udacity-project.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from azureml.core import Workspace, Experiment\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "exp = Experiment(workspace=ws, name=\"udacity-project\")\n",
        "\n",
        "print('Workspace name: ' + ws.name, \n",
        "      'Azure region: ' + ws.location, \n",
        "      'Subscription id: ' + ws.subscription_id, \n",
        "      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
        "\n",
        "run = exp.start_logging()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "gather": {
          "logged": 1662977319339
        }
      }
    },
    {
      "cell_type": "code",
      "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 = \"optimize-pipeline-cluster\"\n",
        "\n",
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    cpu_cluster = 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",
        "    cpu_cluster = ComputeTarget.create(ws, cluster_name, compute_config)\n",
        "\n",
        "cpu_cluster.wait_for_completion(show_output=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "InProgress.\nSucceededProvisioning operation finished, operation \"Succeeded\"\nSucceeded\nAmlCompute wait for completion finished\n\nMinimum number of nodes requested have been provisioned\n"
        }
      ],
      "execution_count": 2,
      "metadata": {
        "gather": {
          "logged": 1662977326060
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Training the model using Hyperdrive on our custom training script"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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, uniform\n",
        "from azureml.core import Environment, ScriptRunConfig\n",
        "import os\n",
        "\n",
        "# Specify parameter sampler\n",
        "ps = RandomParameterSampling( {\n",
        "        \"C\": choice(0.01, 0.001, 0.1, 1),\n",
        "        \"max_iter\": choice(50,100),\n",
        "    }\n",
        ")\n",
        "\n",
        "# Specify a Policy\n",
        "policy = BanditPolicy(slack_factor = 0.1, evaluation_interval=1, delay_evaluation=5)\n",
        "\n",
        "if \"training\" not in os.listdir():\n",
        "    os.mkdir(\"./training\")\n",
        "\n",
        "# Setup environment for your training run\n",
        "sklearn_env = Environment.from_conda_specification(name='sklearn-env', file_path='conda_dependencies.yml')\n",
        "\n",
        "# Create a ScriptRunConfig Object to specify the configuration details of your training job\n",
        "src = ScriptRunConfig(source_directory=\".\",\n",
        "                      script='train.py',\n",
        "                      compute_target=cpu_cluster,\n",
        "                      environment=sklearn_env)\n",
        "\n",
        "# Create a HyperDriveConfig using the src object, hyperparameter sampler, and policy.\n",
        "hyperdrive_config = HyperDriveConfig(run_config=src,\n",
        "                             hyperparameter_sampling=ps,\n",
        "                             policy=policy,\n",
        "                             primary_metric_name='Accuracy',\n",
        "                             primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n",
        "                             max_total_runs=10,\n",
        "                             max_concurrent_runs=4)"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "gather": {
          "logged": 1662977330136
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Submit your hyperdrive run to the experiment and show run details with the widget.\n",
        "hyperdrive_run = exp.submit(hyperdrive_config)"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "gather": {
          "logged": 1662977331329
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(hyperdrive_run).show() \n",
