02 Modélisation avec Azure ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Analyse ML avec Azure ML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='https://github.com/retkowsky/images/blob/master/AzureMLservicebanniere.png?raw=true'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Données Framingham\n",
    "\n",
    "https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset\n",
    "\n",
    "Attributes/columns:\n",
    "\n",
    "- male: 0 = Female; 1 = Male\n",
    "- age: Age at exam time\n",
    "- education: 1 = Some High School; 2 = High School or GED; 3 = Some College or Vocational School; 4 = college\n",
    "- currentSmoker: 0 = nonsmoker; 1 = smoker\n",
    "- cigsPerDay: number of cigarettes smoked per day (estimated average)\n",
    "- BPMeds: 0 = Not on Blood Pressure medications; 1 = Is on Blood Pressure medications\n",
    "- prevalentStroke\n",
    "- prevalentHyp\n",
    "- diabetes: 0 = No; 1 = Yes\n",
    "- totChol in mg/dL\n",
    "- sysBP in mmHg\n",
    "- diaBP in mmHg\n",
    "- BMI: Body Mass Index calculated as: Weight (kg) / Height(meter-squared)\n",
    "- heartRate: Beats/Min (Ventricular)\n",
    "- glucose in mg/dL\n",
    "\n",
    "- TenYearCHD - Did the person get heart disease in the 10 years study period?\n",
    "label; 0 = No for heart disease, 1 = Yes for heart disease;\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Informations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) \\n[GCC 7.3.0]'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-10-13 07:47:46.653574\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "now = datetime.datetime.now()\n",
    "print(now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Version Azure ML : 1.15.0\n"
     ]
    }
   ],
   "source": [
    "#Version\n",
    "import azureml.core\n",
    "print(\"Version Azure ML :\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Workspace Azure ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "subscription_id = \"AREMPLACER\" \n",
    "resource_group = \"AREMPLACER\" \n",
    "workspace_name = \"AREMPLACER\" \n",
    "workspace_region = \"AREMPLACER\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Expérience Azure ML\n",
    "experiment_name = 'monexperimentation'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "project_dir = './monprojet'\n",
    "#deployment_dir = './deploy'\n",
    "model_name = 'monmodele'\n",
    "model_description = 'madescription'\n",
    "vm_name = \"monclustercpu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import logging\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.core.compute import ComputeTarget\n",
    "from azureml.core.model import Model\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun\n",
    "from azureml.core import Workspace\n",
    "from azureml.core import Environment\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "from azureml.widgets import RunDetails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Workspace configuration succeeded\n"
     ]
    }
   ],
   "source": [
    "ws = Workspace.create(\n",
    "    name = workspace_name,\n",
    "    subscription_id = subscription_id,\n",
    "    resource_group = resource_group, \n",
    "    location = workspace_region,\n",
    "    exist_ok = True) #Leverage existing\n",
    "\n",
    "ws.write_config()\n",
    "print('Workspace configuration succeeded')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Workspace name: workshoplcl\n",
      "Azure region: westeurope\n",
      "Resource group: workshoplcl-rg\n"
     ]
    }
   ],
   "source": [
    "ws = Workspace.from_config()\n",
    "print('Workspace name: ' + ws.name, \n",
    "      'Azure region: ' + ws.location, \n",
    "      'Resource group: ' + ws.resource_group, sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compute Targets:\n",
      "\t nbookinstance : ComputeInstance\n",
      "Datastores:\n",
      "\t azureml_globaldatasets : AzureBlob\n",
      "\t workspaceblobstore : AzureBlob\n",
      "\t workspacefilestore : AzureFile\n",
      "Datasets:\n"
     ]
    }
   ],
   "source": [
    "from azureml.core import ComputeTarget, Datastore, Dataset\n",
    "\n",
    "print(\"Compute Targets:\")\n",
    "for compute_name in ws.compute_targets:\n",
    "    compute = ws.compute_targets[compute_name]\n",
    "    print(\"\\t\", compute.name, ':', compute.type)\n",
    "    \n",
    "print(\"Datastores:\")\n",
    "for datastore_name in ws.datastores:\n",
    "    datastore = Datastore.get(ws, datastore_name)\n",
    "    print(\"\\t\", datastore.name, ':', datastore.datastore_type)\n",
    "    \n",
    "print(\"Datasets:\")\n",
    "for dataset_name in list(ws.datasets.keys()):\n",
    "    dataset = Dataset.get_by_name(ws, dataset_name)\n",
    "    print(\"\\t\", dataset.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Expérimentation Azure ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(project_dir):\n",
    "    os.makedirs(project_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment = Experiment(ws, experiment_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': '/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/workshoplcl-rg/providers/Microsoft.MachineLearningServices/workspaces/workshoplcl',\n",
       " 'name': 'workshoplcl',\n",
       " 'location': 'westeurope',\n",
       " 'type': 'Microsoft.MachineLearningServices/workspaces',\n",
       " 'tags': {},\n",
       " 'sku': 'Basic',\n",
       " 'workspaceid': '8e72db2b-253f-43ad-b178-21834bb76976',\n",
       " 'description': '',\n",
       " 'friendlyName': 'workshoplcl',\n",
       " 'creationTime': '2020-10-13T07:30:49.1872058+00:00',\n",
       " 'keyVault': '/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourcegroups/workshoplcl-rg/providers/microsoft.keyvault/vaults/workshoplcl0643011812',\n",
       " 'applicationInsights': '/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourcegroups/workshoplcl-rg/providers/microsoft.insights/components/workshoplcl0507464134',\n",
       " 'identityPrincipalId': 'cc04ec13-5ba2-469f-9d48-93a456fbf38d',\n",
       " 'identityTenantId': '72f988bf-86f1-41af-91ab-2d7cd011db47',\n",
       " 'identityType': 'SystemAssigned',\n",
       " 'storageAccount': '/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourcegroups/workshoplcl-rg/providers/microsoft.storage/storageaccounts/workshoplcl3468771077',\n",
       " 'hbiWorkspace': False,\n",
       " 'discoveryUrl': 'https://westeurope.experiments.azureml.net/discovery',\n",
       " 'notebookInfo': {'fqdn': 'ml-workshoplcl-westeurope-8e72db2b-253f-43ad-b178-21834bb76976.notebooks.azure.net',\n",
       "  'resource_id': 'dbebc869bf3045bd98fa33854b9e1b21'}}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Infos\n",
    "from azureml.core.workspace import Workspace\n",
    "ws = Workspace.from_config()\n",
    "ws.get_details()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Azure ML Compute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'Standard_D1_v2',\n",
       "  'vCPUs': 1,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 3.5,\n",
       "  'maxResourceVolumeMB': 51200},\n",
       " {'name': 'Standard_D2_v2',\n",
       "  'vCPUs': 2,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 7.0,\n",
       "  'maxResourceVolumeMB': 102400},\n",
       " {'name': 'Standard_D3_v2',\n",
