v1/python-sdk/tutorials/automl-with-azureml/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=\"red\" size=\"5\"><strong>!Important!</strong> </br>This notebook is outdated and is not supported by the AutoML Team. Please use the supported version ([link](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines/1k_demand_forecasting_with_pipeline_components/automl-forecasting-demand-hierarchical-timeseries-in-pipeline)).</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical Time Series - Automated ML\n",
    "**_Generate hierarchical time series forecasts with Automated Machine Learning_**\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this notebook we are using a synthetic dataset portraying sales data to predict the the quantity of a vartiety of product skus across several states, stores, and product categories.\n",
    "\n",
    "**NOTE: There are limits on how many runs we can do in parallel per workspace, and we currently recommend to set the parallelism to maximum of 320 runs per experiment per workspace. If users want to have more parallelism and increase this limit they might encounter Too Many Requests errors (HTTP 429).**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prerequisites\n",
    "You'll need to create a compute Instance by following [these](https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-create-manage-compute-instance?tabs=python) instructions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.0 Set up workspace, datastore, experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gather": {
     "logged": 1613003526897
    }
   },
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "from azureml.core import Workspace, Datastore\n",
    "import pandas as pd\n",
    "\n",
    "# Set up your workspace\n",
    "ws = Workspace.from_config()\n",
    "ws.get_details()\n",
    "\n",
    "# Set up your datastores\n",
    "dstore = ws.get_default_datastore()\n",
    "\n",
    "output = {}\n",
    "output[\"SDK version\"] = azureml.core.VERSION\n",
    "output[\"Subscription ID\"] = ws.subscription_id\n",
    "output[\"Workspace\"] = ws.name\n",
    "output[\"Resource Group\"] = ws.resource_group\n",
    "output[\"Location\"] = ws.location\n",
    "output[\"Default datastore name\"] = dstore.name\n",
    "output[\"SDK Version\"] = azureml.core.VERSION\n",
    "pd.set_option(\"display.max_colwidth\", None)\n",
    "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
    "outputDf.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choose an experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gather": {
     "logged": 1613003540729
    }
   },
   "outputs": [],
   "source": [
    "from azureml.core import Experiment\n",
    "\n",
    "experiment = Experiment(ws, \"automl-hts\")\n",
    "\n",
    "print(\"Experiment name: \" + experiment.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.0 Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "source": [
    "### Upload local csv files to datastore\n",
    "You can upload your train and inference csv files to the default datastore in your workspace. \n",
    "\n",
    "A Datastore is a place where data can be stored that is then made accessible to a compute either by means of mounting or copying the data to the compute target.\n",
    "Please refer to [Datastore](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py) documentation on how to access data from Datastore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datastore_path = \"hts-sample\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datastore = ws.get_default_datastore()\n",
    "datastore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the TabularDatasets \n",
    "\n",
    "Datasets in Azure Machine Learning are references to specific data in a Datastore. The data can be retrieved as a [TabularDatasets](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py). We will read in the data as a pandas DataFrame, upload to the data store and register them to your Workspace using ```register_pandas_dataframe``` so they can be called as an input into the training pipeline. We will use the inference dataset as part of the forecasting pipeline. The step need only be completed once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gather": {
     "logged": 1613007017296
    }
   },
   "outputs": [],
   "source": [
    "from azureml.data.dataset_factory import TabularDatasetFactory\n",
    "\n",
    "registered_train = TabularDatasetFactory.register_pandas_dataframe(\n",
    "    pd.read_csv(\"Data/hts-sample-train.csv\"),\n",
    "    target=(datastore, \"hts-sample\"),\n",
    "    name=\"hts-sales-train\",\n",
    ")\n",
    "registered_inference = TabularDatasetFactory.register_pandas_dataframe(\n",
    "    pd.read_csv(\"Data/hts-sample-test.csv\"),\n",