        "hyperdrive_run.wait_for_completion(show_output=True)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "_HyperDriveWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO'…",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f8205ef20e6406ab44a4a4cbaef08ea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/aml.mini.widget.v1": "{\"status\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/runs/HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b?wsid=/subscriptions/f9d5a085-54dc-4215-9ba6-dad5d86e60a0/resourcegroups/aml-quickstarts-207163/workspaces/quick-starts-ws-207163&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\", \"run_id\": \"HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b\", \"run_properties\": {\"run_id\": \"HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b\", \"created_utc\": \"2022-09-12T10:08:51.003787Z\", \"properties\": {\"primary_metric_config\": \"{\\\"name\\\":\\\"Accuracy\\\",\\\"goal\\\":\\\"maximize\\\"}\", \"resume_from\": \"null\", \"runTemplate\": \"HyperDrive\", \"azureml.runsource\": \"hyperdrive\", \"platform\": \"AML\", \"ContentSnapshotId\": \"4e3b41bf-23f3-419b-b0d0-c3e9613125db\", \"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\", \"space_size\": \"8\", \"score\": \"0.9100509832483612\", \"best_child_run_id\": \"HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_1\", \"best_metric_status\": \"Succeeded\", \"best_data_container_id\": \"dcid.HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_1\"}, \"tags\": {\"_aml_system_max_concurrent_jobs\": \"4\", \"_aml_system_max_total_jobs\": \"10\", \"_aml_system_max_duration_minutes\": \"10080\", \"_aml_system_policy_config\": \"{\\\"name\\\":\\\"Bandit\\\",\\\"properties\\\":{\\\"evaluation_interval\\\":1,\\\"delay_evaluation\\\":5,\\\"slack_factor\\\":0.1}}\", \"_aml_system_generator_config\": \"{\\\"name\\\":\\\"RANDOM\\\",\\\"parameter_space\\\":{\\\"C\\\":[\\\"choice\\\",[[0.01,0.001,0.1,1]]],\\\"max_iter\\\":[\\\"choice\\\",[[50,100]]]},\\\"properties\\\":null}\", \"_aml_system_primary_metric_config\": \"{\\\"name\\\":\\\"Accuracy\\\",\\\"goal\\\":\\\"maximize\\\"}\", \"_aml_system_platform_config\": \"{\\\"ServiceAddress\\\": \\\"https://southcentralus.experiments.azureml.net\\\", \\\"SubscriptionId\\\": 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\\\"vmSize\\\": null}, \\\"Overrides\\\": {\\\"Script\\\": \\\"train.py\\\", \\\"Command\\\": \\\"\\\", \\\"UseAbsolutePath\\\": false, \\\"Arguments\\\": [], \\\"SourceDirectoryDataStore\\\": null, \\\"Framework\\\": 0, \\\"Communicator\\\": 0, \\\"Target\\\": \\\"optimize-pipeline-cluster\\\", \\\"DataReferences\\\": {}, \\\"Data\\\": {}, \\\"OutputData\\\": {}, \\\"Datacaches\\\": [], \\\"JobName\\\": null, \\\"MaxRunDurationSeconds\\\": 2592000, \\\"NodeCount\\\": 1, \\\"InstanceTypes\\\": [], \\\"Priority\\\": null, \\\"CredentialPassthrough\\\": false, \\\"Identity\\\": null, \\\"Environment\\\": {\\\"Name\\\": \\\"sklearn-env\\\", \\\"AutoRebuild\\\": true, \\\"Python\\\": {\\\"InterpreterPath\\\": \\\"python\\\", \\\"UserManagedDependencies\\\": false, \\\"CondaDependencies\\\": {\\\"dependencies\\\": [\\\"python=3.6.2\\\", \\\"scikit-learn\\\", \\\"numpy\\\", \\\"pandas\\\", {\\\"pip\\\": [\\\"azureml-defaults\\\"]}]}, \\\"BaseCondaEnvironment\\\": null}, \\\"EnvironmentVariables\\\": {\\\"EXAMPLE_ENV_VAR\\\": \\\"EXAMPLE_VALUE\\\"}, \\\"Docker\\\": {\\\"BaseImage\\\": \\\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:20220708.v1\\\", \\\"Platform\\\": {\\\"Os\\\": \\\"Linux\\\", \\\"Architecture\\\": \\\"amd64\\\"}, \\\"BaseDockerfile\\\": null, \\\"BaseImageRegistry\\\": {\\\"Address\\\": null, \\\"Username\\\": null, \\\"Password\\\": null}, \\\"Enabled\\\": false, \\\"Arguments\\\": []}, \\\"Spark\\\": {\\\"Repositories\\\": [], \\\"Packages\\\": [], \\\"PrecachePackages\\\": true}, \\\"InferencingStackVersion\\\": null}, \\\"History\\\": {\\\"OutputCollection\\\": true, \\\"DirectoriesToWatch\\\": [\\\"logs\\\"], \\\"EnableMLflowTracking\\\": true, \\\"snapshotProject\\\": true}, \\\"Spark\\\": {\\\"Configuration\\\": {\\\"spark.app.name\\\": \\\"Azure ML Experiment\\\", \\\"spark.yarn.maxAppAttempts\\\": \\\"1\\\"}}, \\\"ParallelTask\\\": {\\\"MaxRetriesPerWorker\\\": 0, \\\"WorkerCountPerNode\\\": 1, \\\"TerminalExitCodes\\\": null, \\\"Configuration\\\": {}}, \\\"BatchAi\\\": {\\\"NodeCount\\\": 0}, \\\"AmlCompute\\\": {\\\"Name\\\": null, \\\"VmSize\\\": null, \\\"RetainCluster\\\": false, \\\"ClusterMaxNodeCount\\\": null}, \\\"AISuperComputer\\\": {\\\"InstanceType\\\": \\\"D2\\\", \\\"FrameworkImage\\\": null, \\\"ImageVersion\\\": \\\"pytorch-1.7.0\\\", \\\"Location\\\": null, \\\"AISuperComputerStorageData\\\": null, \\\"Interactive\\\": false, \\\"ScalePolicy\\\": null, \\\"VirtualClusterArmId\\\": null, \\\"TensorboardLogDirectory\\\": null, \\\"SSHPublicKey\\\": null, \\\"SSHPublicKeys\\\": null, \\\"EnableAzmlInt\\\": true, \\\"Priority\\\": \\\"Medium\\\", \\\"SLATier\\\": \\\"Standard\\\", \\\"UserAlias\\\": null}, \\\"KubernetesCompute\\\": {\\\"InstanceType\\\": null}, \\\"Tensorflow\\\": {\\\"WorkerCount\\\": 1, \\\"ParameterServerCount\\\": 1}, \\\"Mpi\\\": {\\\"ProcessCountPerNode\\\": 