       "  'vCPUs': 4,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 14.0,\n",
       "  'maxResourceVolumeMB': 204800},\n",
       " {'name': 'Standard_D4_v2',\n",
       "  'vCPUs': 8,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 28.0,\n",
       "  'maxResourceVolumeMB': 409600},\n",
       " {'name': 'Standard_D11_v2',\n",
       "  'vCPUs': 2,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 14.0,\n",
       "  'maxResourceVolumeMB': 102400},\n",
       " {'name': 'Standard_D12_v2',\n",
       "  'vCPUs': 4,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 28.0,\n",
       "  'maxResourceVolumeMB': 204800},\n",
       " {'name': 'Standard_D13_v2',\n",
       "  'vCPUs': 8,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 56.0,\n",
       "  'maxResourceVolumeMB': 409600},\n",
       " {'name': 'Standard_D14_v2',\n",
       "  'vCPUs': 16,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 112.0,\n",
       "  'maxResourceVolumeMB': 819200},\n",
       " {'name': 'Standard_DS1_v2',\n",
       "  'vCPUs': 1,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 3.5,\n",
       "  'maxResourceVolumeMB': 7168},\n",
       " {'name': 'Standard_DS2_v2',\n",
       "  'vCPUs': 2,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 7.0,\n",
       "  'maxResourceVolumeMB': 14336},\n",
       " {'name': 'Standard_DS3_v2',\n",
       "  'vCPUs': 4,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 14.0,\n",
       "  'maxResourceVolumeMB': 28672},\n",
       " {'name': 'Standard_DS4_v2',\n",
       "  'vCPUs': 8,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 28.0,\n",
       "  'maxResourceVolumeMB': 57344},\n",
       " {'name': 'Standard_DS5_v2',\n",
       "  'vCPUs': 16,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 56.0,\n",
       "  'maxResourceVolumeMB': 114688},\n",
       " {'name': 'Standard_DS11_v2',\n",
       "  'vCPUs': 2,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 14.0,\n",
       "  'maxResourceVolumeMB': 28672},\n",
       " {'name': 'Standard_DS12_v2',\n",
       "  'vCPUs': 4,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 28.0,\n",
       "  'maxResourceVolumeMB': 57344},\n",
       " {'name': 'Standard_DS13_v2',\n",
       "  'vCPUs': 8,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 56.0,\n",
       "  'maxResourceVolumeMB': 114688},\n",
       " {'name': 'Standard_DS14_v2',\n",
       "  'vCPUs': 16,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 112.0,\n",
       "  'maxResourceVolumeMB': 229376},\n",
       " {'name': 'Standard_D2_v3',\n",
       "  'vCPUs': 2,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 8.0,\n",
       "  'maxResourceVolumeMB': 51200},\n",
       " {'name': 'Standard_D4_v3',\n",
       "  'vCPUs': 4,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 16.0,\n",
       "  'maxResourceVolumeMB': 102400},\n",
       " {'name': 'Standard_D8_v3',\n",
       "  'vCPUs': 8,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 32.0,\n",
       "  'maxResourceVolumeMB': 204800},\n",
       " {'name': 'Standard_D16_v3',\n",
       "  'vCPUs': 16,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 64.0,\n",
       "  'maxResourceVolumeMB': 409600},\n",
       " {'name': 'Standard_D32_v3',\n",
       "  'vCPUs': 32,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 128.0,\n",
       "  'maxResourceVolumeMB': 819200},\n",
       " {'name': 'Standard_D64_v3',\n",
       "  'vCPUs': 64,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 256.0,\n",
       "  'maxResourceVolumeMB': 1638400},\n",
       " {'name': 'Standard_D2s_v3',\n",
       "  'vCPUs': 2,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 8.0,\n",
       "  'maxResourceVolumeMB': 16384},\n",
       " {'name': 'Standard_D4s_v3',\n",
       "  'vCPUs': 4,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 16.0,\n",
       "  'maxResourceVolumeMB': 32768},\n",
       " {'name': 'Standard_D8s_v3',\n",
       "  'vCPUs': 8,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 32.0,\n",
       "  'maxResourceVolumeMB': 65536},\n",
       " {'name': 'Standard_D16s_v3',\n",
       "  'vCPUs': 16,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 64.0,\n",
       "  'maxResourceVolumeMB': 131072},\n",
       " {'name': 'Standard_D32s_v3',\n",
       "  'vCPUs': 32,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 128.0,\n",
       "  'maxResourceVolumeMB': 262144},\n",
       " {'name': 'Standard_D64s_v3',\n",
       "  'vCPUs': 64,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 256.0,\n",
       "  'maxResourceVolumeMB': 524288},\n",
       " {'name': 'Standard_M8-2ms',\n",
       "  'vCPUs': 8,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 218.75,\n",
       "  'maxResourceVolumeMB': 256000},\n",
       " {'name': 'Standard_M8-4ms',\n",
       "  'vCPUs': 8,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 218.75,\n",
       "  'maxResourceVolumeMB': 256000},\n",
       " {'name': 'Standard_M8ms',\n",
       "  'vCPUs': 8,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 218.75,\n",
       "  'maxResourceVolumeMB': 256000},\n",
       " {'name': 'Standard_M16-4ms',\n",
       "  'vCPUs': 16,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 437.5,\n",
       "  'maxResourceVolumeMB': 512000},\n",
       " {'name': 'Standard_M16-8ms',\n",
       "  'vCPUs': 16,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 437.5,\n",
       "  'maxResourceVolumeMB': 512000},\n",
       " {'name': 'Standard_M16ms',\n",
       "  'vCPUs': 16,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 437.5,\n",
       "  'maxResourceVolumeMB': 512000},\n",
       " {'name': 'Standard_M32-8ms',\n",
       "  'vCPUs': 32,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 875.0,\n",
       "  'maxResourceVolumeMB': 1024000},\n",
       " {'name': 'Standard_M32-16ms',\n",
       "  'vCPUs': 32,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 875.0,\n",
       "  'maxResourceVolumeMB': 1024000},\n",
       " {'name': 'Standard_M32ls',\n",
       "  'vCPUs': 32,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 256.0,\n",
       "  'maxResourceVolumeMB': 1024000},\n",
       " {'name': 'Standard_M32ms',\n",
       "  'vCPUs': 32,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 875.0,\n",
       "  'maxResourceVolumeMB': 1024000},\n",
       " {'name': 'Standard_M32ts',\n",
       "  'vCPUs': 32,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 192.0,\n",
       "  'maxResourceVolumeMB': 1024000},\n",
       " {'name': 'Standard_M64-16ms',\n",
       "  'vCPUs': 64,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 1750.0,\n",
       "  'maxResourceVolumeMB': 2048000},\n",
       " {'name': 'Standard_M64-32ms',\n",
       "  'vCPUs': 64,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 1750.0,\n",
       "  'maxResourceVolumeMB': 2048000},\n",
       " {'name': 'Standard_M64ls',\n",
       "  'vCPUs': 64,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 512.0,\n",
       "  'maxResourceVolumeMB': 2048000},\n",
       " {'name': 'Standard_M64ms',\n",
       "  'vCPUs': 64,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 1750.0,\n",
       "  'maxResourceVolumeMB': 2048000},\n",
       " {'name': 'Standard_M64s',\n",
       "  'vCPUs': 64,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 1000.0,\n",
