hyperparameter_tuning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Hyperparameter Tuning using HyperDrive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "source": [
        "### Create workspace and experiment instances"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "gather": {
          "logged": 1664177503372
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "outputs": [],
      "source": [
        "from azureml.core import Workspace, Experiment\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "experiment_name = 'capstone-spam-classification-experiment'\n",
        "\n",
        "experiment=Experiment(ws, experiment_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "source": [
        "### Connect to compute cluster target"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "gather": {
          "logged": 1664177503823
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found existing cluster, use it.\n",
            "Succeeded\n",
            "AmlCompute wait for completion finished\n",
            "\n",
            "Minimum number of nodes requested have been provisioned\n"
          ]
        }
      ],
      "source": [
        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "# TODO: Create compute cluster\n",
        "cluster_name = \"capstone-compute-cluster\"\n",
        "\n",
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
        "    print('Found existing cluster, use it.')\n",
        "except ComputeTargetException:\n",
        "    # To use a different region for the compute, add a location='<region>' parameter\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2', max_nodes=4)\n",
        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
        "\n",
        "compute_target.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Get Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "gather": {
          "logged": 1664177531102
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "outputs": [],
      "source": [
        "from azureml.core import Dataset\n",
        "training_dataset = Dataset.get_by_name(ws, name='capstone-spam-dataset')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "source": [
        "### View the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "gather": {
          "logged": 1664177534442
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "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>Category</th>\n",
              "      <th>Message</th>\n",
              "      <th>Column3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5569</th>\n",
              "      <td>spam</td>\n",
              "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5570</th>\n",
              "      <td>ham</td>\n",
              "      <td>Will ü b going to esplanade fr home?</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5571</th>\n",
              "      <td>ham</td>\n",
              "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5572</th>\n",
              "      <td>ham</td>\n",
              "      <td>The guy did some bitching but I acted like i'd...</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5573</th>\n",
              "      <td>ham</td>\n",
              "      <td>Rofl. Its true to its name</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5574 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Category                                            Message Column3\n",
              "0         ham  Go until jurong point, crazy.. Available only ...    None\n",
              "1         ham                      Ok lar... Joking wif u oni...    None\n",
              "2        spam  Free entry in 2 a wkly comp to win FA Cup fina...    None\n",
              "3         ham  U dun say so early hor... U c already then say...    None\n",
              "4         ham  Nah I don't think he goes to usf, he lives aro...    None\n",
              "...       ...                                                ...     ...\n",
              "5569     spam  This is the 2nd time we have tried 2 contact u...    None\n",
              "5570      ham               Will ü b going to esplanade fr home?    None\n",
              "5571      ham  Pity, * was in mood for that. So...any other s...    None\n",
              "5572      ham  The guy did some bitching but I acted like i'd...    None\n",
              "5573      ham                         Rofl. Its true to its name    None\n",
              "\n",
              "[5574 rows x 3 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "training_dataset.to_pandas_dataframe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "gather": {