1}, \\\"PyTorch\\\": {\\\"CommunicationBackend\\\": \\\"nccl\\\", \\\"ProcessCount\\\": null}, \\\"Hdi\\\": {\\\"YarnDeployMode\\\": 2}, \\\"ContainerInstance\\\": {\\\"Region\\\": null, \\\"CpuCores\\\": 2.0, \\\"MemoryGb\\\": 3.5}, \\\"ExposedPorts\\\": null, \\\"Docker\\\": {\\\"UseDocker\\\": false, \\\"SharedVolumes\\\": true, \\\"ShmSize\\\": \\\"2g\\\", \\\"Arguments\\\": []}, \\\"Cmk8sCompute\\\": {\\\"Configuration\\\": {}}, \\\"CommandReturnCodeConfig\\\": {\\\"ReturnCode\\\": 0, \\\"SuccessfulReturnCodes\\\": []}, \\\"EnvironmentVariables\\\": {}, \\\"ApplicationEndpoints\\\": {}, \\\"Parameters\\\": []}, \\\"SnapshotId\\\": \\\"4e3b41bf-23f3-419b-b0d0-c3e9613125db\\\", \\\"Snapshots\\\": [], \\\"SourceCodeDataReference\\\": null, \\\"ParentRunId\\\": null, \\\"DataContainerId\\\": null, \\\"RunType\\\": null, \\\"DisplayName\\\": null, \\\"EnvironmentAssetId\\\": null, \\\"Properties\\\": {}, \\\"Tags\\\": {}, \\\"AggregatedArtifactPath\\\": null}, \\\"ParentRunId\\\": \\\"HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b\\\"}\", \"_aml_system_resume_child_runs\": \"null\", \"_aml_system_all_jobs_generated\": \"true\", \"_aml_system_cancellation_requested\": \"false\", \"_aml_system_progress_metadata_evaluation_timestamp\": \"\\\"2022-09-12T10:08:51.636454\\\"\", \"_aml_system_progress_metadata_digest\": \"\\\"c6d2e237756b2611b16c5d416126eeb61adb842b8691c2d89823d1fc9f7a9378\\\"\", \"_aml_system_progress_metadata_active_timestamp\": \"\\\"2022-09-12T10:08:51.636454\\\"\", \"_aml_system_optimizer_state_artifact\": \"null\", \"_aml_system_outdated_optimizer_state_artifacts\": \"\\\"[]\\\"\", \"_aml_system_HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_0\": \"{\\\"C\\\": 1, \\\"max_iter\\\": 50}\", \"_aml_system_HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_1\": \"{\\\"C\\\": 0.1, \\\"max_iter\\\": 100}\", \"_aml_system_HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_2\": \"{\\\"C\\\": 0.01, \\\"max_iter\\\": 100}\", \"_aml_system_HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_3\": \"{\\\"C\\\": 0.01, \\\"max_iter\\\": 50}\", \"_aml_system_HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_4\": \"{\\\"C\\\": 0.001, \\\"max_iter\\\": 50}\", \"_aml_system_HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_5\": \"{\\\"C\\\": 0.001, \\\"max_iter\\\": 100}\", \"_aml_system_HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_6\": \"{\\\"C\\\": 1, \\\"max_iter\\\": 100}\", \"_aml_system_HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_7\": \"{\\\"C\\\": 0.1, \\\"max_iter\\\": 50}\", \"_aml_system_final_best_metric_update_retry_count\": \"1\"}, \"script_name\": null, \"arguments\": null, \"end_time_utc\": \"2022-09-12T10:29:00.241011Z\", \"status\": \"Completed\", \"log_files\": {\"azureml-logs/hyperdrive.txt\": 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[\"Accuracy\", \"Accuracy\", \"Accuracy\"], \"run_id\": [\"HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_2\", \"HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_1\", \"HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_1\"], \"final\": [false, false, true]}]}]}], \"run_logs\": \"[2022-09-12T10:08:51.736473][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\n[2022-09-12T10:08:52.5267455Z][SCHEDULER][INFO]Scheduling job, id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_0' \\n[2022-09-12T10:08:52.6485848Z][SCHEDULER][INFO]Scheduling job, id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_1' \\n[2022-09-12T10:08:52.7690083Z][SCHEDULER][INFO]Scheduling job, id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_2' \\n[2022-09-12T10:08:52.828224][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\n[2022-09-12T10:08:52.8812340Z][SCHEDULER][INFO]Scheduling job, id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_3' \\n[2022-09-12T10:08:53.1482890Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_1' \\n[2022-09-12T10:08:53.1592295Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_3' \\n[2022-09-12T10:08:53.2249787Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_2' \\n[2022-09-12T10:08:53.5098252Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_0' \\n[2022-09-12T10:25:22.257033][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\n[2022-09-12T10:25:22.5354523Z][SCHEDULER][INFO]Scheduling job, id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_4' \\n[2022-09-12T10:25:22.490636][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\n[2022-09-12T10:25:23.0595703Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_4' \\n[2022-09-12T10:26:22.227469][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\n[2022-09-12T10:26:22.474906][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\n[2022-09-12T10:26:22.5022966Z][SCHEDULER][INFO]Scheduling