       "  'maxResourceVolumeMB': 2048000},\n",
       " {'name': 'Standard_M128-32ms',\n",
       "  'vCPUs': 128,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 3800.0,\n",
       "  'maxResourceVolumeMB': 4096000},\n",
       " {'name': 'Standard_M128-64ms',\n",
       "  'vCPUs': 128,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 3800.0,\n",
       "  'maxResourceVolumeMB': 4096000},\n",
       " {'name': 'Standard_M128ms',\n",
       "  'vCPUs': 128,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 3800.0,\n",
       "  'maxResourceVolumeMB': 4096000},\n",
       " {'name': 'Standard_M128s',\n",
       "  'vCPUs': 128,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 2000.0,\n",
       "  'maxResourceVolumeMB': 4096000},\n",
       " {'name': 'Standard_M64',\n",
       "  'vCPUs': 64,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 1000.0,\n",
       "  'maxResourceVolumeMB': 8192000},\n",
       " {'name': 'Standard_M64m',\n",
       "  'vCPUs': 64,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 1750.0,\n",
       "  'maxResourceVolumeMB': 8192000},\n",
       " {'name': 'Standard_M128',\n",
       "  'vCPUs': 128,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 2000.0,\n",
       "  'maxResourceVolumeMB': 16384000},\n",
       " {'name': 'Standard_M128m',\n",
       "  'vCPUs': 128,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 3800.0,\n",
       "  'maxResourceVolumeMB': 16384000},\n",
       " {'name': 'Standard_D1',\n",
       "  'vCPUs': 1,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 3.5,\n",
       "  'maxResourceVolumeMB': 51200},\n",
       " {'name': 'Standard_D2',\n",
       "  'vCPUs': 2,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 7.0,\n",
       "  'maxResourceVolumeMB': 102400},\n",
       " {'name': 'Standard_D3',\n",
       "  'vCPUs': 4,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 14.0,\n",
       "  'maxResourceVolumeMB': 204800},\n",
       " {'name': 'Standard_D4',\n",
       "  'vCPUs': 8,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 28.0,\n",
       "  'maxResourceVolumeMB': 409600},\n",
       " {'name': 'Standard_D11',\n",
       "  'vCPUs': 2,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 14.0,\n",
       "  'maxResourceVolumeMB': 102400},\n",
       " {'name': 'Standard_D12',\n",
       "  'vCPUs': 4,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 28.0,\n",
       "  'maxResourceVolumeMB': 204800},\n",
       " {'name': 'Standard_D13',\n",
       "  'vCPUs': 8,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 56.0,\n",
       "  'maxResourceVolumeMB': 409600},\n",
       " {'name': 'Standard_D14',\n",
       "  'vCPUs': 16,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 112.0,\n",
       "  'maxResourceVolumeMB': 819200},\n",
       " {'name': 'Standard_HB120rs_v2',\n",
       "  'vCPUs': 120,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 468.75,\n",
       "  'maxResourceVolumeMB': 960000},\n",
       " {'name': 'Standard_D15_v2',\n",
       "  'vCPUs': 20,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 140.0,\n",
       "  'maxResourceVolumeMB': 1024000},\n",
       " {'name': 'Standard_DS15_v2',\n",
       "  'vCPUs': 20,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 140.0,\n",
       "  'maxResourceVolumeMB': 286720},\n",
       " {'name': 'Standard_F2s_v2',\n",
       "  'vCPUs': 2,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 4.0,\n",
       "  'maxResourceVolumeMB': 16384},\n",
       " {'name': 'Standard_F4s_v2',\n",
       "  'vCPUs': 4,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 8.0,\n",
       "  'maxResourceVolumeMB': 32768},\n",
       " {'name': 'Standard_F8s_v2',\n",
       "  'vCPUs': 8,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 16.0,\n",
       "  'maxResourceVolumeMB': 65536},\n",
       " {'name': 'Standard_F16s_v2',\n",
       "  'vCPUs': 16,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 32.0,\n",
       "  'maxResourceVolumeMB': 131072},\n",
       " {'name': 'Standard_F32s_v2',\n",
       "  'vCPUs': 32,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 64.0,\n",
       "  'maxResourceVolumeMB': 262144},\n",
       " {'name': 'Standard_F64s_v2',\n",
       "  'vCPUs': 64,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 128.0,\n",
       "  'maxResourceVolumeMB': 524288},\n",
       " {'name': 'Standard_F72s_v2',\n",
       "  'vCPUs': 72,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 144.0,\n",
       "  'maxResourceVolumeMB': 589824},\n",
       " {'name': 'Standard_NC6s_v3',\n",
       "  'vCPUs': 6,\n",
       "  'gpus': 1,\n",
       "  'memoryGB': 112.0,\n",
       "  'maxResourceVolumeMB': 344064},\n",
       " {'name': 'Standard_NC12s_v3',\n",
       "  'vCPUs': 12,\n",
       "  'gpus': 2,\n",
       "  'memoryGB': 224.0,\n",
       "  'maxResourceVolumeMB': 688128},\n",
       " {'name': 'Standard_NC24rs_v3',\n",
       "  'vCPUs': 24,\n",
       "  'gpus': 4,\n",
       "  'memoryGB': 448.0,\n",
       "  'maxResourceVolumeMB': 1376256},\n",
       " {'name': 'Standard_NC24s_v3',\n",
       "  'vCPUs': 24,\n",
       "  'gpus': 4,\n",
       "  'memoryGB': 448.0,\n",
       "  'maxResourceVolumeMB': 1376256},\n",
       " {'name': 'Standard_NV6',\n",
       "  'vCPUs': 6,\n",
       "  'gpus': 1,\n",
       "  'memoryGB': 56.0,\n",
       "  'maxResourceVolumeMB': 389120},\n",
       " {'name': 'Standard_NV12',\n",
       "  'vCPUs': 12,\n",
       "  'gpus': 2,\n",
       "  'memoryGB': 112.0,\n",
       "  'maxResourceVolumeMB': 696320},\n",
       " {'name': 'Standard_NV24',\n",
       "  'vCPUs': 24,\n",
       "  'gpus': 4,\n",
       "  'memoryGB': 224.0,\n",
       "  'maxResourceVolumeMB': 1474560},\n",
       " {'name': 'Standard_NV12s_v3',\n",
       "  'vCPUs': 12,\n",
       "  'gpus': 1,\n",
       "  'memoryGB': 112.0,\n",
       "  'maxResourceVolumeMB': 344064},\n",
       " {'name': 'Standard_NV24s_v3',\n",
       "  'vCPUs': 24,\n",
       "  'gpus': 2,\n",
       "  'memoryGB': 224.0,\n",
       "  'maxResourceVolumeMB': 688128},\n",
       " {'name': 'Standard_NV48s_v3',\n",
       "  'vCPUs': 48,\n",
       "  'gpus': 4,\n",
       "  'memoryGB': 448.0,\n",
       "  'maxResourceVolumeMB': 1376256},\n",
       " {'name': 'Standard_NC6',\n",
       "  'vCPUs': 6,\n",
       "  'gpus': 1,\n",
       "  'memoryGB': 56.0,\n",
       "  'maxResourceVolumeMB': 389120},\n",
       " {'name': 'Standard_NC12',\n",
       "  'vCPUs': 12,\n",
       "  'gpus': 2,\n",
       "  'memoryGB': 112.0,\n",
       "  'maxResourceVolumeMB': 696320},\n",
       " {'name': 'Standard_NC24',\n",
       "  'vCPUs': 24,\n",
       "  'gpus': 4,\n",
       "  'memoryGB': 224.0,\n",
       "  'maxResourceVolumeMB': 1474560},\n",
       " {'name': 'Standard_NC24r',\n",
       "  'vCPUs': 24,\n",
       "  'gpus': 4,\n",
       "  'memoryGB': 224.0,\n",
       "  'maxResourceVolumeMB': 1474560},\n",
       " {'name': 'Standard_ND6s',\n",
       "  'vCPUs': 6,\n",
       "  'gpus': 1,\n",
       "  'memoryGB': 112.0,\n",
       "  'maxResourceVolumeMB': 344064},\n",
       " {'name': 'Standard_ND12s',\n",
       "  'vCPUs': 12,\n",
       "  'gpus': 2,\n",
       "  'memoryGB': 224.0,\n",
       "  'maxResourceVolumeMB': 688128},\n",
       " {'name': 'Standard_ND24rs',\n",
       "  'vCPUs': 24,\n",