    "    target=(datastore, \"hts-sample\"),\n",
    "    name=\"hts-sales-test\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.0 Build the training pipeline\n",
    "Now that the dataset, WorkSpace, and datastore are set up, we can put together a pipeline for training.\n",
    "\n",
    "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choose a compute target\n",
    "\n",
    "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
    "\n",
    "\\*\\*Creation of AmlCompute takes approximately 5 minutes.**\n",
    "\n",
    "If the AmlCompute with that name is already in your workspace this code will skip the creation process. As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this [article](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-quotas) on the default limits and how to request more quota."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gather": {
     "logged": 1613007037308
    }
   },
   "outputs": [],
   "source": [
    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
    "\n",
    "# Name your cluster\n",
    "compute_name = \"hts-compute\"\n",
    "\n",
    "\n",
    "if compute_name in ws.compute_targets:\n",
    "    compute_target = ws.compute_targets[compute_name]\n",
    "    if compute_target and type(compute_target) is AmlCompute:\n",
    "        print(\"Found compute target: \" + compute_name)\n",
    "else:\n",
    "    print(\"Creating a new compute target...\")\n",
    "    provisioning_config = AmlCompute.provisioning_configuration(\n",
    "        vm_size=\"STANDARD_D16S_V3\", max_nodes=20\n",
    "    )\n",
    "    # Create the compute target\n",
    "    compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n",
    "\n",
    "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
    "    # If no min node count is provided it will use the scale settings for the cluster\n",
    "    compute_target.wait_for_completion(\n",
    "        show_output=True, min_node_count=None, timeout_in_minutes=20\n",
    "    )\n",
    "\n",
    "    # For a more detailed view of current cluster status, use the 'status' property\n",
    "    print(compute_target.status.serialize())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up training parameters\n",
    "\n",
    "We need to provide ``ForecastingParameters``, ``AutoMLConfig`` and ``HTSTrainParameters`` objects. For the forecasting task we need to define several settings including the name of the time column, the maximum forecast horizon, the hierarchy definition, and the level of the hierarchy at which to train.\n",
    "\n",
    "#### ``ForecastingParameters`` arguments\n",
    "| Property                           | Description|\n",
    "| :---------------                   | :------------------- |\n",
    "| **forecast_horizon**               | The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly). Periods are inferred from your data. |\n",
    "| **time_column_name**               | The name of your time column. |\n",
    "| **time_series_id_column_names**    | The column names used to uniquely identify timeseries in data that has multiple rows with the same timestamp. |\n",
    "| **cv_step_size**                   | Number of periods between two consecutive cross-validation folds. The default value is \\\"auto\\\", in which case AutoMl determines the cross-validation step size automatically, if a validation set is not provided. Or users could specify an integer value. |\n",
    "\n",
    "#### ``AutoMLConfig`` arguments\n",
    "| Property                           | Description|\n",
    "| :---------------                   | :------------------- |\n",
    "| **task**                           | forecasting |\n",
    "| **primary_metric**                 | This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i> |\n",
    "| **blocked_models**                 | Blocked models won't be used by AutoML. |\n",
    "| **iteration_timeout_minutes**      | Maximum amount of time in minutes that the model can train. This is optional but provides customers with greater control on exit criteria. |\n",
    "| **iterations**                     | Number of models to train. This is optional but provides customers with greater control on exit criteria. |\n",
    "| **experiment_timeout_hours**       | Maximum amount of time in hours that each experiment can take before it terminates. This is optional but provides customers with greater control on exit criteria. **It does not control the overall timeout for the pipeline run, instead controls the timeout for each training run per partitioned time series.** |\n",
    "| **label_column_name**              | The name of the label column. |\n",