job, id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_5' \\n[2022-09-12T10:26:22.7083880Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_5' \\n[2022-09-12T10:27:22.212399][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\n[2022-09-12T10:27:22.5826384Z][SCHEDULER][INFO]Scheduling job, id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_6' \\n[2022-09-12T10:27:22.7023959Z][SCHEDULER][INFO]Scheduling job, id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_7' \\n[2022-09-12T10:27:22.659250][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\n[2022-09-12T10:27:22.7994695Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_6' \\n[2022-09-12T10:27:22.9155070Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_7' \\n[2022-09-12T10:27:52.193293][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\n[2022-09-12T10:27:52.250995][GENERATOR][WARNING]Could not sample any more jobs from the space.\\n[2022-09-12T10:29:00.427172][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": "stream",
          "name": "stdout",
          "text": "RunId: HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b\nWeb View: https://ml.azure.com/runs/HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b?wsid=/subscriptions/f9d5a085-54dc-4215-9ba6-dad5d86e60a0/resourcegroups/aml-quickstarts-207163/workspaces/quick-starts-ws-207163&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\n\nStreaming azureml-logs/hyperdrive.txt\n=====================================\n\n[2022-09-12T10:08:51.736473][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\n[2022-09-12T10:08:52.5267455Z][SCHEDULER][INFO]Scheduling job, id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_0' \n[2022-09-12T10:08:52.6485848Z][SCHEDULER][INFO]Scheduling job, id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_1' \n[2022-09-12T10:08:52.7690083Z][SCHEDULER][INFO]Scheduling job, id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_2' \n[2022-09-12T10:08:52.828224][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\n[2022-09-12T10:08:52.8812340Z][SCHEDULER][INFO]Scheduling job, id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_3' \n[2022-09-12T10:08:53.1482890Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_1' \n[2022-09-12T10:08:53.1592295Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_3' \n[2022-09-12T10:08:53.2249787Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_2' \n[2022-09-12T10:08:53.5098252Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_0' \n[2022-09-12T10:25:22.257033][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\n[2022-09-12T10:25:22.5354523Z][SCHEDULER][INFO]Scheduling job, id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_4' \n[2022-09-12T10:25:22.490636][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\n[2022-09-12T10:25:23.0595703Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_4' \n[2022-09-12T10:26:22.227469][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\n[2022-09-12T10:26:22.474906][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\n[2022-09-12T10:26:22.5022966Z][SCHEDULER][INFO]Scheduling job, id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_5' \n[2022-09-12T10:26:22.7083880Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_5' \n[2022-09-12T10:27:22.212399][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2022-09-12T10:27:22.5826384Z][SCHEDULER][INFO]Scheduling job, id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_6' \n[2022-09-12T10:27:22.7023959Z][SCHEDULER][INFO]Scheduling job, id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_7' \n[2022-09-12T10:27:22.659250][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2022-09-12T10:27:22.7994695Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_6' \n[2022-09-12T10:27:22.9155070Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_7' \n[2022-09-12T10:27:52.193293][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2022-09-12T10:27:52.250995][GENERATOR][WARNING]Could not sample any more jobs from the space.\n[2022-09-12T10:29:00.427172][CONTROLLER][INFO]Experiment was 'ExperimentStatus.RUNNING', is 'ExperimentStatus.FINISHED'.\n\nExecution Summary\n=================\nRunId: HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b\nWeb View: https://ml.azure.com/runs/HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b?wsid=/subscriptions/f9d5a085-54dc-4215-9ba6-dad5d86e60a0/resourcegroups/aml-quickstarts-207163/workspaces/quick-starts-ws-207163&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\n\n"