       "  'gpus': 4,\n",
       "  'memoryGB': 448.0,\n",
       "  'maxResourceVolumeMB': 1376256},\n",
       " {'name': 'Standard_ND24s',\n",
       "  'vCPUs': 24,\n",
       "  'gpus': 4,\n",
       "  'memoryGB': 448.0,\n",
       "  'maxResourceVolumeMB': 1376256},\n",
       " {'name': 'Standard_ND40rs_v2',\n",
       "  'vCPUs': 40,\n",
       "  'gpus': 8,\n",
       "  'memoryGB': 672.0,\n",
       "  'maxResourceVolumeMB': 2969600},\n",
       " {'name': 'Standard_HC44rs',\n",
       "  'vCPUs': 44,\n",
       "  'gpus': 0,\n",
       "  'memoryGB': 327.83,\n",
       "  'maxResourceVolumeMB': 716800},\n",
       " {'name': 'Standard_NC6s_v2',\n",
       "  'vCPUs': 6,\n",
       "  'gpus': 1,\n",
       "  'memoryGB': 112.0,\n",
       "  'maxResourceVolumeMB': 344064},\n",
       " {'name': 'Standard_NC12s_v2',\n",
       "  'vCPUs': 12,\n",
       "  'gpus': 2,\n",
       "  'memoryGB': 224.0,\n",
       "  'maxResourceVolumeMB': 688128},\n",
       " {'name': 'Standard_NC24rs_v2',\n",
       "  'vCPUs': 24,\n",
       "  'gpus': 4,\n",
       "  'memoryGB': 448.0,\n",
       "  'maxResourceVolumeMB': 1376256},\n",
       " {'name': 'Standard_NC24s_v2',\n",
       "  'vCPUs': 24,\n",
       "  'gpus': 4,\n",
       "  'memoryGB': 448.0,\n",
       "  'maxResourceVolumeMB': 1376256}]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Liste des compute servers disponibles\n",
    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
    "AmlCompute.supported_vmsizes(workspace = ws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating a new compute target...\n",
      "Creating\n",
      "Succeeded..................\n",
      "AmlCompute wait for completion finished\n",
      "\n",
      "Minimum number of nodes requested have been provisioned\n",
      "CPU times: user 329 ms, sys: 24.5 ms, total: 354 ms\n",
      "Wall time: 1min 52s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
    "from azureml.core.compute_target import ComputeTargetException\n",
    "\n",
    "try:\n",
    "    compute_target = ComputeTarget(workspace=ws, name=vm_name)\n",
    "    print('Found existing compute target.')\n",
    "except ComputeTargetException:\n",
    "    print('Creating a new compute target...')\n",
    "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D12_V2',\n",
    "                                                           min_nodes=1, \n",
    "                                                           max_nodes=2, \n",
    "                                                           idle_seconds_before_scaledown=1200)\n",
    "\n",
    "    # create the cluster\n",
    "    compute_target = ComputeTarget.create(ws, vm_name, compute_config)\n",
    "    # Show output\n",
    "    compute_target.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compute Targets:\n",
      "\t nbookinstance : ComputeInstance\n",
      "\t monclustercpu : AmlCompute\n"
     ]
    }
   ],
   "source": [
    "from azureml.core import ComputeTarget, Datastore, Dataset\n",
    "\n",
    "print(\"Compute Targets:\")\n",
    "for compute_name in ws.compute_targets:\n",
    "    compute = ws.compute_targets[compute_name]\n",
    "    print(\"\\t\", compute.name, ':', compute.type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>monexperimentation</td><td>workshoplcl</td><td><a href=\"https://ml.azure.com/experiments/monexperimentation?wsid=/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourcegroups/workshoplcl-rg/workspaces/workshoplcl\" 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: monexperimentation,\n",
       "Workspace: workshoplcl)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Environnement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Docker based environment avec scikit-learn\n",
    "training_venv = Environment(\"training_env\")\n",
    "\n",
    "training_venv.docker.enabled = True\n",
    "training_venv.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing ./monprojet/train.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile $project_dir/train.py\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "import os\n",
    "\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "dataset_url = ('https://github.com/raw/retkowsky/WorkshopMLOps/master/framingham.csv')\n",
    "df = pd.read_csv(dataset_url)\n",
    "\n",
    "smoke = (df['currentSmoker']==1)\n",
    "df.loc[smoke,'cigsPerDay'] = df.loc[smoke,'cigsPerDay'].fillna(df.loc[smoke,'cigsPerDay'].mean())\n",
    "\n",
    "df['BPMeds'].fillna(0, inplace = True)\n",
    "df['glucose'].fillna(df.glucose.mean(), inplace = True)\n",
    "df['totChol'].fillna(df.totChol.mean(), inplace = True)\n",
    "df['education'].fillna(1, inplace = True)\n",
    "df['BMI'].fillna(df.BMI.mean(), inplace = True)\n",
    "df['heartRate'].fillna(df.heartRate.mean(), inplace = True)\n",
    "\n",
    "features = df.iloc[:,:-1]\n",
    "result = df.iloc[:,-1] # the last column is what we are about to forecast\n",
    "\n",
    "# Train et Test\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, result, test_size = 0.2, random_state = 14)\n",
    "\n",
    "# RandomForest classifier\n",
    "clf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Create a selector object that will use the random forest classifier to identify\n",
    "# features that have an importance of more than 0.12\n",
    "sfm = SelectFromModel(clf, threshold=0.12)\n",
    "\n",
    "# Train the selector\n",
    "sfm.fit(X_train, y_train)\n",
    "\n",
    "# Features selected\n",
    "feat_labels = list(features.columns.values) # creating a list with features' names\n",
    "for feature_list_index in sfm.get_support(indices=True):\n",
    "    print(feat_labels[feature_list_index])\n",
    "\n",
    "# Feature importance\n",
    "importances = clf.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in clf.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "print(\"Feature ranking =\")\n",
    "for f in range(X_train.shape[1]):\n",
    "    print(\"%d. feature %d (%f)\" % (f + 1, indices[f], importances[indices[f]]))\n",
    "\n",
    "# With only imporant features. Can check X_important_train.shape[1]\n",
    "X_important_train = sfm.transform(X_train)\n",
    "X_important_test = sfm.transform(X_test)\n",
    "\n",
    "clf_important = RandomForestClassifier(n_estimators=10000, random_state=0, n_jobs=-1)\n",
    "clf_important.fit(X_important_train, y_train)\n",
    "\n",
    "# Calcul Accuracy\n",
    "from sklearn import metrics\n",
    "y_pred=clf.predict(X_test)\n",
    "accuracy=metrics.accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy du modèle :\", accuracy)\n",
    "\n",
    "# Export modèle\n",
    "os.makedirs('./outputs/model', exist_ok=True)\n",
    "\n",
    "filename = './outputs/model/chd-rf-model'\n",
    "pickle.dump(clf_important, open(filename, 'wb'))\n",
    "print(\"model saved in ././outputs/model/chd-rf-model folder\")\n",
    "print(\"Saving model completed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>male</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>currentSmoker</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentStroke</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>26.97</td>\n",
       "      <td>80.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>28.73</td>\n",
