hyperparameter_tuning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning using HyperDrive\n",
    "\n",
    "TODO: Import Dependencies. In the cell below, import all the dependencies that you will need to complete the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "gather": {
     "logged": 1598531914256
    }
   },
   "outputs": [],
   "source": [
    "from azureml.core import Workspace, Experiment\n",
    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
    "from azureml.widgets import RunDetails\n",
    "from azureml.train.sklearn import SKLearn\n",
    "from azureml.train.hyperdrive.run import PrimaryMetricGoal\n",
    "from azureml.train.hyperdrive.policy import BanditPolicy\n",
    "from azureml.train.hyperdrive.sampling import RandomParameterSampling\n",
    "from azureml.train.hyperdrive.runconfig import HyperDriveConfig\n",
    "from azureml.train.hyperdrive.parameter_expressions import choice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "TODO: Get data. In the cell below, write code to access the data you will be using in this project. Remember that the dataset needs to be external."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting data: the dataset was uploaded to and is accesible through my github repo\n",
    "from azureml.data.dataset_factory import TabularDatasetFactory\n",
    "\n",
    "rawdata_homic2018 = \"https://raw.githubusercontent.com/hftamayo/azuremlproj03/main/cad2018.csv\"\n",
    "dshomic2018 = TabularDatasetFactory.from_delimited_files(path=rawdata_homic2018, separator=',')\n",
    "hyperpdhomic2018 = dshomic2018.to_pandas_dataframe().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "gather": {
     "logged": 1598531917374
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "expname = 'hyperp3ht'\n",
    "\n",
    "exphyper=Experiment(ws, expname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating\n",
      "Succeeded\n",
      "AmlCompute wait for completion finished\n",
      "\n",
      "Minimum number of nodes requested have been provisioned\n"
     ]
    }
   ],
   "source": [
    "# Compute name has restrictions, please refer to official documentation\n",
    "ccname = \"ccp03hyp\"\n",
    "try:\n",
    "    ccluster = ComputeTarget(ws, ccname)\n",
    "    print(f\"{ccname} exists already\")\n",
    "except:\n",
    "    ccconfig = AmlCompute.provisioning_configuration(vm_size=\"Standard_D2_V2\", max_nodes=5)\n",
    "    ccluster = ComputeTarget.create(ws, ccname, ccconfig)\n",
    "ccluster.wait_for_completion(show_output=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "gather": {
     "logged": 1598531923519
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "source": [
    "## Hyperdrive Configuration\n",
    "\n",
    "TODO: Explain the model you are using and the reason for chosing the different hyperparameters, termination policy and config settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "gather": {
     "logged": 1598544893076
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:azureml.train.sklearn:'SKLearn' estimator is deprecated. Please use 'ScriptRunConfig' from 'azureml.core.script_run_config' with your own defined environment or the AzureML-Tutorial curated environment.\n"
     ]
    }
   ],
   "source": [
    "# TODO: Create an early termination policy. This is not required if you are using Bayesian sampling.\n",
    "early_termination_policy = BanditPolicy(evaluation_interval=1, slack_factor=0.2, delay_evaluation=5)\n",
    "\n",
    "#TODO: Create the different params that you will be using during training\n",
    "#param_sampling = RandomParameterSampling({\"--C\": choice(1,2,3,4,5), \"--max_iter\": choice(80,100,120,150,170,200)})\n",
    "param_sampling = RandomParameterSampling({\"--C\": choice(1,2,3,4), \"--max_iter\": choice(40,80,120,130,200)})\n",
    "\n",
    "if \"outputs\" not in os.listdir():\n",
    "    os.mkdir(\"./outputs\")\n",
    "    \n",
    "#TODO: Create your estimator and hyperdrive config\n",
    "estimator = SKLearn(source_directory='.', compute_target=ccluster, entry_script='train.py')\n",
    "\n",
    "hyperdrive_run_config = HyperDriveConfig(estimator=estimator, policy=early_termination_policy, primary_metric_name=\"Accuracy\",\n",
    "                                          hyperparameter_sampling=param_sampling,\n",
    "                                         max_total_runs=20,\n",
    "                                          primary_metric_goal=PrimaryMetricGoal.MAXIMIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "gather": {
     "logged": 1598544897941
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:If 'script' has been provided here and a script file name has been specified in 'run_config', 'script' provided in ScriptRunConfig initialization will take precedence.\n"
     ]
    }
   ],
   "source": [
    "#TODO: Submit your experiment\n",
    "hyperDrive_run = exphyper.submit(hyperdrive_run_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "gather": {
     "logged": 1598544898497
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "source": [
    "## Run Details\n",