    "| **n_cross_validations**            | Number of cross validation splits. The default value is \\\"auto\\\", in which case AutoMl determines the number of cross-validations automatically, if a validation set is not provided. Or users could specify an integer value. Rolling Origin Validation is used to split time-series in a temporally consistent way. |\n",
    "| **enable_early_stopping**          | Flag to enable early termination if the primary metric is no longer improving. |\n",
    "| **enable_engineered_explanations** | Engineered feature explanations will be downloaded if enable_engineered_explanations flag is set to True. By default it is set to False to save storage space. |\n",
    "| **track_child_runs**               | Flag to disable tracking of child runs. Only best run is tracked if the flag is set to False (this includes the model and metrics of the run). |\n",
    "| **pipeline_fetch_max_batch_size**  | Determines how many pipelines (training algorithms) to fetch at a time for training, this helps reduce throttling when training at large scale. |\n",
    "| **model_explainability**           | Flag to disable explaining the best automated ML model at the end of all training iterations. The default is True and will block non-explainable models which may impact the forecast accuracy. For more information, see [Interpretability: model explanations in automated machine learning](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-automl). |\n",
    "\n",
    "#### ``HTSTrainParameters`` arguments\n",
    "| Property                           | Description|\n",
    "| :---------------                   | :------------------- |\n",
    "| **automl_settings**                | The ``AutoMLConfig`` object defined above. |\n",
    "| **hierarchy_column_names**         | The names of columns that define the hierarchical structure of the data from highest level to most granular. |\n",
    "| **training_level**                 | The level of the hierarchy to be used for training models. |\n",
    "| **enable_engineered_explanations** | The switch controls engineered explanations. |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gather": {
     "logged": 1613007061544
    }
   },
   "outputs": [],
   "source": [
    "from azureml.train.automl.runtime._hts.hts_parameters import HTSTrainParameters\n",
    "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
    "from azureml.train.automl.automlconfig import AutoMLConfig\n",
    "\n",
    "\n",
    "model_explainability = True\n",
    "\n",
    "engineered_explanations = False\n",
    "# Define your hierarchy. Adjust the settings below based on your dataset.\n",
    "hierarchy = [\"state\", \"store_id\", \"product_category\", \"SKU\"]\n",
    "training_level = \"SKU\"\n",
    "\n",
    "# Set your forecast parameters. Adjust the settings below based on your dataset.\n",
    "time_column_name = \"date\"\n",
    "label_column_name = \"quantity\"\n",
    "forecast_horizon = 7\n",
    "\n",
    "forecasting_parameters = ForecastingParameters(\n",
    "    time_column_name=time_column_name,\n",
    "    forecast_horizon=forecast_horizon,\n",
    ")\n",
    "\n",
    "automl_settings = AutoMLConfig(\n",
    "    task=\"forecasting\",\n",
    "    primary_metric=\"normalized_root_mean_squared_error\",\n",
    "    experiment_timeout_hours=1,\n",
    "    label_column_name=label_column_name,\n",
    "    track_child_runs=False,\n",
    "    forecasting_parameters=forecasting_parameters,\n",
    "    pipeline_fetch_max_batch_size=15,\n",
    "    model_explainability=model_explainability,\n",
    "    n_cross_validations=\"auto\",  # Feel free to set to a small integer (>=2) if runtime is an issue.\n",
    "    cv_step_size=\"auto\",\n",
    "    # The following settings are specific to this sample and should be adjusted according to your own needs.\n",
    "    iteration_timeout_minutes=10,\n",
    "    iterations=15,\n",
    ")\n",
    "\n",
    "hts_parameters = HTSTrainParameters(\n",
    "    automl_settings=automl_settings,\n",
    "    hierarchy_column_names=hierarchy,\n",
    "    training_level=training_level,\n",
    "    enable_engineered_explanations=engineered_explanations,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up hierarchy training pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parallel run step is leveraged to train multiple models at once. To configure the ParallelRunConfig you will need to determine the appropriate number of workers and nodes for your use case. The ``process_count_per_node`` is based off the number of cores of the compute VM. The node_count will determine the number of master nodes to use, increasing the node count will speed up the training process.\n",
    "\n",
    "| Property                           | Description|\n",
    "| :---------------                   | :------------------- |\n",
    "| **experiment**                     | The experiment used for training. |\n",