          "logged": 1598531923519
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "source": [
        "### Hyperdrive Configuration\n",
        "We are using a Logistic Regression classifier, the main hyperparameters there are C and max_iter. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "gather": {
          "logged": 1664177544993
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
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        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "outputs": [],
      "source": [
        "# TODO: Create an early termination policy. This is not required if you are using Bayesian sampling.\n",
        "from azureml.train.hyperdrive.policy import BanditPolicy\n",
        "early_termination_policy = BanditPolicy(slack_factor = 0.1, evaluation_interval=1, delay_evaluation=5)\n",
        "\n",
        "#TODO: Create the different params that you will be using during training\n",
        "from azureml.train.hyperdrive.parameter_expressions import choice\n",
        "from azureml.train.hyperdrive.sampling import RandomParameterSampling\n",
        "param_sampling = RandomParameterSampling( \n",
        "    {\n",
        "        \"C\": choice(0.01, 0.001, 0.1, 1),\n",
        "        \"max_iter\": choice(50,100),\n",
        "    }\n",
        ")\n",
        "\n",
        "# Setup environment for your training run\n",
        "from azureml.core import Environment\n",
        "sklearn_env = Environment.from_conda_specification(name='sklearn-env', file_path='conda_dependencies.yml')\n",
        "\n",
        "\n",
        "# Create a ScriptRunConfig Object to specify the configuration details of your training job\n",
        "from azureml.core import ScriptRunConfig\n",
        "script_run_config = ScriptRunConfig(source_directory=\".\",\n",
        "                      script='train.py',\n",
        "                      compute_target=compute_target,\n",
        "                      environment=sklearn_env)\n",
        "\n",
        "# Create Hyperdrive config\n",
        "from azureml.train.hyperdrive.runconfig import HyperDriveConfig\n",
        "from azureml.train.hyperdrive.run import PrimaryMetricGoal\n",
        "hyperdrive_run_config = HyperDriveConfig(run_config=script_run_config,\n",
        "                             hyperparameter_sampling=param_sampling,\n",
        "                             policy=early_termination_policy,\n",
        "                             primary_metric_name='Accuracy',\n",
        "                             primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n",
        "                             max_total_runs=10,\n",
        "                             max_concurrent_runs=4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "source": [
        "### Submit your hyperdrive run to the experiment \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "gather": {
          "logged": 1664177572874
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "outputs": [],
      "source": [
        "hyperdrive_run = experiment.submit(hyperdrive_run_config)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "gather": {
          "logged": 1598544898497
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        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "source": [
        "### Run Details"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "gather": {
          "logged": 1664181603941
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "outputs": [
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            "text/plain": [
              "_HyperDriveWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO'…"
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          },
          "metadata": {},
          "output_type": "display_data"
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0.001, \\\"max_iter\\\": 100}\", \"_aml_system_HD_353966e4-eddf-47b6-b19d-22d440935562_1\": \"{\\\"C\\\": 0.01, \\\"max_iter\\\": 50}\", \"_aml_system_HD_353966e4-eddf-47b6-b19d-22d440935562_2\": \"{\\\"C\\\": 0.01, \\\"max_iter\\\": 100}\", \"_aml_system_HD_353966e4-eddf-47b6-b19d-22d440935562_3\": \"{\\\"C\\\": 0.1, \\\"max_iter\\\": 50}\", \"_aml_system_HD_353966e4-eddf-47b6-b19d-22d440935562_4\": \"{\\\"C\\\": 1, \\\"max_iter\\\": 50}\", \"_aml_system_HD_353966e4-eddf-47b6-b19d-22d440935562_5\": \"{\\\"C\\\": 1, \\\"max_iter\\\": 100}\", \"_aml_system_HD_353966e4-eddf-47b6-b19d-22d440935562_6\": \"{\\\"C\\\": 0.1, \\\"max_iter\\\": 