       "      <td>95.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>127.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>25.34</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>28.58</td>\n",
       "      <td>65.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>23.10</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   male  age  education  currentSmoker  cigsPerDay  BPMeds  prevalentStroke  \\\n",
       "0     1   39        4.0              0         0.0     0.0                0   \n",
       "1     0   46        2.0              0         0.0     0.0                0   \n",
       "2     1   48        1.0              1        20.0     0.0                0   \n",
       "3     0   61        3.0              1        30.0     0.0                0   \n",
       "4     0   46        3.0              1        23.0     0.0                0   \n",
       "\n",
       "   prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  \\\n",
       "0             0         0    195.0  106.0   70.0  26.97       80.0     77.0   \n",
       "1             0         0    250.0  121.0   81.0  28.73       95.0     76.0   \n",
       "2             0         0    245.0  127.5   80.0  25.34       75.0     70.0   \n",
       "3             1         0    225.0  150.0   95.0  28.58       65.0    103.0   \n",
       "4             0         0    285.0  130.0   84.0  23.10       85.0     85.0   \n",
       "\n",
       "   TenYearCHD  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           1  \n",
       "4           0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "url = ('https://github.com/raw/retkowsky/WorkshopMLOps/master/framingham.csv')\n",
    "df = pd.read_csv(url)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Définition de tags pour le run\n",
    "tagsdurun = {\"Type\": \"test\" , \"Langage\" : \"Python\" , \"Framework\" : \"Scikit-Learn\", \"Team\" : \"DataScience\" , \"Pays\" : \"France\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>monexperimentation</td><td>monexperimentation_1602575538_84ebca82</td><td>azureml.scriptrun</td><td>Starting</td><td><a href=\"https://ml.azure.com/experiments/monexperimentation/runs/monexperimentation_1602575538_84ebca82?wsid=/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourcegroups/workshoplcl-rg/workspaces/workshoplcl\" 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: monexperimentation,\n",
       "Id: monexperimentation_1602575538_84ebca82,\n",
       "Type: azureml.scriptrun,\n",
       "Status: Starting)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from azureml.core import ScriptRunConfig\n",
    "from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n",
    "\n",
    "src = ScriptRunConfig(source_directory=project_dir, script='train.py')\n",
    "\n",
    "# Compute Target\n",
    "src.run_config.target = compute_target.name\n",
    "\n",
    "# Set environment\n",
    "src.run_config.environment = training_venv\n",
    " \n",
    "run = experiment.submit(config=src, tags=tagsdurun)\n",
    "run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Prévoir 2 minutes de temps de traitement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Widget pour suivre l'avancement du run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b3157fbbb53b4839b3915498d0c2cdb2",
       "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\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/experiments/monexperimentation/runs/monexperimentation_1602575538_84ebca82?wsid=/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourcegroups/workshoplcl-rg/workspaces/workshoplcl\", \"run_id\": \"monexperimentation_1602575538_84ebca82\", \"run_properties\": {\"run_id\": \"monexperimentation_1602575538_84ebca82\", \"created_utc\": \"2020-10-13T07:52:23.480615Z\", \"properties\": {\"_azureml.ComputeTargetType\": \"amlcompute\", \"ContentSnapshotId\": \"0abc68e1-ae04-4ca0-aa19-e91cff0861be\", \"ProcessInfoFile\": \"azureml-logs/process_info.json\", \"ProcessStatusFile\": \"azureml-logs/process_status.json\"}, \"tags\": {\"Type\": \"test\", \"Langage\": \"Python\", \"Framework\": \"Scikit-Learn\", \"Team\": \"DataScience\", \"Pays\": \"France\", \"_aml_system_ComputeTargetStatus\": \"{\\\"AllocationState\\\":\\\"steady\\\",\\\"PreparingNodeCount\\\":0,\\\"RunningNodeCount\\\":0,\\\"CurrentNodeCount\\\":1}\"}, \"script_name\": null, \"arguments\": null, \"end_time_utc\": \"2020-10-13T08:02:02.98967Z\", \"status\": \"Completed\", \"log_files\": {\"azureml-logs/20_image_build_log.txt\": \"https://workshoplcl3468771077.blob.core.windows.net/azureml/ExperimentRun/dcid.monexperimentation_1602575538_84ebca82/azureml-logs/20_image_build_log.txt?sv=2019-02-02&sr=b&sig=saDVzAYNluHjwXac4v15vo05hTOydcKANilTJKfyYZY%3D&st=2020-10-13T07%3A51%3A52Z&se=2020-10-13T16%3A01%3A52Z&sp=r\", \"azureml-logs/55_azureml-execution-tvmps_acf50f4fa1807c7df2240e8b769cf8ea6fe893276aaecb7682a3a735ba74c436_d.txt\": 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[\"azureml-logs/65_job_prep-tvmps_acf50f4fa1807c7df2240e8b769cf8ea6fe893276aaecb7682a3a735ba74c436_d.txt\"], [\"azureml-logs/70_driver_log.txt\"], [\"azureml-logs/75_job_post-tvmps_acf50f4fa1807c7df2240e8b769cf8ea6fe893276aaecb7682a3a735ba74c436_d.txt\"], [\"logs/azureml/119_azureml.log\"]], \"run_duration\": \"0:09:39\"}, \"child_runs\": [], \"children_metrics\": {}, \"run_metrics\": [], \"run_logs\": \"2020-10-13 08:00:51,033|azureml|DEBUG|Inputs:: kwargs: {'OutputCollection': True, 'EnableMLflowTracking': True, 'snapshotProject': True, 'only_in_process_features': True, 'skip_track_logs_dir': True}, track_folders: None, deny_list: None, directories_to_watch: []\\n2020-10-13 08:00:51,034|azureml.history._tracking.PythonWorkingDirectory|DEBUG|Execution target type: batchai\\n2020-10-13 08:00:51,043|azureml.history._tracking.PythonWorkingDirectory|DEBUG|Failed to import pyspark with error: No module named 'pyspark'\\n2020-10-13 08:00:51,043|azureml.history._tracking.PythonWorkingDirectory.workingdir|DEBUG|Pinning working directory for filesystems: ['pyfs']\\n2020-10-13 08:00:51,445|azureml.core.run|DEBUG|Adding new factory <function ScriptRun._from_run_dto at 0x7f9be4651158> for run source azureml.scriptrun\\n2020-10-13 08:00:51,488|azureml.core.authentication.TokenRefresherDaemon|DEBUG|Starting daemon and triggering first instance\\n2020-10-13 08:00:51,498|azureml._restclient.clientbase|INFO|Created a worker pool for first use\\n2020-10-13 08:00:51,499|azureml.core.authentication|DEBUG|Time to expire 1813891.500998 seconds\\n2020-10-13 08:00:51,499|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://westeurope.experiments.azureml.net.\\n2020-10-13 08:00:51,499|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://westeurope.experiments.azureml.net.\\n2020-10-13 08:00:51,499|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://westeurope.experiments.azureml.net.\\n2020-10-13 08:00:51,499|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://westeurope.experiments.azureml.net.\\n2020-10-13 08:00:51,500|azureml._restclient.clientbase|DEBUG|ClientBase: Calling get with url None\\n2020-10-13 08:00:51,557|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://westeurope.experiments.azureml.net.\\n2020-10-13 08:00:51,558|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://westeurope.experiments.azureml.net.\\n2020-10-13 08:00:51,558|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://westeurope.experiments.azureml.net.