    "\n",
    "OPTIONAL: Write about the different models trained and their performance. Why do you think some models did better than others?\n",
    "\n",
    "TODO: In the cell below, use the `RunDetails` widget to show the different experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "gather": {
     "logged": 1598546648408
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
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    "scrolled": true
   },
   "outputs": [
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     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1763d31b7e5f499d9e12ffb7880afb24",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_HyperDriveWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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target.\\r\\n[2021-01-29T11:35:44.2282818Z][SCHEDULER][INFO]The execution environment is being prepared. Please be patient as it can take a few minutes.\\r\\n[2021-01-29T11:36:12.645365][GENERATOR][INFO]Max number of jobs '20' reached for experiment.\\r\\n[2021-01-29T11:36:12.761079][GENERATOR][INFO]All jobs generated.\\r\\n[2021-01-29T11:36:16.7273247Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_5'\\r\\n[2021-01-29T11:36:16.7184013Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_0'\\r\\n[2021-01-29T11:36:16.7520623Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_19'\\r\\n[2021-01-29T11:36:16.7198689Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_1'\\r\\n[2021-01-29T11:36:16.7347672Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_9'\\r\\n[2021-01-29T11:36:16.7419655Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_13'\\r\\n[2021-01-29T11:36:16.7234416Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_3'\\r\\n[2021-01-29T11:36:16.7320275Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_7'\\r\\n[2021-01-29T11:36:16.7333117Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_8'\\r\\n[2021-01-29T11:36:16.7373088Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_11'\\r\\n[2021-01-29T11:36:16.7299235Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_6'\\r\\n[2021-01-29T11:36:16.7259265Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_4'\\r\\n[2021-01-29T11:36:16.7506527Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_18'\\r\\n[2021-01-29T11:36:16.7521938Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_17'\\r\\n[2021-01-29T11:36:16.7495211Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_14'\\r\\n[2021-01-29T11:36:16.7504362Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_16'\\r\\n[2021-01-29T11:36:16.7499303Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_15'\\r\\n[2021-01-29T11:36:16.7210753Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_2'\\r\\n[2021-01-29T11:36:16.7360898Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_10'\\r\\n[2021-01-29T11:36:16.7395810Z][SCHEDULER][INFO]Scheduling job, id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_12'\\r\\n[2021-01-29T11:36:16.7176421Z][SCHEDULER][INFO]The execution environment was successfully prepared.\\r\\n[2021-01-29T11:36:17.2869606Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_11'\\r\\n[2021-01-29T11:36:17.3631137Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_10'\\r\\n[2021-01-29T11:36:17.4012652Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_13'\\r\\n[2021-01-29T11:36:17.4279638Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_18'\\r\\n[2021-01-29T11:36:17.5244461Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_8'\\r\\n[2021-01-29T11:36:17.6148066Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_6'\\r\\n[2021-01-29T11:36:17.7102174Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_17'\\r\\n[2021-01-29T11:36:17.7115404Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_2'\\r\\n[2021-01-29T11:36:17.7505197Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_14'\\r\\n[2021-01-29T11:36:17.8206884Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_7'\\r\\n[2021-01-29T11:36:17.8700378Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_16'\\r\\n[2021-01-29T11:36:17.9875108Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_19'\\r\\n[2021-01-29T11:36:18.0398209Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_3'\\r\\n[2021-01-29T11:36:18.1130321Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_6a6c0070-9185-420b-af26-284a46fb5ae7_5'\\r\\n[2021-01-29T11:36:18.1137838Z][SCHEDULER][INFO]Successfully scheduled a job. 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RunDetails(hyperDrive_run).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best Model\n",
    "\n",
    "TODO: In the cell below, get the best model from the hyperdrive experiments and display all the properties of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "gather": {
     "logged": 1598546650307
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Run Id:  HD_6a6c0070-9185-420b-af26-284a46fb5ae7_6\n",
      "\n",
      " Accuracy: 0.8914342629482072\n",
      "\n",
      " Regularization Strength: 1.0\n",
      "\n",
      " Max Iterations: 120\n"
     ]
    }
   ],
   "source": [