100}\", \"_aml_system_HD_353966e4-eddf-47b6-b19d-22d440935562_7\": \"{\\\"C\\\": 0.001, \\\"max_iter\\\": 50}\", \"_aml_system_final_best_metric_update_retry_count\": \"1\"}, \"script_name\": null, \"arguments\": null, \"end_time_utc\": \"2022-09-26T07:39:30.182358Z\", \"status\": \"Completed\", \"log_files\": {\"azureml-logs/hyperdrive.txt\": 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\"HD_353966e4-eddf-47b6-b19d-22d440935562_5\", \"run_number\": 1664177875, \"metric\": 0.96915352, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2022-09-26T07:38:01.198492Z\", \"end_time\": \"2022-09-26T07:38:23.086244Z\", \"created_time\": \"2022-09-26T07:37:55.947179Z\", \"created_time_dt\": \"2022-09-26T07:37:55.947179Z\", \"duration\": \"0:00:27\", \"hyperdrive_id\": \"353966e4-eddf-47b6-b19d-22d440935562\", \"arguments\": null, \"param_C\": 1, \"param_max_iter\": 100, \"best_metric\": 0.96915352}, {\"run_id\": \"HD_353966e4-eddf-47b6-b19d-22d440935562_6\", \"run_number\": 1664177876, \"metric\": 0.89096126, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2022-09-26T07:38:01.198018Z\", \"end_time\": \"2022-09-26T07:38:22.713672Z\", \"created_time\": \"2022-09-26T07:37:56.073614Z\", \"created_time_dt\": \"2022-09-26T07:37:56.073614Z\", \"duration\": \"0:00:26\", \"hyperdrive_id\": \"353966e4-eddf-47b6-b19d-22d440935562\", \"arguments\": null, \"param_C\": 0.1, \"param_max_iter\": 100, \"best_metric\": 0.96915352}], \"children_metrics\": {\"categories\": [0], \"series\": {\"Regularization Strength:\": [{\"categories\": [1664177575, 1664177576, 1664177875, 1664177876], \"mode\": \"markers\", \"name\": \"Regularization Strength:\", \"stepped\": false, \"type\": \"scatter\", \"data\": [0.01, 0.01, 1.0, 0.1]}, {\"categories\": [1664177575, 1664177576, 1664177875, 1664177876], \"mode\": \"lines\", \"name\": \"Regularization Strength:_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [0.01, 0.01, 1.0, 1.0]}], \"Max iterations:\": [{\"categories\": [1664177575, 1664177576, 1664177875, 1664177876], \"mode\": \"markers\", \"name\": \"Max iterations:\", \"stepped\": false, \"type\": \"scatter\", \"data\": [50, 100, 100, 100]}, {\"categories\": [1664177575, 1664177576, 1664177875, 1664177876], \"mode\": \"lines\", \"name\": \"Max iterations:_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [50, 100, 100, 100]}], \"Accuracy\": [{\"categories\": [1664177575, 1664177576, 1664177875, 1664177876], \"mode\": \"markers\", \"name\": \"Accuracy\", \"stepped\": false, \"type\": \"scatter\", \"data\": [0.8622668579626973, 0.8622668579626973, 0.9691535150645624, 0.890961262553802]}, {\"categories\": [1664177575, 1664177576, 1664177875, 1664177876], \"mode\": \"lines\", \"name\": \"Accuracy_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [0.8622668579626973, 0.8622668579626973, 0.9691535150645624, 0.9691535150645624]}]}, \"metricName\": null, \"primaryMetricName\": \"Accuracy\", \"showLegend\": false}, \"run_metrics\": [{\"name\": \"best_child_by_primary_metric\", \"run_id\": \"HD_353966e4-eddf-47b6-b19d-22d440935562\", \"categories\": [0], \"series\": [{\"data\": [{\"time_elapse\": [304, 365, 365], \"metric_value\": [0.890961262553802, 0.9691535150645624, 0.9691535150645624], \"metric_name\": [\"Accuracy\", \"Accuracy\", \"Accuracy\"], \"run_id\": [\"HD_353966e4-eddf-47b6-b19d-22d440935562_3\", \"HD_353966e4-eddf-47b6-b19d-22d440935562_4\", \"HD_353966e4-eddf-47b6-b19d-22d440935562_4\"], \"final\": [false, false, true]}]}]}], \"run_logs\": \"[2022-09-26T07:32:53.107348][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\n[2022-09-26T07:32:55.4216549Z][SCHEDULER][INFO]Scheduling job, id='HD_353966e4-eddf-47b6-b19d-22d440935562_0' \\n[2022-09-26T07:32:55.5761068Z][SCHEDULER][INFO]Scheduling job, id='HD_353966e4-eddf-47b6-b19d-22d440935562_1' \\n[2022-09-26T07:32:55.7844813Z][SCHEDULER][INFO]Scheduling job, id='HD_353966e4-eddf-47b6-b19d-22d440935562_2' \\n[2022-09-26T07:32:55.8051835Z][SCHEDULER][INFO]Scheduling job, id='HD_353966e4-eddf-47b6-b19d-22d440935562_3' \\n[2022-09-26T07:32:55.767354][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\n[2022-09-26T07:32:55.9606091Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_353966e4-eddf-47b6-b19d-22d440935562_1' \\n[2022-09-26T07:32:56.0443603Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_353966e4-eddf-47b6-b19d-22d440935562_0' \\n[2022-09-26T07:32:56.0631122Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_353966e4-eddf-47b6-b19d-22d440935562_3' \\n[2022-09-26T07:32:56.1368478Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_353966e4-eddf-47b6-b19d-22d440935562_2' \\n[2022-09-26T07:37:55.267308][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\n[2022-09-26T07:37:55.6776126Z][SCHEDULER][INFO]Scheduling