        },
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "{'runId': 'HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b',\n 'target': 'optimize-pipeline-cluster',\n 'status': 'Completed',\n 'startTimeUtc': '2022-09-12T10:08:51.058504Z',\n 'endTimeUtc': '2022-09-12T10:29:00.241011Z',\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': '4e3b41bf-23f3-419b-b0d0-c3e9613125db',\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': '0.9100509832483612',\n  'best_child_run_id': 'HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_1',\n  'best_metric_status': 'Succeeded',\n  'best_data_container_id': 'dcid.HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_1'},\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': '340d0e41-99a3-4caf-a12b-ef1d66f2c390',\n   'amlClientSessionId': '57e75686-c6c7-4d01-9c99-48460be30024',\n   'subscriptionId': 'f9d5a085-54dc-4215-9ba6-dad5d86e60a0',\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': '4e3b41bf-23f3-419b-b0d0-c3e9613125db',\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://mlstrg207163.blob.core.windows.net/azureml/ExperimentRun/dcid.HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b/azureml-logs/hyperdrive.txt?sv=2019-07-07&sr=b&sig=BTkYQ%2BA0KBgs8vTTQLA%2BJBSkFkn6reBZN%2FLfEbQWkVM%3D&skoid=60cd9028-d8f1-4bf3-ac06-c886a38b0737&sktid=660b3398-b80e-49d2-bc5b-ac1dc93b5254&skt=2022-09-12T09%3A58%3A54Z&ske=2022-09-13T18%3A08%3A54Z&sks=b&skv=2019-07-07&st=2022-09-12T10%3A19%3A03Z&se=2022-09-12T18%3A29%3A03Z&sp=r'},\n 'submittedBy': 'ODL_User 207163'}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "gather": {
          "logged": 1662978569143
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "# Get your best run and save the model from that run.\n",
        "\n",
        "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'])\n",
        "\n",
        "model = best_run.register_model(\n",
        "    model_name=\"hyperdrive-best-model\", model_path=\".\"\n",
        ")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Best Run Id:  HD_9b05eee2-7913-4c8d-9016-80784dc0ad7b_1\n\n Best Accuracy: 0.9100509832483612\n"
        }
      ],
      "execution_count": 6,
      "metadata": {
        "gather": {
          "logged": 1662978573399
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Training the model using AutoML "
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.data.dataset_factory import TabularDatasetFactory\n",
        "from azureml.core import Dataset\n",
        "\n",
        "# Create TabularDataset using TabularDatasetFactory\n",
        "file_path = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
        "ds = Dataset.Tabular.from_delimited_files(path=file_path)\n",
        " "
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "gather": {
          "logged": 1662978580738
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.train.automl import AutoMLConfig\n",
        "\n",
        "# Set parameters for AutoMLConfig\n",
        "# NOTE: DO NOT CHANGE THE experiment_timeout_minutes PARAMETER OR YOUR INSTANCE WILL TIME OUT.\n",
        "# If you wish to run the experiment longer, you will need to run this notebook in your own\n",
        "# Azure tenant, which will incur personal costs.\n",
        "automl_config = AutoMLConfig(\n",
        "    experiment_timeout_minutes=30,\n",
        "    task='classification',\n",
        "    primary_metric='accuracy',\n",
        "    training_data=ds,\n",
        "    label_column_name='y',\n",
        "    n_cross_validations=2)"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "gather": {
          "logged": 1662978580783
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Submit your automl run\n",
        "automl_run = exp.submit(automl_config, show_output=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "No run_configuration provided, running on local with default configuration\nRunning in the active local environment.\n"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "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>udacity-project</td><td>AutoML_ab7fc1d6-ed22-4d6c-b282-3bce39da90d0</td><td>automl</td><td>Preparing</td><td><a href=\"https://ml.azure.com/runs/AutoML_ab7fc1d6-ed22-4d6c-b282-3bce39da90d0?wsid=/subscriptions/f9d5a085-54dc-4215-9ba6-dad5d86e60a0/resourcegroups/aml-quickstarts-207163/workspaces/quick-starts-ws-207163&amp;tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\" target=\"_blank\" rel=\"noopener\">Link to Azure Machine Learning studio</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Current status: DatasetEvaluation. Gathering dataset statistics.