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ########5  |  85% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ########5  |  85% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ########5  |  85% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ########5  |  85% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ########5  |  85% \u001b[0m\u001b[91m\n",
      "mkl-2019.4           | 204.1 MB  | ########5  |  86% \u001b[0m\u001b[91m\n",
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      "\u001b[91m\n",
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      "  Stored in directory: /root/.cache/pip/wheels/ba/2a/e1/6f7be2e2ea150e2486bff64fd6f0670f4f35f4c8f31c819fb8\n",
      "Successfully built json-logging-py wrapt fusepy dill liac-arff\n",
      "Installing collected packages: azureml-dataprep-native, urllib3, chardet, idna, requests, azure-core, PyJWT, msal, pycparser, cffi, cryptography, portalocker, msal-extensions, azure-identity, distro, dotnetcore2, cloudpickle, fusepy, azureml-dataprep, configparser, dill, adal, liac-arff, azureml-model-management-sdk, json-logging-py, applicationinsights, gunicorn, oauthlib, requests-oauthlib, isodate, msrest, msrestazure, azure-common, azure-mgmt-containerregistry, jmespath, azure-mgmt-storage, azure-mgmt-keyvault, azure-mgmt-authorization, websocket-client, docker, pathspec, ruamel.yaml.clib, ruamel.yaml, pyopenssl, pyasn1, ndg-httpsclient, contextlib2, azure-mgmt-network, azure-graphrbac, azure-mgmt-resource, zipp, importlib-metadata, jsonpickle, jeepney, SecretStorage, backports.weakref, backports.tempfile, azureml-core, MarkupSafe, Jinja2, click, werkzeug, itsdangerous, flask, azureml-defaults, wrapt, inference-schema\n",
      "\n",
      "Successfully installed Jinja2-2.11.2 MarkupSafe-1.1.1 PyJWT-1.7.1 SecretStorage-3.1.2 adal-1.2.3 applicationinsights-0.11.9 azure-common-1.1.25 azure-core-1.5.0 azure-graphrbac-0.61.1 azure-identity-1.2.0 azure-mgmt-authorization-0.60.0 azure-mgmt-containerregistry-2.8.0 azure-mgmt-keyvault-2.2.0 azure-mgmt-network-10.2.0 azure-mgmt-resource-9.0.0 azure-mgmt-storage-10.0.0 azureml-core-1.6.0 azureml-dataprep-1.6.3 azureml-dataprep-native-14.1.0 azureml-defaults-1.6.0 azureml-model-management-sdk-1.0.1b6.post1 backports.tempfile-1.0 backports.weakref-1.0.post1 cffi-1.14.0 chardet-3.0.4 click-7.1.2 cloudpickle-1.4.1 configparser-3.7.4 contextlib2-0.6.0.post1 cryptography-2.9.2 dill-0.3.1.1 distro-1.5.0 docker-4.2.0 dotnetcore2-2.1.14 flask-1.0.3 fusepy-3.0.1 gunicorn-19.9.0 idna-2.9 importlib-metadata-1.6.0 inference-schema-1.0.2 isodate-0.6.0 itsdangerous-1.1.0 jeepney-0.4.3 jmespath-0.10.0 json-logging-py-0.2 jsonpickle-1.4.1 liac-arff-2.4.0 msal-1.3.0 msal-extensions-0.1.3 msrest-0.6.14 msrestazure-0.6.3 ndg-httpsclient-0.5.1 oauthlib-3.1.0 pathspec-0.8.0 portalocker-1.7.0 pyasn1-0.4.8 pycparser-2.20 pyopenssl-19.1.0 requests-2.23.0 requests-oauthlib-1.3.0 ruamel.yaml-0.16.10 ruamel.yaml.clib-0.2.0 urllib3-1.25.9 websocket-client-0.57.0 werkzeug-0.16.1 wrapt-1.11.1 zipp-3.1.0\n",
      "#\n",
      "# To activate this environment, use:\n",
      "# > source activate /azureml-envs/azureml_022b8d8faf6253082d55b26d46e8a59c\n",
      "#\n",
      "# To deactivate an active environment, use:\n",
      "# > source deactivate\n",
      "#\n",
      "\n",
      "\u001b[91m\n",
      "\u001b[0m\n",
      "Removing intermediate container 5f01422a6772\n",
      " ---> f87854dff705\n",
      "Step 9/15 : ENV PATH /azureml-envs/azureml_022b8d8faf6253082d55b26d46e8a59c/bin:$PATH\n",
      " ---> Running in c37dbe86d622\n",
      "Removing intermediate container c37dbe86d622\n",