\\n2020-10-13 08:00:51,604|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.RunClient.get-async:False|DEBUG|[START]\\n2020-10-13 08:00:51,604|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.RunClient|DEBUG|ClientBase: Calling get with url /history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/runs/{runId}\\n2020-10-13 08:00:51,695|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.RunClient.get-async:False|DEBUG|[STOP]\\n2020-10-13 08:00:51,696|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82|DEBUG|Constructing run from dto. type: azureml.scriptrun, source: None, props: {'_azureml.ComputeTargetType': 'amlcompute', 'ContentSnapshotId': '0abc68e1-ae04-4ca0-aa19-e91cff0861be', 'ProcessInfoFile': 'azureml-logs/process_info.json', 'ProcessStatusFile': 'azureml-logs/process_status.json'}\\n2020-10-13 08:00:51,696|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunContextManager|DEBUG|Valid logs dir, setting up content loader\\n2020-10-13 08:00:51,697|azureml|WARNING|Could not import azureml.mlflow or azureml.contrib.mlflow mlflow APIs will not run against AzureML services.  Add azureml-mlflow as a conda dependency for the run if this behavior is desired\\n2020-10-13 08:00:51,697|azureml.WorkerPool|DEBUG|[START]\\n2020-10-13 08:00:51,697|azureml.SendRunKillSignal|DEBUG|[START]\\n2020-10-13 08:00:51,697|azureml.RunStatusContext|DEBUG|[START]\\n2020-10-13 08:00:51,698|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunContextManager.RunStatusContext|DEBUG|[START]\\n2020-10-13 08:00:51,698|azureml.MetricsClient|DEBUG|[START]\\n2020-10-13 08:00:51,698|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient|DEBUG|[START]\\n2020-10-13 08:00:51,698|azureml.WorkingDirectoryCM|DEBUG|[START]\\n2020-10-13 08:00:51,698|azureml.history._tracking.PythonWorkingDirectory.workingdir|DEBUG|[START]\\n2020-10-13 08:00:51,698|azureml.history._tracking.PythonWorkingDirectory|INFO|Current working dir: /mnt/batch/tasks/shared/LS_root/jobs/workshoplcl/azureml/monexperimentation_1602575538_84ebca82/mounts/workspaceblobstore/azureml/monexperimentation_1602575538_84ebca82\\n2020-10-13 08:00:51,698|azureml.history._tracking.PythonWorkingDirectory.workingdir|DEBUG|Calling pyfs\\n2020-10-13 08:00:51,699|azureml.history._tracking.PythonWorkingDirectory.workingdir|DEBUG|Storing working dir for pyfs as /mnt/batch/tasks/shared/LS_root/jobs/workshoplcl/azureml/monexperimentation_1602575538_84ebca82/mounts/workspaceblobstore/azureml/monexperimentation_1602575538_84ebca82\\n2020-10-13 08:01:21,491|azureml.core.authentication|DEBUG|Time to expire 1813861.50883 seconds\\n2020-10-13 08:01:35,076|azureml.history._tracking.PythonWorkingDirectory.workingdir|DEBUG|Calling pyfs\\n2020-10-13 08:01:35,077|azureml.history._tracking.PythonWorkingDirectory|INFO|Current working dir: /mnt/batch/tasks/shared/LS_root/jobs/workshoplcl/azureml/monexperimentation_1602575538_84ebca82/mounts/workspaceblobstore/azureml/monexperimentation_1602575538_84ebca82\\n2020-10-13 08:01:35,077|azureml.history._tracking.PythonWorkingDirectory.workingdir|DEBUG|Reverting working dir from /mnt/batch/tasks/shared/LS_root/jobs/workshoplcl/azureml/monexperimentation_1602575538_84ebca82/mounts/workspaceblobstore/azureml/monexperimentation_1602575538_84ebca82 to /mnt/batch/tasks/shared/LS_root/jobs/workshoplcl/azureml/monexperimentation_1602575538_84ebca82/mounts/workspaceblobstore/azureml/monexperimentation_1602575538_84ebca82\\n2020-10-13 08:01:35,077|azureml.history._tracking.PythonWorkingDirectory|INFO|Working dir is already updated /mnt/batch/tasks/shared/LS_root/jobs/workshoplcl/azureml/monexperimentation_1602575538_84ebca82/mounts/workspaceblobstore/azureml/monexperimentation_1602575538_84ebca82\\n2020-10-13 08:01:35,077|azureml.history._tracking.PythonWorkingDirectory.workingdir|DEBUG|[STOP]\\n2020-10-13 08:01:35,077|azureml.WorkingDirectoryCM|DEBUG|[STOP]\\n2020-10-13 08:01:35,077|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.FlushingMetricsClient|DEBUG|[START]\\n2020-10-13 08:01:35,077|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient|DEBUG|Overrides: Max batch size: 50, batch cushion: 5, Interval: 1.\\n2020-10-13 08:01:35,078|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatch.PostMetricsBatchDaemon|DEBUG|Starting daemon and triggering first instance\\n2020-10-13 08:01:35,078|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient|DEBUG|Used <class 'azureml._common.async_utils.batch_task_queue.BatchTaskQueue'> for use_batch=True.\\n2020-10-13 08:01:35,078|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|[START]\\n2020-10-13 08:01:35,078|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|flush timeout 120 is different from task queue timeout 120, using flush timeout\\n2020-10-13 08:01:35,078|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|Waiting 120 seconds on tasks: [].\\n2020-10-13 08:01:35,079|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|\\n2020-10-13 08:01:35,079|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|[STOP]\\n2020-10-13 08:01:35,079|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient|DEBUG|Overrides: Max batch size: 50, batch cushion: 5, Interval: 1.\\n2020-10-13 08:01:35,079|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatchV2.PostMetricsBatchV2Daemon|DEBUG|Starting daemon and triggering first instance\\n2020-10-13 08:01:35,079|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient|DEBUG|Used <class 'azureml._common.async_utils.batch_task_queue.BatchTaskQueue'> for use_batch=True.\\n2020-10-13 08:01:35,079|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatchV2.WaitFlushSource:MetricsClient|DEBUG|[START]\\n2020-10-13 08:01:35,079|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatchV2.WaitFlushSource:MetricsClient|DEBUG|flush timeout 120 is different from task queue timeout 120, using flush timeout\\n2020-10-13 08:01:35,080|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatchV2.WaitFlushSource:MetricsClient|DEBUG|Waiting 120 seconds on tasks: [].\\n2020-10-13 08:01:35,080|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatchV2|DEBUG|\\n2020-10-13 08:01:35,080|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatchV2.WaitFlushSource:MetricsClient|DEBUG|[STOP]\\n2020-10-13 08:01:35,080|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.FlushingMetricsClient|DEBUG|[STOP]\\n2020-10-13 08:01:35,080|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.wait_on_ingest-async:False|DEBUG|[START]\\n2020-10-13 08:01:35,080|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient|DEBUG|ClientBase: Calling wait_on_ingest with url /history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/runs/{runId}/metricsingest/wait\\n2020-10-13 08:01:35,444|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.wait_on_ingest-async:False|DEBUG|[STOP]\\n2020-10-13 08:01:35,445|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient|DEBUG|[STOP]\\n2020-10-13 08:01:35,445|azureml.MetricsClient|DEBUG|[STOP]\\n2020-10-13 08:01:35,445|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.FlushingMetricsClient|DEBUG|[START]\\n2020-10-13 08:01:35,445|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|[START]\\n2020-10-13 08:01:35,446|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|flush timeout 300 is different from task queue timeout 120, using flush timeout\\n2020-10-13 08:01:35,446|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|Waiting 300 seconds on tasks: [].