\nCurrent status: FeaturesGeneration. Generating features for the dataset.\nCurrent status: DatasetFeaturization. Beginning to fit featurizers and featurize the dataset.\nCurrent status: DatasetFeaturizationCompleted. Completed fit featurizers and featurizing the dataset.\nCurrent status: DatasetBalancing. Performing class balancing sweeping\nCurrent status: DatasetCrossValidationSplit. Generating individually featurized CV splits.\n\n********************************************************************************************\nDATA GUARDRAILS: \n\nTYPE:         Class balancing detection\nSTATUS:       ALERTED\nDESCRIPTION:  To decrease model bias, please cancel the current run and fix balancing problem.\n              Learn more about imbalanced data: https://aka.ms/AutomatedMLImbalancedData\nDETAILS:      Imbalanced data can lead to a falsely perceived positive effect of a model's accuracy because the input data has bias towards one class.\n+------------------------------+--------------------------------+--------------------------------------+\n|Size of the smallest class    |Name/Label of the smallest class|Number of samples in the training data|\n+==============================+================================+======================================+\n|3692                          |yes                             |32950                                 |\n+------------------------------+--------------------------------+--------------------------------------+\n\n********************************************************************************************\n\nTYPE:         Missing feature values imputation\nSTATUS:       PASSED\nDESCRIPTION:  No feature missing values were detected in the training data.\n              Learn more about missing value imputation: https://aka.ms/AutomatedMLFeaturization\n\n********************************************************************************************\n\nTYPE:         High cardinality feature detection\nSTATUS:       PASSED\nDESCRIPTION:  Your inputs were analyzed, and no high cardinality features were detected.\n              Learn more about high cardinality feature handling: https://aka.ms/AutomatedMLFeaturization\n\n********************************************************************************************\nCurrent status: ModelSelection. Beginning model selection.\n\n********************************************************************************************\nITER: The iteration being evaluated.\nPIPELINE: A summary description of the pipeline being evaluated.\nDURATION: Time taken for the current iteration.\nMETRIC: The result of computing score on the fitted pipeline.\nBEST: The best observed score thus far.\n********************************************************************************************\n\n ITER   PIPELINE                                       DURATION            METRIC      BEST\n    0   MaxAbsScaler LightGBM                          0:00:28             0.9125    0.9125\n    1   MaxAbsScaler XGBoostClassifier                 0:00:34             0.9110    0.9125\n    2   MaxAbsScaler ExtremeRandomTrees                0:00:30             0.7373    0.9125\n    3   SparseNormalizer XGBoostClassifier             0:00:31             0.9132    0.9132\n    4   MaxAbsScaler LightGBM                          0:00:27             0.9123    0.9132\n    5   MaxAbsScaler LightGBM                          0:00:27             0.8883    0.9132\n    6   StandardScalerWrapper XGBoostClassifier        0:00:28             0.9077    0.9132\n    7   MaxAbsScaler LogisticRegression                0:00:29             0.9086    0.9132\n    8   StandardScalerWrapper ExtremeRandomTrees       0:00:28             0.8895    0.9132\n    9   StandardScalerWrapper XGBoostClassifier        0:00:28             0.9115    0.9132\n   10   SparseNormalizer LightGBM                      0:00:27             0.9041    0.9132\n   11   StandardScalerWrapper XGBoostClassifier        0:00:29             0.9134    0.9134\n   12   MaxAbsScaler LogisticRegression                0:00:29             0.9084    0.9134\n   13   MaxAbsScaler SGD                               0:00:27             0.8795    0.9134\n   14   StandardScalerWrapper XGBoostClassifier        0:00:29             0.9127    0.9134\n   15   SparseNormalizer RandomForest                  