      " ---> ab99ac6d3be8\n",
      "Step 10/15 : ENV AZUREML_CONDA_ENVIRONMENT_PATH /azureml-envs/azureml_022b8d8faf6253082d55b26d46e8a59c\n",
      " ---> Running in 8a1d301c30d2\n",
      "Removing intermediate container 8a1d301c30d2\n",
      " ---> a42ab7907fb9\n",
      "Step 11/15 : ENV LD_LIBRARY_PATH /azureml-envs/azureml_022b8d8faf6253082d55b26d46e8a59c/lib:$LD_LIBRARY_PATH\n",
      " ---> Running in 668247b88cab\n",
      "Removing intermediate container 668247b88cab\n",
      " ---> a79de46b36cc\n",
      "Step 12/15 : COPY azureml-environment-setup/spark_cache.py azureml-environment-setup/log4j.properties /azureml-environment-setup/\n",
      " ---> d26b990a833c\n",
      "Step 13/15 : RUN if [ $SPARK_HOME ]; then /bin/bash -c '$SPARK_HOME/bin/spark-submit  /azureml-environment-setup/spark_cache.py'; fi\n",
      " ---> Running in 0c63481bc62c\n",
      "Removing intermediate container 0c63481bc62c\n",
      " ---> 3d557c5ae943\n",
      "Step 14/15 : ENV AZUREML_ENVIRONMENT_IMAGE True\n",
      " ---> Running in 07fa2e4f87fa\n",
      "Removing intermediate container 07fa2e4f87fa\n",
      " ---> 789ad73b44e9\n",
      "Step 15/15 : CMD [\"bash\"]\n",
      " ---> Running in 695dce9745ad\n",
      "Removing intermediate container 695dce9745ad\n",
      " ---> 5e9f5209d81e\n",
      "Successfully built 5e9f5209d81e\n",
      "Successfully tagged eacbmlservicew8493899780.azurecr.io/azureml/azureml_a9bb5b2e7baa83080a7719e12de22936:latest\n",
      "2020/06/01 07:08:27 Successfully executed container: acb_step_0\n",
      "2020/06/01 07:08:27 Executing step ID: acb_step_1. Timeout(sec): 5400, Working directory: '', Network: 'acb_default_network'\n",
      "2020/06/01 07:08:27 Pushing image: eacbmlservicew8493899780.azurecr.io/azureml/azureml_a9bb5b2e7baa83080a7719e12de22936:latest, attempt 1\n",
      "The push refers to repository [eacbmlservicew8493899780.azurecr.io/azureml/azureml_a9bb5b2e7baa83080a7719e12de22936]\n",
      "f052d20045c4: Preparing\n",
      "9bbc0de0778d: Preparing\n",
      "383da07e95b1: Preparing\n",
      "bc437215bd32: Preparing\n",
      "83935dc49209: Preparing\n",
      "29aca35268c9: Preparing\n",
      "e1171d4d60ca: Preparing\n",
      "6ef1a8ae63b7: Preparing\n",
      "85389f9ead9e: Preparing\n",
      "f2608f66a0e3: Preparing\n",
      "0e259b09e5f4: Preparing\n",
      "340dc32eb998: Preparing\n",
      "df18b66efaa6: Preparing\n",
      "ccdb13a20bf2: Preparing\n",
      "9513cdf4e497: Preparing\n",
      "7f083f9454c0: Preparing\n",
      "29f36b5893dc: Preparing\n",
      "29aca35268c9: Waiting\n",
      "e1171d4d60ca: Waiting\n",
      "6ef1a8ae63b7: Waiting\n",
      "340dc32eb998: Waiting\n",
      "85389f9ead9e: Waiting\n",
      "f2608f66a0e3: Waiting\n",
      "0e259b09e5f4: Waiting\n",
      "df18b66efaa6: Waiting\n",
      "ccdb13a20bf2: Waiting\n",
      "9513cdf4e497: Waiting\n",
      "7f083f9454c0: Waiting\n",
      "29f36b5893dc: Waiting\n",
      "83935dc49209: Pushed\n",
      "f052d20045c4: Pushed\n",
      "383da07e95b1: Pushed\n",
      "bc437215bd32: Pushed\n",
      "29aca35268c9: Pushed\n",
      "e1171d4d60ca: Pushed\n",
      "6ef1a8ae63b7: Pushed\n",
      "\n",
      "340dc32eb998: Pushed\n",
      "f2608f66a0e3: Pushed\n",
      "ccdb13a20bf2: Pushed\n",
      "85389f9ead9e: Pushed\n",
      "0e259b09e5f4: Pushed\n",
      "7f083f9454c0: Pushed\n",
      "9513cdf4e497: Pushed\n",
      "df18b66efaa6: Pushed\n",
      "29f36b5893dc: Pushed\n",
      "9bbc0de0778d: Pushed\n",
      "latest: digest: sha256:210b4d5b6d24b881805bbdb8c073f4ee7b3df814936b91ee28e08bb2b84d5afb size: 3883\n",