job, id='HD_353966e4-eddf-47b6-b19d-22d440935562_4' \\n[2022-09-26T07:37:55.7516173Z][SCHEDULER][INFO]Scheduling job, id='HD_353966e4-eddf-47b6-b19d-22d440935562_5' \\n[2022-09-26T07:37:55.8813951Z][SCHEDULER][INFO]Scheduling job, id='HD_353966e4-eddf-47b6-b19d-22d440935562_6' \\n[2022-09-26T07:37:55.9767950Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_353966e4-eddf-47b6-b19d-22d440935562_4' \\n[2022-09-26T07:37:55.951249][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\n[2022-09-26T07:37:55.9781925Z][SCHEDULER][INFO]Scheduling job, id='HD_353966e4-eddf-47b6-b19d-22d440935562_7' \\n[2022-09-26T07:37:56.0311742Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_353966e4-eddf-47b6-b19d-22d440935562_5' \\n[2022-09-26T07:37:56.2393831Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_353966e4-eddf-47b6-b19d-22d440935562_7' \\n[2022-09-26T07:37:56.2521182Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_353966e4-eddf-47b6-b19d-22d440935562_6' \\n[2022-09-26T07:39:30.369992][CONTROLLER][INFO]Experiment was 'ExperimentStatus.RUNNING', is 'ExperimentStatus.FINISHED'.\\n\\nRun is completed.\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.44.0\"}, \"loading\": false}"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RunId: HD_353966e4-eddf-47b6-b19d-22d440935562\n",
            "Web View: https://ml.azure.com/runs/HD_353966e4-eddf-47b6-b19d-22d440935562?wsid=/subscriptions/d4ad7261-832d-46b2-b093-22156001df5b/resourcegroups/aml-quickstarts-208897/workspaces/quick-starts-ws-208897&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\n",
            "\n",
            "Execution Summary\n",
            "=================\n",
            "RunId: HD_353966e4-eddf-47b6-b19d-22d440935562\n",
            "Web View: https://ml.azure.com/runs/HD_353966e4-eddf-47b6-b19d-22d440935562?wsid=/subscriptions/d4ad7261-832d-46b2-b093-22156001df5b/resourcegroups/aml-quickstarts-208897/workspaces/quick-starts-ws-208897&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'runId': 'HD_353966e4-eddf-47b6-b19d-22d440935562',\n",
              " 'target': 'capstone-compute-cluster',\n",
              " 'status': 'Completed',\n",
              " 'startTimeUtc': '2022-09-26T07:32:52.37356Z',\n",
              " 'endTimeUtc': '2022-09-26T07:39:30.182358Z',\n",
              " 'services': {},\n",
              " 'properties': {'primary_metric_config': '{\"name\":\"Accuracy\",\"goal\":\"maximize\"}',\n",
              "  'resume_from': 'null',\n",
              "  'runTemplate': 'HyperDrive',\n",
              "  'azureml.runsource': 'hyperdrive',\n",
              "  'platform': 'AML',\n",
              "  'ContentSnapshotId': '61d9f2d5-297e-4a25-9c50-7e73d12aeec2',\n",
              "  'user_agent': 'python/3.8.5 (Linux-5.15.0-1017-azure-x86_64-with-glibc2.10) msrest/0.7.1 Hyperdrive.Service/1.0.0 Hyperdrive.SDK/core.1.44.0',\n",
              "  'space_size': '8',\n",
              "  'score': '0.9691535150645624',\n",
              "  'best_child_run_id': 'HD_353966e4-eddf-47b6-b19d-22d440935562_4',\n",
              "  'best_metric_status': 'Succeeded',\n",
              "  'best_data_container_id': 'dcid.HD_353966e4-eddf-47b6-b19d-22d440935562_4'},\n",
              " 'inputDatasets': [],\n",
              " 'outputDatasets': [],\n",
              " 'runDefinition': {'configuration': None,\n",
              "  'attribution': None,\n",
              "  'telemetryValues': {'amlClientType': 'azureml-sdk-train',\n",
              "   'amlClientModule': '[Scrubbed]',\n",
              "   'amlClientFunction': '[Scrubbed]',\n",
              "   'tenantId': '660b3398-b80e-49d2-bc5b-ac1dc93b5254',\n",
              "   'amlClientRequestId': '837247bf-e2fc-44ab-9046-70a3bb315871',\n",
              "   'amlClientSessionId': '27f186f0-09d2-4cc2-9d64-8542ad313e13',\n",
              "   'subscriptionId': 'd4ad7261-832d-46b2-b093-22156001df5b',\n",
              "   'estimator': 'NoneType',\n",
              "   'samplingMethod': 'RANDOM',\n",
              "   'terminationPolicy': 'Bandit',\n",
              "   'primaryMetricGoal': 'maximize',\n",
              "   'maxTotalRuns': 10,\n",
              "   'maxConcurrentRuns': 4,\n",
              "   'maxDurationMinutes': 10080,\n",
              "   'vmSize': None},\n",
              "  'snapshotId': '61d9f2d5-297e-4a25-9c50-7e73d12aeec2',\n",
              "  'snapshots': [],\n",
              "  'sourceCodeDataReference': None,\n",
              "  'parentRunId': None,\n",