0:00:37             0.8193    0.9134\n   16   StandardScalerWrapper LogisticRegression       0:00:27             0.9084    0.9134\n   17   StandardScalerWrapper RandomForest             0:00:31             0.9015    0.9134\n   18   StandardScalerWrapper XGBoostClassifier        0:00:30             0.9124    0.9134\n   19   TruncatedSVDWrapper RandomForest               0:01:30             0.8245    0.9134\n   20   TruncatedSVDWrapper RandomForest               0:02:04             0.8327    0.9134\n   21   StandardScalerWrapper XGBoostClassifier        0:00:29             0.9117    0.9134\n   22   StandardScalerWrapper LightGBM                 0:00:31             0.9128    0.9134\n   23   StandardScalerWrapper XGBoostClassifier        0:01:34             0.9071    0.9134\n   24   MaxAbsScaler LightGBM                          0:00:27             0.8880    0.9134\n   25   StandardScalerWrapper XGBoostClassifier        0:00:28             0.8880    0.9134\n   26   MaxAbsScaler LightGBM                          0:00:28             0.9096    0.9134\n   27   StandardScalerWrapper XGBoostClassifier        0:00:44             0.9133    0.9134\n   28   StandardScalerWrapper ExtremeRandomTrees       0:00:59             0.8880    0.9134\n   29   MaxAbsScaler LightGBM                          0:00:27             0.8910    0.9134\n   30   VotingEnsemble                                 0:00:38             0.9171    0.9171\n   31   StackEnsemble                                  0:00:41             0.9157    0.9171\nStopping criteria reached at iteration 32. Ending experiment.\n********************************************************************************************\nCurrent status: BestRunExplainModel. Best run model explanations started\nCurrent status: ModelExplanationDataSetSetup. Model explanations data setup completed\nCurrent status: PickSurrogateModel. Choosing LightGBM as the surrogate model for explanations\nCurrent status: EngineeredFeatureExplanations. Computation of engineered features started\nCurrent status: EngineeredFeatureExplanations. Computation of engineered features completed\nCurrent status: RawFeaturesExplanations. Computation of raw features started\nCurrent status: RawFeaturesExplanations. Computation of raw features completed\nCurrent status: BestRunExplainModel. Best run model explanations completed\n********************************************************************************************\n"
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": "2022-09-12:10:55:50,518 INFO     [explanation_client.py:334] Using default datastore for uploads\n"
        }
      ],
      "execution_count": 9,
      "metadata": {
        "gather": {
          "logged": 1662980157355
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrieve and save your best automl model.\n",
        "\n",
        "best_automl_run = automl_run.get_best_child()\n",
        "\n",
        "best_automl_model_name = best_automl_run.properties['model_name']\n",
        "\n",
        "best_automl_model = best_automl_run.register_model(model_name=\"best-automl-model\", model_path='.')"
      ],
      "outputs": [],
      "execution_count": 10,
      "metadata": {
        "gather": {
          "logged": 1662980158370
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3. Delete the compute cluster"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fetch or create the compute resource\n",
        "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:\n",
        "    print('Already deleted!')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "InProgressCurrent provisioning state of AmlCompute is \"Deleting\"\n\n..........Current provisioning state of AmlCompute is \"Deleting\"\n\n..........Current provisioning state of AmlCompute is \"Deleting\"\n\n...........Current provisioning state of AmlCompute is \"Deleting\"\n\n.....\nSucceededProvisioning operation finished, operation \"Succeeded\"\nAlready deleted!\n"
        }
      ],
      "execution_count": 11,
      "metadata": {
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1662980385394
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3 (ipykernel)"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.5",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "microsoft": {
      "host": {
        "AzureML": {
          "notebookHasBeenCompleted": true
        }
      }
    },
    "nteract": {
      "version": "nteract-front-end@1.0.0"
    },
    "vscode": {
      "interpreter": {
        "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}