      "2020/06/01 07:11:13 Successfully pushed image: eacbmlservicew8493899780.azurecr.io/azureml/azureml_a9bb5b2e7baa83080a7719e12de22936:latest\n",
      "2020/06/01 07:11:13 Step ID: acb_step_0 marked as successful (elapsed time in seconds: 308.028293)\n",
      "2020/06/01 07:11:13 Populating digests for step ID: acb_step_0...\n",
      "2020/06/01 07:11:15 Successfully populated digests for step ID: acb_step_0\n",
      "2020/06/01 07:11:15 Step ID: acb_step_1 marked as successful (elapsed time in seconds: 166.489391)\n",
      "2020/06/01 07:11:15 The following dependencies were found:\n",
      "2020/06/01 07:11:15 \n",
      "- image:\n",
      "    registry: eacbmlservicew8493899780.azurecr.io\n",
      "    repository: azureml/azureml_a9bb5b2e7baa83080a7719e12de22936\n",
      "    tag: latest\n",
      "    digest: sha256:210b4d5b6d24b881805bbdb8c073f4ee7b3df814936b91ee28e08bb2b84d5afb\n",
      "  runtime-dependency:\n",
      "    registry: mcr.microsoft.com\n",
      "    repository: azureml/base\n",
      "    tag: intelmpi2018.3-ubuntu16.04\n",
      "    digest: sha256:a1b514f3ba884b9a7695cbba5638933ddaf222e8ce3e8c81e8cdf861679abb05\n",
      "  git: {}\n",
      "\n",
      "\n",
      "Run ID: ccx was successful after 8m2s\n",
      "\n",
      "Streaming azureml-logs/55_azureml-execution-tvmps_e919f456ced86c0b0ca9bda9ffb892e234c9a2fc7b9d57a8e86a1adcdb90f1d8_d.txt\n",
      "========================================================================================================================\n",
      "\n",
      "2020-06-01T07:11:42Z Starting output-watcher...\n",
      "2020-06-01T07:11:42Z IsDedicatedCompute == True, won't poll for Low Pri Preemption\n",
      "\n",
      "Streaming azureml-logs/65_job_prep-tvmps_e919f456ced86c0b0ca9bda9ffb892e234c9a2fc7b9d57a8e86a1adcdb90f1d8_d.txt\n",
      "===============================================================================================================\n",
      "\n",
      "Entering job preparation. Current time:2020-06-01T07:12:29.978962\n",
      "Starting job preparation. Current time:2020-06-01T07:12:30.831025\n",
      "Extracting the control code.\n",
      "fetching and extracting the control code on master node.\n",
      "Retrieving project from snapshot: 9118e3a2-2e64-47b4-bf73-4861048f154e\n",
      "Starting the daemon thread to refresh tokens in background for process with pid = 60\n",
      "Starting project file download.\n",
      "Finished project file download.\n",
      "downloadDataStore - Download from datastores if requested.\n",
      "\n",
      "Streaming azureml-logs/70_driver_log.txt\n",
      "========================================\n",
      "\n",
      "2020/06/01 07:12:34 Starting App Insight Logger for task:  runTaskLet\n",
      "Entering context manager injector. Current time:2020-06-01T07:12:36.275219\n",
      "Starting the daemon thread to refresh tokens in background for process with pid = 114\n",
      "Entering Run History Context Manager.\n",
      "Preparing to call script [ train.py ] with arguments: ['--regularization', '0.5']\n",
      "After variable expansion, calling script [ train.py ] with arguments: ['--regularization', '0.5']\n",
      "\n",
      "/azureml-envs/azureml_022b8d8faf6253082d55b26d46e8a59c/lib/python3.6/site-packages/azureml/dataprep/api/dataflow.py:722: UserWarning: Your pandas and pyarrow versions are incompatible. Please install pyarrow>=0.12.0 for improved performance of to_pandas_dataframe. You can ensure the correct version is installed by running: pip install pyarrow>=0.12.0 --upgrade\n",
      "  warnings.warn('Your pandas and pyarrow versions are incompatible. '\n",