              "  'dataContainerId': None,\n",
              "  'runType': None,\n",
              "  'displayName': None,\n",
              "  'environmentAssetId': None,\n",
              "  'properties': {},\n",
              "  'tags': {},\n",
              "  'aggregatedArtifactPath': None},\n",
              " 'logFiles': {'azureml-logs/hyperdrive.txt': 'https://mlstrg208897.blob.core.windows.net/azureml/ExperimentRun/dcid.HD_353966e4-eddf-47b6-b19d-22d440935562/azureml-logs/hyperdrive.txt?sv=2019-07-07&sr=b&sig=aNyX1Q%2FbHpmaH7%2BzNoEIUqPqJ%2BydEs%2BVcIWEYlgR3AM%3D&skoid=07784e2f-1e52-4cf3-833a-141e12b1c91f&sktid=660b3398-b80e-49d2-bc5b-ac1dc93b5254&skt=2022-09-26T06%3A26%3A30Z&ske=2022-09-27T14%3A36%3A30Z&sks=b&skv=2019-07-07&st=2022-09-26T08%3A28%3A40Z&se=2022-09-26T16%3A38%3A40Z&sp=r'},\n",
              " 'submittedBy': 'ODL_User 208897'}"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "ename": "KeyError",
          "evalue": "'log_files'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "File \u001b[0;32m/anaconda/envs/azureml_py38/lib/python3.8/site-packages/ipywidgets/widgets/widget.py:756\u001b[0m, in \u001b[0;36mWidget._handle_msg\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m    754\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffer_paths\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[1;32m    755\u001b[0m             _put_buffers(state, data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffer_paths\u001b[39m\u001b[38;5;124m'\u001b[39m], msg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffers\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m--> 756\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    758\u001b[0m \u001b[38;5;66;03m# Handle a state request.\u001b[39;00m\n\u001b[1;32m    759\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrequest_state\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
            "File \u001b[0;32m/anaconda/envs/azureml_py38/lib/python3.8/site-packages/ipywidgets/widgets/widget.py:625\u001b[0m, in \u001b[0;36mWidget.set_state\u001b[0;34m(self, sync_data)\u001b[0m\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys:\n\u001b[1;32m    623\u001b[0m     from_json \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrait_metadata(name, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfrom_json\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m    624\u001b[0m                                     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_trait_from_json)\n\u001b[0;32m--> 625\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_trait(name, from_json(sync_data[name], \u001b[38;5;28mself\u001b[39m))\n",
            "File \u001b[0;32m/anaconda/envs/azureml_py38/lib/python3.8/contextlib.py:120\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    119\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 120\u001b[0m         \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m    122\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
            "File \u001b[0;32m/anaconda/envs/azureml_py38/lib/python3.8/site-packages/traitlets/traitlets.py:1371\u001b[0m, in \u001b[0;36mHasTraits.hold_trait_notifications\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1369\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m changes \u001b[38;5;129;01min\u001b[39;00m cache\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[1;32m   1370\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m change \u001b[38;5;129;01min\u001b[39;00m changes:\n\u001b[0;32m-> 1371\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/anaconda/envs/azureml_py38/lib/python3.8/site-packages/ipywidgets/widgets/widget.py:686\u001b[0m, in \u001b[0;36mWidget.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m    683\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_send_property(name, \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)):\n\u001b[1;32m    684\u001b[0m         \u001b[38;5;66;03m# Send new state to front-end\u001b[39;00m\n\u001b[1;32m    685\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend_state(key\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m--> 686\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mWidget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/anaconda/envs/azureml_py38/lib/python3.8/site-packages/traitlets/traitlets.py:1386\u001b[0m, in \u001b[0;36mHasTraits.