\\n2020-10-13 08:01:35,446|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|\\n2020-10-13 08:01:35,446|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|[STOP]\\n2020-10-13 08:01:35,446|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatchV2.WaitFlushSource:MetricsClient|DEBUG|[START]\\n2020-10-13 08:01:35,446|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatchV2.WaitFlushSource:MetricsClient|DEBUG|flush timeout 300 is different from task queue timeout 120, using flush timeout\\n2020-10-13 08:01:35,446|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatchV2.WaitFlushSource:MetricsClient|DEBUG|Waiting 300 seconds on tasks: [].\\n2020-10-13 08:01:35,446|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatchV2|DEBUG|\\n2020-10-13 08:01:35,446|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatchV2.WaitFlushSource:MetricsClient|DEBUG|[STOP]\\n2020-10-13 08:01:35,447|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.FlushingMetricsClient|DEBUG|[STOP]\\n2020-10-13 08:01:35,447|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.wait_on_ingest-async:False|DEBUG|[START]\\n2020-10-13 08:01:35,447|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient|DEBUG|ClientBase: Calling wait_on_ingest with url /history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/runs/{runId}/metricsingest/wait\\n2020-10-13 08:01:35,623|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.wait_on_ingest-async:False|DEBUG|[STOP]\\n2020-10-13 08:01:35,623|azureml.RunStatusContext|DEBUG|[STOP]\\n2020-10-13 08:01:35,623|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.FlushingMetricsClient|DEBUG|[START]\\n2020-10-13 08:01:35,624|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|[START]\\n2020-10-13 08:01:35,624|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|flush timeout 300.0 is different from task queue timeout 120, using flush timeout\\n2020-10-13 08:01:35,624|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|Waiting 300.0 seconds on tasks: [].\\n2020-10-13 08:01:35,624|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|\\n2020-10-13 08:01:35,624|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|[STOP]\\n2020-10-13 08:01:35,624|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatchV2.WaitFlushSource:MetricsClient|DEBUG|[START]\\n2020-10-13 08:01:35,624|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatchV2.WaitFlushSource:MetricsClient|DEBUG|flush timeout 300.0 is different from task queue timeout 120, using flush timeout\\n2020-10-13 08:01:35,624|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatchV2.WaitFlushSource:MetricsClient|DEBUG|Waiting 300.0 seconds on tasks: [].\\n2020-10-13 08:01:35,624|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatchV2|DEBUG|\\n2020-10-13 08:01:35,624|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.PostMetricsBatchV2.WaitFlushSource:MetricsClient|DEBUG|[STOP]\\n2020-10-13 08:01:35,625|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.FlushingMetricsClient|DEBUG|[STOP]\\n2020-10-13 08:01:35,625|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.wait_on_ingest-async:False|DEBUG|[START]\\n2020-10-13 08:01:35,625|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient|DEBUG|ClientBase: Calling wait_on_ingest with url /history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/runs/{runId}/metricsingest/wait\\n2020-10-13 08:01:35,736|azureml._SubmittedRun#monexperimentation_1602575538_84ebca82.RunHistoryFacade.MetricsClient.wait_on_ingest-async:False|DEBUG|[STOP]\\n2020-10-13 08:01:35,736|azureml.SendRunKillSignal|DEBUG|[STOP]\\n2020-10-13 08:01:35,736|azureml.HistoryTrackingWorkerPool.WorkerPoolShutdown|DEBUG|[START]\\n2020-10-13 08:01:35,737|azureml.HistoryTrackingWorkerPool.WorkerPoolShutdown|DEBUG|[STOP]\\n2020-10-13 08:01:35,737|azureml.WorkerPool|DEBUG|[STOP]\\n\\nRun is completed.\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.15.0\"}, \"loading\": false}"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RunDetails(run).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pour suivre l'avancement du run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Completed'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run.get_status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'runId': 'monexperimentation_1602575538_84ebca82',\n",
       " 'target': 'monclustercpu',\n",
       " 'status': 'Completed',\n",
       " 'startTimeUtc': '2020-10-13T07:59:17.814717Z',\n",
       " 'endTimeUtc': '2020-10-13T08:02:02.98967Z',\n",
       " 'properties': {'_azureml.ComputeTargetType': 'amlcompute',\n",
       "  'ContentSnapshotId': '0abc68e1-ae04-4ca0-aa19-e91cff0861be',\n",
       "  'ProcessInfoFile': 'azureml-logs/process_info.json',\n",
       "  'ProcessStatusFile': 'azureml-logs/process_status.json'},\n",
       " 'inputDatasets': [],\n",
       " 'outputDatasets': [],\n",
       " 'runDefinition': {'script': 'train.py',\n",
       "  'command': [],\n",
       "  'useAbsolutePath': False,\n",
       "  'arguments': [],\n",
       "  'sourceDirectoryDataStore': None,\n",
       "  'framework': 'Python',\n",
       "  'communicator': 'None',\n",
       "  'target': 'monclustercpu',\n",
       "  'dataReferences': {},\n",
       "  'data': {},\n",
       "  'outputData': {},\n",
       "  'jobName': None,\n",
       "  'maxRunDurationSeconds': 2592000,\n",
       "  'nodeCount': 1,\n",
       "  'priority': None,\n",
       "  'environment': {'name': 'training_env',\n",
       "   'version': 'Autosave_2020-10-13T07:52:21Z_13b660a6',\n",
       "   'python': {'interpreterPath': 'python',\n",
       "    'userManagedDependencies': False,\n",
       "    'condaDependencies': {'channels': ['anaconda', 'conda-forge'],\n",
       "     'dependencies': ['python=3.6.2',\n",
       "      {'pip': ['azureml-defaults~=1.15.0']},\n",
       "      'scikit-learn'],\n",
       "     'name': 'azureml_5f77a57357429044efc38b318c0344eb'},\n",
       "    'baseCondaEnvironment': None},\n",
       "   'environmentVariables': {'EXAMPLE_ENV_VAR': 'EXAMPLE_VALUE'},\n",
       "   'docker': {'baseImage': 'mcr.microsoft.com/azureml/intelmpi2018.3-ubuntu16.04:20200821.v1',\n",
       "    'platform': {'os': 'Linux', 'architecture': 'amd64'},\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",
       "   'enableMLflowTracking': True,\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': None},\n",
       "  'aiSuperComputer': {'instanceType': None,\n",
       "   'frameworkImage': None,\n",
       "   'imageVersion': None,\n",
       "   'location': None},\n",
       "  'tensorflow': {'workerCount': 1, 'parameterServerCount': 1},\n",
       "  'mpi': {'processCountPerNode': 1},\n",
       "  'hdi': {'yarnDeployMode': 'Cluster'},\n",
       "  'containerInstance': {'region': None, 'cpuCores': 2.0, 'memoryGb': 3.5},\n",
       "  'exposedPorts': None,\n",
       "  'docker': {'useDocker': True,\n",
       "   'sharedVolumes': True,\n",
       "   'shmSize': '2g',\n",
       "   'arguments': []},\n",
       "  'cmk8sCompute': {'configuration': {}},\n",
       "  'globalJobDispatcher': {'vmSize': []}},\n",