      "Train a Ridge regression model with regularization strength of 0.5\n",
      "Predict the test set\n",
      "Score is  0.45238302601412694\n",
      "Starting the daemon thread to refresh tokens in background for process with pid = 114\n",
      "\n",
      "\n",
      "The experiment completed successfully. Finalizing run...\n",
      "Cleaning up all outstanding Run operations, waiting 300.0 seconds\n",
      "2 items cleaning up...\n",
      "\n",
      "Streaming azureml-logs/75_job_post-tvmps_e919f456ced86c0b0ca9bda9ffb892e234c9a2fc7b9d57a8e86a1adcdb90f1d8_d.txt\n",
      "===============================================================================================================\n",
      "\n",
      "Entering job release. Current time:2020-06-01T07:12:59.288019\n",
      "Starting job release. Current time:2020-06-01T07:13:00.247726\n",
      "Logging experiment finalizing status in history service.\n",
      "Starting the daemon thread to refresh tokens in background for process with pid = 312\n",
      "Entering context manager injector. Current time:2020-06-01T07:13:00.269483\n",
      "Job release is complete. Current time:2020-06-01T07:13:03.278667\n",
      "\n",
      "Execution Summary\n",
      "=================\n",
      "RunId: diabetes_dxd_1590994974_fc193c44\n",
      "Web View: https://ml.azure.com/experiments/diabetes_dxd/runs/diabetes_dxd_1590994974_fc193c44?wsid=/subscriptions/f80606e5-788f-4dc3-a9ea-2eb9a7836082/resourcegroups/adlsgen2/workspaces/eacbmlservicews\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'runId': 'diabetes_dxd_1590994974_fc193c44',\n",
       " 'target': 'myComputeCluster',\n",
       " 'status': 'Completed',\n",
       " 'startTimeUtc': '2020-06-01T07:11:41.925656Z',\n",
       " 'endTimeUtc': '2020-06-01T07:13:07.352926Z',\n",
       " 'properties': {'_azureml.ComputeTargetType': 'amlcompute',\n",
       "  'ContentSnapshotId': 'e8f45a50-658c-430b-bfbe-f2a797d9e8bd',\n",
       "  'azureml.git.dirty': 'True',\n",
       "  'AzureML.DerivedImageName': 'azureml/azureml_a9bb5b2e7baa83080a7719e12de22936',\n",
       "  'ProcessInfoFile': 'azureml-logs/process_info.json',\n",
       "  'ProcessStatusFile': 'azureml-logs/process_status.json'},\n",
       " 'inputDatasets': [{'dataset': {'id': '640e0da3-4812-4d5d-8bbc-323e1354b451'}, 'consumptionDetails': {'type': 'Reference'}}],\n",
       " 'runDefinition': {'script': 'train.py',\n",
       "  'useAbsolutePath': False,\n",
       "  'arguments': ['--regularization', '0.5'],\n",
       "  'sourceDirectoryDataStore': None,\n",
       "  'framework': 'Python',\n",
       "  'communicator': 'None',\n",
       "  'target': 'myComputeCluster',\n",
       "  'dataReferences': {},\n",
       "  'data': {},\n",
       "  'jobName': None,\n",
       "  'maxRunDurationSeconds': None,\n",
       "  'nodeCount': 1,\n",
       "  'environment': {'name': 'diabetes_remote_env',\n",
       "   'version': 'Autosave_2020-06-01T07:02:58Z_6ac76270',\n",
       "   'python': {'interpreterPath': 'python',\n",
       "    'userManagedDependencies': False,\n",
       "    'condaDependencies': {'channels': ['anaconda', 'conda-forge'],\n",
       "     'dependencies': ['python=3.6.2',\n",
       "      {'pip': ['azureml-defaults', 'inference-schema[numpy-support]']},\n",
       "      'scikit-learn',\n",
       "      'pandas',\n",
       "      'numpy',\n",
       "      'joblib',\n",
       "      'matplotlib'],\n",
       "     'name': 'azureml_022b8d8faf6253082d55b26d46e8a59c'},\n",
       "    'baseCondaEnvironment': None},\n",
       "   'environmentVariables': {'EXAMPLE_ENV_VAR': 'EXAMPLE_VALUE'},\n",
