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how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb Normal file
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{
"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": [
"# Automated Machine Learning\n",
"_**Classification using whitelist models**_\n",
"\n",
"## Contents\n",
"1. [Introduction](#Introduction)\n",
"1. [Setup](#Setup)\n",
"1. [Data](#Data)\n",
"1. [Train](#Train)\n",
"1. [Results](#Results)\n",
"1. [Test](#Test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
"\n",
"Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
"This notebooks shows how can automl can be trained on a a selected list of models,see the readme.md for the models.\n",
"This trains the model exclusively on tensorflow based models.\n",
"\n",
"In this notebook you will learn how to:\n",
"1. Create an `Experiment` in an existing `Workspace`.\n",
"2. Configure AutoML using `AutoMLConfig`.\n",
"3. Train the model on a whilelisted models using local compute. \n",
"4. Explore the results.\n",
"5. Test the best fitted model."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"import os\n",
"import random\n",
"\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib.pyplot import imshow\n",
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn import datasets\n",
"\n",
"import azureml.core\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.train.automl import AutoMLConfig\n",
"from azureml.train.automl.run import AutoMLRun"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# Choose a name for the experiment and specify the project folder.\n",
"experiment_name = 'automl-local-whitelist'\n",
"project_folder = './sample_projects/automl-local-whitelist'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
"output = {}\n",
"output['SDK version'] = azureml.core.VERSION\n",
"output['Subscription ID'] = ws.subscription_id\n",
"output['Workspace Name'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.DataFrame(data = output, index = ['']).T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Opt-in diagnostics for better experience, quality, and security of future releases."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.telemetry import set_diagnostics_collection\n",
"set_diagnostics_collection(send_diagnostics = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data\n",
"\n",
"This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import datasets\n",
"\n",
"digits = datasets.load_digits()\n",
"\n",
"# Exclude the first 100 rows from training so that they can be used for test.\n",
"X_train = digits.data[100:,:]\n",
"y_train = digits.target[100:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train\n",
"\n",
"Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
"\n",
"|Property|Description|\n",
"|-|-|\n",
"|**task**|classification or regression|\n",
"|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
"|**n_cross_validations**|Number of cross validation splits.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
"|**whitelist_models**|List of models that AutoML should use. The possible values are listed [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#configure-your-experiment-settings).|"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_config = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
" primary_metric = 'AUC_weighted',\n",
" iteration_timeout_minutes = 60,\n",
" iterations = 10,\n",
" n_cross_validations = 3,\n",
" verbosity = logging.INFO,\n",
" X = X_train, \n",
" y = y_train,\n",
" enable_tf=True,\n",
" whitelist_models=[\"TensorFlowLinearClassifier\", \"TensorFlowDNN\"],\n",
" path = project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
"In this example, we specify `show_output = True` to print currently running iterations to the console."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"local_run = experiment.submit(automl_config, show_output = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"local_run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Widget for Monitoring Runs\n",
"\n",
"The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
"\n",
"**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.widgets import RunDetails\n",
"RunDetails(local_run).show() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"#### Retrieve All Child Runs\n",
"You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"children = list(local_run.get_children())\n",
"metricslist = {}\n",
"for run in children:\n",
" properties = run.get_properties()\n",
" metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
" metricslist[int(properties['iteration'])] = metrics\n",
"\n",
"rundata = pd.DataFrame(metricslist).sort_index(1)\n",
"rundata"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Retrieve the Best Model\n",
"\n",
"Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"best_run, fitted_model = local_run.get_output()\n",
"print(best_run)\n",
"print(fitted_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Best Model Based on Any Other Metric\n",
"Show the run and the model that has the smallest `log_loss` value:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lookup_metric = \"log_loss\"\n",
"best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
"print(best_run)\n",
"print(fitted_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Model from a Specific Iteration\n",
"Show the run and the model from the third iteration:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iteration = 3\n",
"third_run, third_model = local_run.get_output(iteration = iteration)\n",
"print(third_run)\n",
"print(third_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test\n",
"\n",
"#### Load Test Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"digits = datasets.load_digits()\n",
"X_test = digits.data[:10, :]\n",
"y_test = digits.target[:10]\n",
"images = digits.images[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Testing Our Best Fitted Model\n",
"We will try to predict 2 digits and see how our model works."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Randomly select digits and test.\n",
"for index in np.random.choice(len(y_test), 2, replace = False):\n",
" print(index)\n",
" predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
" label = y_test[index]\n",