    "| **train_data**                     | The file dataset to be used as input to the training run. |\n",
    "| **node_count**                     | The number of compute nodes to be used for running the user script. We recommend to start with 3 and increase the node_count if the training time is taking too long. |\n",
    "| **process_count_per_node**         | Process count per node, we recommend 2:1 ratio for number of cores: number of processes per node. eg. If node has 16 cores then configure 8 or less process count per node for optimal performance. |\n",
    "| **train_pipeline_parameters**      | The set of configuration parameters defined in the previous section. |\n",
    "| **run_invocation_timeout**         | Maximum amount of time in seconds that the ``ParallelRunStep`` class is allowed. This is optional but provides customers with greater control on exit criteria. This must be greater than ``experiment_timeout_hours`` by at least 300 seconds. |\n",
    "\n",
    "Calling this method will create a new aggregated dataset which is generated dynamically on pipeline execution.\n",
    "\n",
    "**Note**: Total time taken for the **training step** in the pipeline to complete = $ \\frac{t}{ p \\times n } \\times ts $\n",
    "where,\n",
    "- $ t $ is time taken for training one partition (can be viewed in the training logs)\n",
    "- $ p $ is ``process_count_per_node``\n",
    "- $ n $ is ``node_count``\n",
    "- $ ts $ is total number of partitions in time series based on ``partition_column_names``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.contrib.automl.pipeline.steps import AutoMLPipelineBuilder\n",
    "\n",
    "\n",
    "training_pipeline_steps = AutoMLPipelineBuilder.get_many_models_train_steps(\n",
    "    experiment=experiment,\n",
    "    train_data=registered_train,\n",
    "    compute_target=compute_target,\n",
    "    node_count=2,\n",
    "    process_count_per_node=8,\n",
    "    train_pipeline_parameters=hts_parameters,\n",
    "    run_invocation_timeout=3900,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.pipeline.core import Pipeline\n",
    "\n",
    "training_pipeline = Pipeline(ws, steps=training_pipeline_steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submit the pipeline to run\n",
    "Next we submit our pipeline to run. The whole training pipeline takes about 1h using a Standard_D16_V3 VM with our current ParallelRunConfig setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_run = experiment.submit(training_pipeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_run.wait_for_completion(show_output=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the run status, if training_run is in completed state, continue to forecasting. If training_run is in another state, check the portal for failures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Optional] Get the explanations\n",
    "First we need to download the explanations to the local disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if model_explainability:\n",
    "    expl_output = training_run.get_pipeline_output(\"explanations\")\n",
    "    expl_output.download(\"training_explanations\")\n",
    "else:\n",
    "    print(\n",
    "        \"Model explanations are available only if model_explainability is set to True.\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The explanations are downloaded to the \"training_explanations/azureml\" directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "if model_explainability:\n",
    "    explanations_dirrectory = os.listdir(\n",
    "        os.path.join(\"training_explanations\", \"azureml\")\n",
    "    )\n",
    "    if len(explanations_dirrectory) > 1:\n",
    "        print(\n",
    "            \"Warning! The directory contains multiple explanations, only the first one will be displayed.\"\n",
    "        )\n",
    "    print(\"The explanations are located at {}.\".format(explanations_dirrectory[0]))\n",
    "    # Now we will list all the explanations.\n",
    "    explanation_path = os.path.join(\n",
    "        \"training_explanations\",\n",
    "        \"azureml\",\n",
    "        explanations_dirrectory[0],\n",
    "        \"training_explanations\",\n",
    "    )\n",
    "    print(\"Available explanations\")\n",
    "    print(\"==============================\")\n",
    "    print(\"\\n\".join(os.listdir(explanation_path)))\n",
    "else:\n",
    "    print(\n",
    "        \"Model explanations are available only if model_explainability is set to True.\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View the explanations on \"state\" level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "\n",
    "explanation_type = \"raw\"\n",
    "level = \"state\"\n",
    "\n",
    "if model_explainability:\n",
    "    display(\n",