       "   'docker': {'baseImage': 'mcr.microsoft.com/azureml/base:intelmpi2018.3-ubuntu16.04',\n",
       "    'baseDockerfile': None,\n",
       "    'baseImageRegistry': {'address': None, 'username': None, 'password': None},\n",
       "    'enabled': True,\n",
       "    'arguments': []},\n",
       "   'spark': {'repositories': [], 'packages': [], 'precachePackages': True},\n",
       "   'inferencingStackVersion': None},\n",
       "  'history': {'outputCollection': True,\n",
       "   'directoriesToWatch': ['logs'],\n",
       "   'snapshotProject': True},\n",
       "  'spark': {'configuration': {'spark.app.name': 'Azure ML Experiment',\n",
       "    'spark.yarn.maxAppAttempts': '1'}},\n",
       "  'parallelTask': {'maxRetriesPerWorker': 0,\n",
       "   'workerCountPerNode': 1,\n",
       "   'terminalExitCodes': None,\n",
       "   'configuration': {}},\n",
       "  'amlCompute': {'name': None,\n",
       "   'vmSize': None,\n",
       "   'retainCluster': False,\n",
       "   'clusterMaxNodeCount': 1},\n",
       "  'tensorflow': {'workerCount': 1, 'parameterServerCount': 1},\n",
       "  'mpi': {'processCountPerNode': 1},\n",
       "  'hdi': {'yarnDeployMode': 'Cluster'},\n",
       "  'containerInstance': {'region': None, 'cpuCores': 2, 'memoryGb': 3.5},\n",
       "  'exposedPorts': None,\n",
       "  'docker': {'useDocker': True,\n",
       "   'sharedVolumes': True,\n",
       "   'shmSize': '2g',\n",
       "   'arguments': []},\n",
       "  'cmk8sCompute': {'configuration': {}},\n",
       "  'itpCompute': {'configuration': {}}},\n",
       " 'logFiles': {'azureml-logs/20_image_build_log.txt': 'https://eacbmlservicew2479569759.blob.core.windows.net/azureml/ExperimentRun/dcid.diabetes_dxd_1590994974_fc193c44/azureml-logs/20_image_build_log.txt?sv=2019-02-02&sr=b&sig=yS4xtwTlRO00YDEiFnK%2FyJjHiFNOeHW9NvE4q7D54jM%3D&st=2020-06-01T07%3A03%3A09Z&se=2020-06-01T15%3A13%3A09Z&sp=r',\n",
       "  'azureml-logs/55_azureml-execution-tvmps_e919f456ced86c0b0ca9bda9ffb892e234c9a2fc7b9d57a8e86a1adcdb90f1d8_d.txt': 'https://eacbmlservicew2479569759.blob.core.windows.net/azureml/ExperimentRun/dcid.diabetes_dxd_1590994974_fc193c44/azureml-logs/55_azureml-execution-tvmps_e919f456ced86c0b0ca9bda9ffb892e234c9a2fc7b9d57a8e86a1adcdb90f1d8_d.txt?sv=2019-02-02&sr=b&sig=jJAi%2BIy7D65U9NfTBJEkrTAoh926mFTttzSVCZyzgYE%3D&st=2020-06-01T07%3A03%3A09Z&se=2020-06-01T15%3A13%3A09Z&sp=r',\n",
       "  'azureml-logs/65_job_prep-tvmps_e919f456ced86c0b0ca9bda9ffb892e234c9a2fc7b9d57a8e86a1adcdb90f1d8_d.txt': 'https://eacbmlservicew2479569759.blob.core.windows.net/azureml/ExperimentRun/dcid.diabetes_dxd_1590994974_fc193c44/azureml-logs/65_job_prep-tvmps_e919f456ced86c0b0ca9bda9ffb892e234c9a2fc7b9d57a8e86a1adcdb90f1d8_d.txt?sv=2019-02-02&sr=b&sig=LFuuv18eSElZffAYQhUQuTcjupdnY0yV0tFKQGSzoaQ%3D&st=2020-06-01T07%3A03%3A09Z&se=2020-06-01T15%3A13%3A09Z&sp=r',\n",
       "  'azureml-logs/70_driver_log.txt': 'https://eacbmlservicew2479569759.blob.core.windows.net/azureml/ExperimentRun/dcid.diabetes_dxd_1590994974_fc193c44/azureml-logs/70_driver_log.txt?sv=2019-02-02&sr=b&sig=0LxzYO20oeYKHtltl4Wmm%2F64v5BoAm1kyAilUE4WpHs%3D&st=2020-06-01T07%3A03%3A09Z&se=2020-06-01T15%3A13%3A09Z&sp=r',\n",
       "  'azureml-logs/75_job_post-tvmps_e919f456ced86c0b0ca9bda9ffb892e234c9a2fc7b9d57a8e86a1adcdb90f1d8_d.txt': 'https://eacbmlservicew2479569759.blob.core.windows.net/azureml/ExperimentRun/dcid.diabetes_dxd_1590994974_fc193c44/azureml-logs/75_job_post-tvmps_e919f456ced86c0b0ca9bda9ffb892e234c9a2fc7b9d57a8e86a1adcdb90f1d8_d.txt?sv=2019-02-02&sr=b&sig=cE%2Bk5gBaZmbhTn%2BNphef4UWmAQYjQBv1qOFWewMeZrs%3D&st=2020-06-01T07%3A03%3A09Z&se=2020-06-01T15%3A13%3A09Z&sp=r',\n",