       " 'logFiles': {'azureml-logs/20_image_build_log.txt': 'https://workshoplcl3468771077.blob.core.windows.net/azureml/ExperimentRun/dcid.monexperimentation_1602575538_84ebca82/azureml-logs/20_image_build_log.txt?sv=2019-02-02&sr=b&sig=saDVzAYNluHjwXac4v15vo05hTOydcKANilTJKfyYZY%3D&st=2020-10-13T07%3A51%3A52Z&se=2020-10-13T16%3A01%3A52Z&sp=r',\n",
       "  'azureml-logs/55_azureml-execution-tvmps_acf50f4fa1807c7df2240e8b769cf8ea6fe893276aaecb7682a3a735ba74c436_d.txt': 'https://workshoplcl3468771077.blob.core.windows.net/azureml/ExperimentRun/dcid.monexperimentation_1602575538_84ebca82/azureml-logs/55_azureml-execution-tvmps_acf50f4fa1807c7df2240e8b769cf8ea6fe893276aaecb7682a3a735ba74c436_d.txt?sv=2019-02-02&sr=b&sig=4579RgFRhu1Z96wpAfh4O5MBxj9kXmJJqy%2Fc40V50Yk%3D&st=2020-10-13T07%3A51%3A52Z&se=2020-10-13T16%3A01%3A52Z&sp=r',\n",
       "  'azureml-logs/65_job_prep-tvmps_acf50f4fa1807c7df2240e8b769cf8ea6fe893276aaecb7682a3a735ba74c436_d.txt': 'https://workshoplcl3468771077.blob.core.windows.net/azureml/ExperimentRun/dcid.monexperimentation_1602575538_84ebca82/azureml-logs/65_job_prep-tvmps_acf50f4fa1807c7df2240e8b769cf8ea6fe893276aaecb7682a3a735ba74c436_d.txt?sv=2019-02-02&sr=b&sig=6PyGDPuiLx3Uiz2401t6Wzw%2BwMV%2BEaQRrXKEaS11OMU%3D&st=2020-10-13T07%3A51%3A52Z&se=2020-10-13T16%3A01%3A52Z&sp=r',\n",
       "  'azureml-logs/70_driver_log.txt': 'https://workshoplcl3468771077.blob.core.windows.net/azureml/ExperimentRun/dcid.monexperimentation_1602575538_84ebca82/azureml-logs/70_driver_log.txt?sv=2019-02-02&sr=b&sig=ezDUkUXOQmGTbKCBMRFrSayqT7nV4BmvHXRZGFY1YwY%3D&st=2020-10-13T07%3A51%3A52Z&se=2020-10-13T16%3A01%3A52Z&sp=r',\n",
       "  'azureml-logs/75_job_post-tvmps_acf50f4fa1807c7df2240e8b769cf8ea6fe893276aaecb7682a3a735ba74c436_d.txt': 'https://workshoplcl3468771077.blob.core.windows.net/azureml/ExperimentRun/dcid.monexperimentation_1602575538_84ebca82/azureml-logs/75_job_post-tvmps_acf50f4fa1807c7df2240e8b769cf8ea6fe893276aaecb7682a3a735ba74c436_d.txt?sv=2019-02-02&sr=b&sig=KT4IzHPI4e02pQKRf%2BfQiHQsko747rMVacwCwATyQu4%3D&st=2020-10-13T07%3A51%3A52Z&se=2020-10-13T16%3A01%3A52Z&sp=r',\n",
       "  'azureml-logs/process_info.json': 'https://workshoplcl3468771077.blob.core.windows.net/azureml/ExperimentRun/dcid.monexperimentation_1602575538_84ebca82/azureml-logs/process_info.json?sv=2019-02-02&sr=b&sig=HQ9k0Ox333d51zg5b0ZfQ3i5hIr41SSZFP%2FescGwCKc%3D&st=2020-10-13T07%3A51%3A52Z&se=2020-10-13T16%3A01%3A52Z&sp=r',\n",
       "  'azureml-logs/process_status.json': 'https://workshoplcl3468771077.blob.core.windows.net/azureml/ExperimentRun/dcid.monexperimentation_1602575538_84ebca82/azureml-logs/process_status.json?sv=2019-02-02&sr=b&sig=YsgI%2FUsptr3mIeNmr3ntpIIcA0TkSpsLOz4U57lNB9o%3D&st=2020-10-13T07%3A51%3A52Z&se=2020-10-13T16%3A01%3A52Z&sp=r',\n",
       "  'logs/azureml/119_azureml.log': 'https://workshoplcl3468771077.blob.core.windows.net/azureml/ExperimentRun/dcid.monexperimentation_1602575538_84ebca82/logs/azureml/119_azureml.log?sv=2019-02-02&sr=b&sig=eJooM1eIbeoX5DFWGGVJgDZsp3OG%2BFCny8ukQ7TnMI8%3D&st=2020-10-13T07%3A51%3A52Z&se=2020-10-13T16%3A01%3A52Z&sp=r',\n",
       "  'logs/azureml/job_prep_azureml.log': 'https://workshoplcl3468771077.blob.core.windows.net/azureml/ExperimentRun/dcid.monexperimentation_1602575538_84ebca82/logs/azureml/job_prep_azureml.log?sv=2019-02-02&sr=b&sig=tlOOr71C365DBrTNc%2FLxqZtM96qj%2BKRUG3Jb0zT8AyM%3D&st=2020-10-13T07%3A51%3A52Z&se=2020-10-13T16%3A01%3A52Z&sp=r',\n",
       "  'logs/azureml/job_release_azureml.log': 'https://workshoplcl3468771077.blob.core.windows.net/azureml/ExperimentRun/dcid.monexperimentation_1602575538_84ebca82/logs/azureml/job_release_azureml.log?sv=2019-02-02&sr=b&sig=hGRLVcEop5faWFZEngqLyBSikiC5XGNcA9mUy1gvrck%3D&st=2020-10-13T07%3A51%3A52Z&se=2020-10-13T16%3A01%3A52Z&sp=r'}}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run.get_details()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'nodeId': 'tvmps_acf50f4fa1807c7df2240e8b769cf8ea6fe893276aaecb7682a3a735ba74c436_d',\n",
       "  'port': 50000,\n",
       "  'publicIpAddress': '20.54.177.218',\n",
       "  'privateIpAddress': '10.0.0.4',\n",
       "  'nodeState': 'idle'}]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statut du compute server\n",
    "compute_target.list_nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'currentNodeCount': 1,\n",
       " 'targetNodeCount': 1,\n",
       " 'nodeStateCounts': {'preparingNodeCount': 0,\n",
       "  'runningNodeCount': 0,\n",
       "  'idleNodeCount': 1,\n",
       "  'unusableNodeCount': 0,\n",
       "  'leavingNodeCount': 0,\n",
       "  'preemptedNodeCount': 0},\n",
       " 'allocationState': 'Steady',\n",
       " 'allocationStateTransitionTime': '2020-10-13T07:50:13.010000+00:00',\n",
       " 'errors': None,\n",
       " 'creationTime': '2020-10-13T07:48:26.754906+00:00',\n",
       " 'modifiedTime': '2020-10-13T07:48:42.685390+00:00',\n",
       " 'provisioningState': 'Succeeded',\n",
       " 'provisioningStateTransitionTime': None,\n",
       " 'scaleSettings': {'minNodeCount': 1,\n",
       "  'maxNodeCount': 2,\n",
       "  'nodeIdleTimeBeforeScaleDown': 'PT1200S'},\n",
       " 'vmPriority': 'Dedicated',\n",
       " 'vmSize': 'STANDARD_D12_V2'}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get_status () gets the latest status of the AmlCompute target\n",
    "compute_target.get_status().serialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Référencement du modèle avec Azure ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK!\n",
      "Version du modèle :  1\n"
     ]
    }
   ],
   "source": [
    "if run.get_status() == 'Completed':\n",
    "    print(\"OK!\")\n",
    "    model_run = run.register_model(model_name=model_name,  \n",
    "                               model_path=\"././outputs/model/chd-rf-model\",\n",
    "                               tags={\"type\": \"classification\", \"framework\": \"scikit\", \"description\": model_description, \"run_id\": run.id})\n",
    "    print(\"Version du modèle : \", model_run.version)\n",
    "else:\n",
    "    print(\"OK\")\n",
    "    Exception(\"Erreur\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Liste des modèles référencés"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "monmodele Version = 1\n",
      "\t type : classification\n",
      "\t framework : scikit\n",
      "\t description : madescription\n",
      "\t run_id : monexperimentation_1602575538_84ebca82\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from azureml.core import Model\n",
    "\n",
    "for model in Model.list(ws):\n",
    "    print(model.name, 'Version =', model.version)\n",
    "    for tag_name in model.tags:\n",
    "        tag = model.tags[tag_name]\n",
    "        print ('\\t',tag_name, ':', tag)\n",
    "    for prop_name in model.properties:\n",
    "        prop = model.properties[prop_name]\n",
    "        print ('\\t',prop_name, ':', prop)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>monexperimentation</td><td>workshoplcl</td><td><a href=\"https://ml.azure.com/experiments/monexperimentation?wsid=/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourcegroups/workshoplcl-rg/workspaces/workshoplcl\" 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: monexperimentation,\n",
       "Workspace: workshoplcl)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Fin"
   ]
  }
 ],
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