    "        pd.read_csv(\n",
    "            os.path.join(explanation_path, \"{}_explanations_{}.csv\").format(\n",
    "                explanation_type, level\n",
    "            )\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.0 Forecasting\n",
    "For hierarchical forecasting we need to provide the HTSInferenceParameters object.\n",
    "#### ``HTSInferenceParameters`` arguments\n",
    "| Property                           | Description|\n",
    "| :---------------                   | :------------------- |\n",
    "| **hierarchy_forecast_level:**      | The default level of the hierarchy to produce prediction/forecast on. |\n",
    "| **allocation_method:**             | \\[Optional] The disaggregation method to use if the hierarchy forecast level specified is below the define hierarchy training level. <br><i>(average historical proportions) 'average_historical_proportions'</i><br><i>(proportions of the historical averages) 'proportions_of_historical_average'</i> |\n",
    "\n",
    "#### ``get_many_models_batch_inference_steps`` arguments\n",
    "| Property                           | Description|\n",
    "| :---------------                   | :------------------- |\n",
    "| **experiment**                     | The experiment used for inference run. |\n",
    "| **inference_data**                 | The data to use for inferencing. It should be the same schema as used for training.\n",
    "| **compute_target**                 | The compute target that runs the inference pipeline. |\n",
    "| **node_count**                     | The number of compute nodes to be used for running the user script. We recommend to start with the number of cores per node (varies by compute sku). |\n",
    "| **process_count_per_node**         | \\[Optional] The number of processes per node. By default it's 2 (should be at most half of the number of cores in a single node of the compute cluster that will be used for the experiment).\n",
    "| **inference_pipeline_parameters**  | \\[Optional] The ``HTSInferenceParameters`` object defined above. |\n",
    "| **train_run_id**                   | \\[Optional] The run id of the **training pipeline**. By default it is the latest successful training pipeline run in the experiment. |\n",
    "| **train_experiment_name**          | \\[Optional] The train experiment that contains the train pipeline. This one is only needed when the train pipeline is not in the same experiement as the inference pipeline. |\n",
    "| **run_invocation_timeout**         | \\[Optional] Maximum amount of time in seconds that the ``ParallelRunStep`` class is allowed. This is optional but provides customers with greater control on exit criteria. |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.automl.runtime._hts.hts_parameters import HTSInferenceParameters\n",
    "\n",
    "inference_parameters = HTSInferenceParameters(\n",
    "    hierarchy_forecast_level=\"store_id\",  # The setting is specific to this dataset and should be changed based on your dataset.\n",
    "    allocation_method=\"proportions_of_historical_average\",\n",
    ")\n",
    "\n",
    "steps = AutoMLPipelineBuilder.get_many_models_batch_inference_steps(\n",
    "    experiment=experiment,\n",
    "    inference_data=registered_inference,\n",
    "    compute_target=compute_target,\n",
    "    inference_pipeline_parameters=inference_parameters,\n",
    "    node_count=2,\n",
    "    process_count_per_node=8,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.pipeline.core import Pipeline\n",
    "\n",
    "inference_pipeline = Pipeline(ws, steps=steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inference_run = experiment.submit(inference_pipeline)\n",
    "inference_run.wait_for_completion(show_output=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieve results\n",
    "\n",
    "Forecast results can be retrieved through the following code. The prediction results summary and the actual predictions are downloaded in forecast_results folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecasts = inference_run.get_pipeline_output(\"forecasts\")\n",
    "forecasts.download(\"forecast_results\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resbumit the Pipeline\n",
    "\n",
    "The inference pipeline can be submitted with different configurations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inference_run = experiment.submit(\n",
    "    inference_pipeline, pipeline_parameters={\"hierarchy_forecast_level\": \"state\"}\n",
    ")\n",
    "inference_run.wait_for_completion(show_output=False)"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "jialiu"
   }
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  "categories": [
   "how-to-use-azureml",
   "automated-machine-learning"
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