       "  'azureml-logs/process_info.json': 'https://eacbmlservicew2479569759.blob.core.windows.net/azureml/ExperimentRun/dcid.diabetes_dxd_1590994974_fc193c44/azureml-logs/process_info.json?sv=2019-02-02&sr=b&sig=g0XMKC3A1MMJH7Mah7LtQ1oIelqWx9DXSHwg6TTP%2B7A%3D&st=2020-06-01T07%3A03%3A09Z&se=2020-06-01T15%3A13%3A09Z&sp=r',\n",
       "  'azureml-logs/process_status.json': 'https://eacbmlservicew2479569759.blob.core.windows.net/azureml/ExperimentRun/dcid.diabetes_dxd_1590994974_fc193c44/azureml-logs/process_status.json?sv=2019-02-02&sr=b&sig=F2VxAceuMUm2zvgvkr7PwiT6VWtv9IqnDUfBNQjmAWM%3D&st=2020-06-01T07%3A03%3A09Z&se=2020-06-01T15%3A13%3A09Z&sp=r',\n",
       "  'logs/azureml/114_azureml.log': 'https://eacbmlservicew2479569759.blob.core.windows.net/azureml/ExperimentRun/dcid.diabetes_dxd_1590994974_fc193c44/logs/azureml/114_azureml.log?sv=2019-02-02&sr=b&sig=8jl%2BZ%2FcC%2FwC3Pp3UPyRgvqiIfvxF3zLls9BWLzEk02g%3D&st=2020-06-01T07%3A03%3A09Z&se=2020-06-01T15%3A13%3A09Z&sp=r',\n",
       "  'logs/azureml/job_prep_azureml.log': 'https://eacbmlservicew2479569759.blob.core.windows.net/azureml/ExperimentRun/dcid.diabetes_dxd_1590994974_fc193c44/logs/azureml/job_prep_azureml.log?sv=2019-02-02&sr=b&sig=vJw5ULYlnUNYFS0DSKIhqySaTtZCPzL0WEWDuokTTGE%3D&st=2020-06-01T07%3A03%3A09Z&se=2020-06-01T15%3A13%3A09Z&sp=r',\n",
       "  'logs/azureml/job_release_azureml.log': 'https://eacbmlservicew2479569759.blob.core.windows.net/azureml/ExperimentRun/dcid.diabetes_dxd_1590994974_fc193c44/logs/azureml/job_release_azureml.log?sv=2019-02-02&sr=b&sig=nFtclIybwQd6Dj0MGDeBiRh7BnlKS5CmzDV5BZjVDw4%3D&st=2020-06-01T07%3A03%3A09Z&se=2020-06-01T15%3A13%3A09Z&sp=r'}}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# specify show_output to True for a verbose log\n",
    "run.wait_for_completion(show_output=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Register the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['azureml-logs/20_image_build_log.txt', 'azureml-logs/55_azureml-execution-tvmps_e919f456ced86c0b0ca9bda9ffb892e234c9a2fc7b9d57a8e86a1adcdb90f1d8_d.txt', 'azureml-logs/65_job_prep-tvmps_e919f456ced86c0b0ca9bda9ffb892e234c9a2fc7b9d57a8e86a1adcdb90f1d8_d.txt', 'azureml-logs/70_driver_log.txt', 'azureml-logs/75_job_post-tvmps_e919f456ced86c0b0ca9bda9ffb892e234c9a2fc7b9d57a8e86a1adcdb90f1d8_d.txt', 'azureml-logs/process_info.json', 'azureml-logs/process_status.json', 'logs/azureml/114_azureml.log', 'logs/azureml/job_prep_azureml.log', 'logs/azureml/job_release_azureml.log', 'outputs/diabetes_reg_remote_model.pkl']\n"
     ]
    }
   ],
   "source": [
    "print(run.get_file_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diabetes_ridge_regression\tdiabetes_ridge_regression:1\t1\n"
     ]
    }
   ],
   "source": [
    "# register model \n",
    "model = run.register_model(model_name='diabetes_ridge_regression', model_path='outputs/diabetes_reg_remote_model.pkl')\n",
    "\n",
    "print(model.name, model.id, model.version, sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a scoring script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting score.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile score.py\n",
    "\n",
    "import joblib\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "from inference_schema.schema_decorators import input_schema, output_schema\n",
    "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
    "\n",
    "\n",
    "# The init() method is called once, when the web service starts up.\n",
    "#\n",
    "# Typically you would deserialize the model file, as shown here using joblib,\n",