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m   1384\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnotify_change\u001b[39m(\u001b[38;5;28mself\u001b[39m, change):\n\u001b[1;32m   1385\u001b[0m     \u001b[38;5;124;03m\"\"\"Notify observers of a change event\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1386\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_notify_observers\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/anaconda/envs/azureml_py38/lib/python3.8/site-packages/traitlets/traitlets.py:1431\u001b[0m, in \u001b[0;36mHasTraits._notify_observers\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m   1428\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, EventHandler) \u001b[38;5;129;01mand\u001b[39;00m c\u001b[38;5;241m.\u001b[39mname \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1429\u001b[0m     c \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, c\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m-> 1431\u001b[0m \u001b[43mc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/anaconda/envs/azureml_py38/lib/python3.8/site-packages/azureml/widgets/_userrun/_run_details.py:627\u001b[0m, in \u001b[0;36m_UserRunDetails._on_selected_run_log_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m    625\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_on_selected_run_log_change\u001b[39m(\u001b[38;5;28mself\u001b[39m, change):\n\u001b[1;32m    626\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mselected_run_log \u001b[38;5;241m=\u001b[39m change\u001b[38;5;241m.\u001b[39mnew\n\u001b[0;32m--> 627\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_run_logs_async(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwidget_instance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_properties\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlog_files\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m,\n\u001b[1;32m    628\u001b[0m                              \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwidget_instance\u001b[38;5;241m.\u001b[39mrun_properties[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstatus\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m    629\u001b[0m                              \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39merror, change\u001b[38;5;241m.\u001b[39mnew)\n",
            "\u001b[0;31mKeyError\u001b[0m: 'log_files'"
          ]
        }
      ],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(hyperdrive_run).show() \n",
        "hyperdrive_run.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Get the best model and display its properties"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "gather": {
          "logged": 1664177992546
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best Run Id:  HD_353966e4-eddf-47b6-b19d-22d440935562_4\n",
            "\n",
            " Best Accuracy: 0.9691535150645624\n"
          ]
        }
      ],
      "source": [
        "best_run = hyperdrive_run.get_best_run_by_primary_metric()\n",
        "best_run_metrics = best_run.get_metrics()\n",
        "parameter_values = best_run.get_details()['runDefinition']['arguments']\n",
        "\n",
        "print('Best Run Id: ', best_run.id)\n",
        "print('\\n Best Accuracy:', best_run_metrics['Accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "source": [
        "### Register the best model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "gather": {
          "logged": 1664177993449
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "outputs": [],
      "source": [
        "model = best_run.register_model(\n",
        "    model_name=\"hyperdrive-best-model\", model_path=\".\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "source": [
        "### Shutdown the computes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "gather": {
          "logged": 1664181236623
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Already deleted!\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "    instance = ComputeTarget(workspace=ws, name=cluster_name)\n",
        "\n",
        "    instance.delete()\n",
        "    instance.wait_for_completion(show_output=True)\n",
        "    print('Deleted compute resource')\n",
        "\n",
        "except ComputeTargetException as e:\n",
        "    print('Already deleted!')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "kernelspec": {
      "display_name": "Python 3.9.6 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.6"
    },
    "microsoft": {
      "host": {
        "AzureML": {
          "notebookHasBeenCompleted": true
        }
      }
    },
    "nteract": {
      "version": "nteract-front-end@1.0.0"
    },
    "vscode": {
      "interpreter": {
        "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}