    "brmodel = hyperDrive_run.get_best_run_by_primary_metric()\n",
    "brmetrics = brmodel.get_metrics()\n",
    "\n",
    "print('Best Run Id: ', brmodel.id)\n",
    "print('\\n Accuracy:', brmetrics['Accuracy'])\n",
    "print('\\n Regularization Strength:',brmetrics['Regularization Strength:'])\n",
    "print('\\n Max Iterations:',brmetrics['Max iterations:'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['azureml-logs/55_azureml-execution-tvmps_c6b08b7f163ecec0ba509382f1ba598cf4f7cfef64ad6d43f1da46e62e320312_d.txt',\n",
       " 'azureml-logs/65_job_prep-tvmps_c6b08b7f163ecec0ba509382f1ba598cf4f7cfef64ad6d43f1da46e62e320312_d.txt',\n",
       " 'azureml-logs/70_driver_log.txt',\n",
       " 'azureml-logs/75_job_post-tvmps_c6b08b7f163ecec0ba509382f1ba598cf4f7cfef64ad6d43f1da46e62e320312_d.txt',\n",
       " 'azureml-logs/process_info.json',\n",
       " 'azureml-logs/process_status.json',\n",
       " 'logs/azureml/105_azureml.log',\n",
       " 'logs/azureml/job_prep_azureml.log',\n",
       " 'logs/azureml/job_release_azureml.log',\n",
       " 'outputs/bhypermodel.pkl']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this lines is really useful in case you want to check files related to the model\n",
    "brmodel.get_file_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "gather": {
     "logged": 1598546657829
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model(workspace=Workspace.create(name='quick-starts-ws-136496', subscription_id='1b944a9b-fdae-4f97-aeb1-b7eea0beac53', resource_group='aml-quickstarts-136496'), name=bhypermodel, id=bhypermodel:2, version=2, tags={}, properties={})\n"
     ]
    }
   ],
   "source": [
    "#TODO: Save the best model: the files is created and uploaded during the execution of train.py\n",
    "model = brmodel.register_model(model_name = 'bhypermodel', model_path = 'outputs/bhypermodel.pkl') \n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "source": [
    "## Model Deployment\n",
    "\n",
    "Remember you have to deploy only one of the two models you trained.. Perform the steps in the rest of this notebook only if you wish to deploy this model.\n",
    "\n",
    "TODO: In the cell below, register the model, create an inference config and deploy the model as a web service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from azureml.core.environment import Environment\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "env=Environment.from_conda_specification(name=\"hyperenv\",\n",
    "                                                  file_path=\"hyperenv.yml\")\n",
    "\n",
    "#myenv=Environment.from_conda_specification(name=\"myenv\",\n",
    "#                                                  file_path=\"./conda_dephyper.yml\")\n",
    "#env = Environment.get(workspace=ws, name=\"AzureML-AutoML\")\n",
    "#OPCIONAL: env = Environment.get(workspace, \"AzureML-Minimal\").clone(env_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tips: You can try get_logs(): https://aka.ms/debugimage#dockerlog or local deployment: https://aka.ms/debugimage#debug-locally to debug if deployment takes longer than 10 minutes.\n",
      "Running.............................................................................................................................................................................................\n",
      "Succeeded\n",
      "ACI service creation operation finished, operation \"Succeeded\"\n",
      "Healthy\n",
      "http://4115e61b-0d9d-4629-8598-c56894967dbd.southcentralus.azurecontainer.io/score\n",
      "http://4115e61b-0d9d-4629-8598-c56894967dbd.southcentralus.azurecontainer.io/swagger.json\n"
     ]
    }
   ],
   "source": [
    "from azureml.core.environment import Environment\n",
    "from azureml.core.model import InferenceConfig, Model\n",
    "\n",
    "infcfg = InferenceConfig(entry_script='score.py',\n",
    "                                    environment=env)\n",
    "\n",
    "# deploying the model via WebService\n",
    "from azureml.core.webservice import AciWebservice\n",
    "\n",
    "depcfg = AciWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1)\n",
    "webservice = Model.deploy(ws, \"wshypp03ht01\", [model], infcfg, depcfg)\n",
    "webservice.wait_for_deployment(show_output = True)\n",
    "print(webservice.state)\n",
    "\n",
    "print(webservice.scoring_uri)\n",
    "\n",
    "print(webservice.swagger_uri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2125, 21917, 42, 2, 1, 1]]\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "#Use a test case in particular from the dataset\n",
    "#data = {\"data\":\n",
    "#        [\n",
    "#          {\n",
    "#           \"id\": 2125, \"regfalle\": 21917, \"edad\": 42, \"deptoocuhe\": 2, \"tipoarma\": 1, \"pracaut\": 1\n",
    "#          },\n",
    "#      ]\n",
    "#    }\n",
    "data = [[2125, 21917, 42, 2, 1, 1]]\n",
    "         \n",
    "# Convert to JSON string\n",
    "tstdatahomic2018 = json.dumps(data)\n",
    "\n",