    "# and store it in a global variable so your run() method can access it later.\n",
    "def init():\n",
    "    global model\n",
    "\n",
    "    # The AZUREML_MODEL_DIR environment variable indicates\n",
    "    # a directory containing the model file you registered.\n",
    "    model_filename = 'diabetes_reg_remote_model.pkl'\n",
    "    model_path = os.path.join(os.environ['AZUREML_MODEL_DIR'], model_filename)\n",
    "\n",
    "    model = joblib.load(model_path)\n",
    "\n",
    "\n",
    "# The run() method is called each time a request is made to the scoring API.\n",
    "#\n",
    "# Shown here are the optional input_schema and output_schema decorators\n",
    "# from the inference-schema pip package. Using these decorators on your\n",
    "# run() method parses and validates the incoming payload against\n",
    "# the example input you provide here. This will also generate a Swagger\n",
    "# API document for your web service.\n",
    "@input_schema('data', NumpyParameterType(np.array([[59, 2, 32.1, 101.0, 157, 93.2, 38.0, 4.0, 4.8598, 87]])))\n",
    "@output_schema(NumpyParameterType(np.array([151.000])))\n",
    "def run(data):\n",
    "    # Use the model object loaded by init().\n",
    "    result = model.predict(data)\n",
    "\n",
    "    # You can return any JSON-serializable object.\n",
    "    return result.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the (inference) environement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Environment\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "\n",
    "environment = Environment('my-sklearn-environment')\n",
    "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
    "    'azureml-defaults',\n",
    "    'inference-schema[numpy-support]',\n",
    "    'joblib',\n",
    "    'numpy',\n",
    "    'scikit-learn'\n",
    "])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a inference configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.model import InferenceConfig\n",
    "\n",
    "inference_config = InferenceConfig(entry_script='score.py', environment=environment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploy in a custom environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running..........................................................\n",
      "Succeeded\n",
      "ACI service creation operation finished, operation \"Succeeded\"\n"
     ]
    }
   ],
   "source": [
    "from azureml.core import Webservice\n",
    "from azureml.core.webservice import AciWebservice\n",
    "from azureml.exceptions import WebserviceException\n",
    "from azureml.core.model import Model, InferenceConfig # à ajouter ?\n",
    "\n",
    "\n",
    "service_name = 'diabetes-custom-service2'\n",
    "\n",
    "# Remove any existing service under the same name.\n",
    "try:\n",
    "    Webservice(ws, service_name).delete()\n",
    "except WebserviceException:\n",
    "    pass\n",
    "\n",
    "aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
    "\n",
    "service = Model.deploy(workspace=ws,\n",
    "                       name=service_name,\n",
    "                       models=[model],\n",
    "                       inference_config=inference_config,\n",
    "                       deployment_config=aci_config)\n",
    "\n",
    "service.wait_for_deployment(show_output=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-06-01T07:18:16,194813250+00:00 - gunicorn/run \n",
      "2020-06-01T07:18:16,195966554+00:00 - rsyslog/run \n",
      "2020-06-01T07:18:16,198822062+00:00 - nginx/run \n",