    "print(tstdatahomic2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: In the cell below, send a request to the web service you deployed to test it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "headers = {'Content-type': 'application/json'}\n",
    "\n",
    "response = requests.post(webservice.scoring_uri, tstdatahomic2018, headers=headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n",
      "[0]\n"
     ]
    }
   ],
   "source": [
    "# Print results\n",
    "print(response.status_code)\n",
    "print(response.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: In the cell below, print the logs of the web service and delete the service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-01-29T12:09:16,601308249+00:00 - rsyslog/run \n",
      "2021-01-29T12:09:16,601308349+00:00 - iot-server/run \n",
      "2021-01-29T12:09:16,603478376+00:00 - gunicorn/run \n",
      "2021-01-29T12:09:16,605247297+00:00 - nginx/run \n",
      "rsyslogd: /azureml-envs/azureml_8eff28b157f42edcd2424a5aae6c8074/lib/libuuid.so.1: no version information available (required by rsyslogd)\n",
      "/usr/sbin/nginx: /azureml-envs/azureml_8eff28b157f42edcd2424a5aae6c8074/lib/libcrypto.so.1.0.0: no version information available (required by /usr/sbin/nginx)\n",
      "/usr/sbin/nginx: /azureml-envs/azureml_8eff28b157f42edcd2424a5aae6c8074/lib/libcrypto.so.1.0.0: no version information available (required by /usr/sbin/nginx)\n",
      "/usr/sbin/nginx: /azureml-envs/azureml_8eff28b157f42edcd2424a5aae6c8074/lib/libssl.so.1.0.0: no version information available (required by /usr/sbin/nginx)\n",
      "/usr/sbin/nginx: /azureml-envs/azureml_8eff28b157f42edcd2424a5aae6c8074/lib/libssl.so.1.0.0: no version information available (required by /usr/sbin/nginx)\n",
      "/usr/sbin/nginx: /azureml-envs/azureml_8eff28b157f42edcd2424a5aae6c8074/lib/libssl.so.1.0.0: no version information available (required by /usr/sbin/nginx)\n",
      "EdgeHubConnectionString and IOTEDGE_IOTHUBHOSTNAME are not set. Exiting...\n",
      "2021-01-29T12:09:16,736531309+00:00 - iot-server/finish 1 0\n",
      "2021-01-29T12:09:16,737853925+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 (13)\n",
      "Using worker: sync\n",
      "worker timeout is set to 300\n",
      "Booting worker with pid: 43\n",
      "SPARK_HOME not set. Skipping PySpark Initialization.\n",
      "Generating new fontManager, this may take some time...\n",
      "Initializing logger\n",
      "2021-01-29 12:09:20,288 | root | INFO | Starting up app insights client\n",
      "2021-01-29 12:09:20,288 | root | INFO | Starting up request id generator\n",
      "2021-01-29 12:09:20,288 | root | INFO | Starting up app insight hooks\n",
      "2021-01-29 12:09:20,288 | root | INFO | Invoking user's init function\n",
      "2021-01-29 12:09:20,321 | root | INFO | Users's init has completed successfully\n",
      "/azureml-envs/azureml_8eff28b157f42edcd2424a5aae6c8074/lib/python3.6/site-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/azureml-envs/azureml_8eff28b157f42edcd2424a5aae6c8074/lib/python3.6/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.linear_model.logistic module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.linear_model. Anything that cannot be imported from sklearn.linear_model is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n",
      "/azureml-envs/azureml_8eff28b157f42edcd2424a5aae6c8074/lib/python3.6/site-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator LogisticRegression from version 0.20.3 when using version 0.22.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  UserWarning)\n",
      "2021-01-29 12:09:20,324 | root | INFO | Skipping middleware: dbg_model_info as it's not enabled.\n",
      "2021-01-29 12:09:20,324 | root | INFO | Skipping middleware: dbg_resource_usage as it's not enabled.\n",
      "2021-01-29 12:09:20,325 | root | INFO | Scoring timeout is found from os.environ: 60000 ms\n",
      "2021-01-29 12:09:25,402 | root | INFO | Swagger file not present\n",
      "2021-01-29 12:09:25,402 | root | INFO | 404\n",
      "127.0.0.1 - - [29/Jan/2021:12:09:25 +0000] \"GET /swagger.json HTTP/1.0\" 404 19 \"-\" \"Go-http-client/1.1\"\n",
      "2021-01-29 12:09:31,090 | root | INFO | Swagger file not present\n",
      "2021-01-29 12:09:31,090 | root | INFO | 404\n",
      "127.0.0.1 - - [29/Jan/2021:12:09:31 +0000] \"GET /swagger.json HTTP/1.0\" 404 19 \"-\" \"Go-http-client/1.1\"\n",
      "2021-01-29 12:12:32,068 | root | INFO | Validation Request Content-Type\n",
      "2021-01-29 12:12:32,069 | root | INFO | Scoring Timer is set to 60.0 seconds\n",
      "2021-01-29 12:12:32,070 | root | INFO | 200\n",
      "127.0.0.1 - - [29/Jan/2021:12:12:32 +0000] \"POST /score HTTP/1.0\" 200 3 \"-\" \"python-requests/2.25.1\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(webservice.get_logs())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#BEST PRACTICES: deleting webservice and CCluster that won't be used anymore\n",
    "webservice.delete()\n",
    "ccluster.delete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "python3-azureml"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "nteract": {
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