      "/usr/sbin/nginx: /azureml-envs/azureml_0bc8cb4f185b37c328a427a1e38cbb9b/lib/libcrypto.so.1.0.0: no version information available (required by /usr/sbin/nginx)\n",
      "/usr/sbin/nginx: /azureml-envs/azureml_0bc8cb4f185b37c328a427a1e38cbb9b/lib/libcrypto.so.1.0.0: no version information available (required by /usr/sbin/nginx)\n",
      "/usr/sbin/nginx: /azureml-envs/azureml_0bc8cb4f185b37c328a427a1e38cbb9b/lib/libssl.so.1.0.0: no version information available (required by /usr/sbin/nginx)\n",
      "/usr/sbin/nginx: /azureml-envs/azureml_0bc8cb4f185b37c328a427a1e38cbb9b/lib/libssl.so.1.0.0: no version information available (required by /usr/sbin/nginx)\n",
      "/usr/sbin/nginx: /azureml-envs/azureml_0bc8cb4f185b37c328a427a1e38cbb9b/lib/libssl.so.1.0.0: no version information available (required by /usr/sbin/nginx)\n",
      "2020-06-01T07:18:16,198049260+00:00 - iot-server/run \n",
      "EdgeHubConnectionString and IOTEDGE_IOTHUBHOSTNAME are not set. Exiting...\n",
      "2020-06-01T07:18:16,344660605+00:00 - iot-server/finish 1 0\n",
      "2020-06-01T07:18:16,346154609+00:00 - Exit code 1 is normal. Not restarting iot-server.\n",
      "Starting gunicorn 19.9.0\n",
      "Listening at: http://127.0.0.1:31311 (12)\n",
      "Using worker: sync\n",
      "worker timeout is set to 300\n",
      "Booting worker with pid: 43\n",
      "Initialized PySpark session.\n",
      "Initializing logger\n",
      "Starting up app insights client\n",
      "Starting up request id generator\n",
      "Starting up app insight hooks\n",
      "Invoking user's init function\n",
      "/azureml-envs/azureml_0bc8cb4f185b37c328a427a1e38cbb9b/lib/python3.6/site-packages/sklearn/base.py:334: UserWarning: Trying to unpickle estimator Ridge from version 0.22.1 when using version 0.23.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  UserWarning)\n",
      "Users's init has completed successfully\n",
      "Scoring timeout is found from os.environ: 60000 ms\n",
      "200\n",
      "127.0.0.1 - - [01/Jun/2020:07:18:33 +0000] \"GET /swagger.json HTTP/1.0\" 200 2082 \"-\" \"Go-http-client/1.1\"\n",
      "200\n",
      "127.0.0.1 - - [01/Jun/2020:07:18:39 +0000] \"GET /swagger.json HTTP/1.0\" 200 2082 \"-\" \"Go-http-client/1.1\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(service.get_logs())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://848461b2-923e-4879-a4b9-8e956f2da3d3.westus2.azurecontainer.io/score\n"
     ]
    }
   ],
   "source": [
    "print(service.scoring_uri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reload the model\n",
    "\n",
    "from azureml.core import Webservice\n",
    "\n",
    "\n",
    "service_name = 'diabetes-custom-service2'\n",
    "service = Webservice(ws, service_name)\n",
    "\n",
    "print(service.name, service.scoring_uri, sep='\\t')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[210.4030796084371], [211.80666178533193]]\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "input_payload = json.dumps({\n",
    "    'data': [\n",
    "        [59, 2, 32.1, 101.0, 157, 93.2, 38.0, 4.0, 4.8598, 87],\n",
    "        [69, 2, 32.1, 101.0, 157, 93.2, 38.0, 4.0, 4.8598, 87]\n",
    "    ],\n",
    "    'method': 'predict'  # If you have a classification model, you can get probabilities by changing this to 'predict_proba'.\n",
    "})\n",
    "\n",
    "output = service.run(input_payload)\n",
    "\n",
    "print(output)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#service.delete()"
   ]
  }
 ],
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   "name": "python3-azureml"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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