" title = \"Label value = %d Predicted value = %d \" % (label, predicted)\n",
" fig = plt.figure(1, figsize = (3,3))\n",
" ax1 = fig.add_axes((0,0,.8,.8))\n",
" ax1.set_title(title)\n",
" plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
" plt.show()"
]
}
],
"metadata": {
"authors": [
{
"name": "savitam"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"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": [
"# Automated Machine Learning\n",
"_**Energy Demand Forecasting**_\n",
"\n",
"## Contents\n",
"1. [Introduction](#Introduction)\n",
"1. [Setup](#Setup)\n",
"1. [Data](#Data)\n",
"1. [Train](#Train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"In this example, we show how AutoML can be used for energy demand forecasting.\n",
"\n",
"Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
"\n",
"In this notebook you would see\n",
"1. Creating an Experiment in an existing Workspace\n",
"2. Instantiating AutoMLConfig with new task type \"forecasting\" for timeseries data training, and other timeseries related settings: for this dataset we use the basic one: \"time_column_name\" \n",
"3. Training the Model using local compute\n",
"4. Exploring the results\n",
"5. Testing the fitted model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.core\n",
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import logging\n",
"import warnings\n",
"# Squash warning messages for cleaner output in the notebook\n",
"warnings.showwarning = lambda *args, **kwargs: None\n",
"\n",
"\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.train.automl import AutoMLConfig\n",
"from azureml.train.automl.run import AutoMLRun\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib.pyplot import imshow\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# choose a name for the run history container in the workspace\n",
"experiment_name = 'automl-energydemandforecasting'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-local-energydemandforecasting'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\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['Project Directory'] = project_folder\n",
"output['Run History Name'] = experiment_name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.DataFrame(data=output, index=['']).T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data\n",
"Read energy demanding data from file, and preview data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv(\"nyc_energy.csv\", parse_dates=['timeStamp'])\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Split the data to train and test\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train = data[data['timeStamp'] < '2017-02-01']\n",
"test = data[data['timeStamp'] >= '2017-02-01']\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare the test data, we will feed X_test to the fitted model and get prediction"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_test = test.pop('demand').values\n",
"X_test = test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Split the train data to train and valid\n",
"\n",
"Use one month's data as valid data\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train = train[train['timeStamp'] < '2017-01-01']\n",
"X_valid = train[train['timeStamp'] >= '2017-01-01']\n",
"y_train = X_train.pop('demand').values\n",
"y_valid = X_valid.pop('demand').values\n",
"print(X_train.shape)\n",
"print(y_train.shape)\n",
"print(X_valid.shape)\n",
"print(y_valid.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train\n",
"\n",
"Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
"\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",
"|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers. |\n",
"|**X_valid**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y_valid**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers. |\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"time_column_name = 'timeStamp'\n",
"automl_settings = {\n",
" \"time_column_name\": time_column_name,\n",
"}\n",
"\n",
"\n",
"automl_config = AutoMLConfig(task = 'forecasting',\n",
" debug_log = 'automl_nyc_energy_errors.log',\n",
" primary_metric='normalized_root_mean_squared_error',\n",
" iterations = 10,\n",
" iteration_timeout_minutes = 5,\n",
" X = X_train,\n",
" y = y_train,\n",
" X_valid = X_valid,\n",
" y_valid = y_valid,\n",
" path=project_folder,\n",
" verbosity = logging.INFO,\n",
" **automl_settings)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
"You will see the currently running iterations printing to the console."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"local_run = experiment.submit(automl_config, show_output=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"local_run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Retrieve the Best Model\n",
"Below we select the best pipeline from our iterations. The get_output method on automl_classifier returns the best run and the fitted model for the last fit invocation. There are overloads on get_output that allow you to retrieve the best run and fitted model for any logged metric or a particular iteration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"best_run, fitted_model = local_run.get_output()\n",
"fitted_model.steps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test the Best Fitted Model\n",
"\n",
"Predict on training and test set, and calculate residual values."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred = fitted_model.predict(X_test)\n",
"y_pred"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Define a Check Data Function\n",
"\n",
"Remove the nan values from y_test to avoid error when calculate metrics "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def _check_calc_input(y_true, y_pred, rm_na=True):\n",
" \"\"\"\n",
" Check that 'y_true' and 'y_pred' are non-empty and\n",
" have equal length.\n",
"\n",
" :param y_true: Vector of actual values\n",
" :type y_true: array-like\n",
"\n",
" :param y_pred: Vector of predicted values\n",
" :type y_pred: array-like\n",
"\n",
" :param rm_na:\n",
" If rm_na=True, remove entries where y_true=NA and y_pred=NA.\n",
" :type rm_na: boolean\n",
"\n",
" :return:\n",
" Tuple (y_true, y_pred). if rm_na=True,\n",
" the returned vectors may differ from their input values.\n",
" :rtype: Tuple with 2 entries\n",
" \"\"\"\n",
" if len(y_true) != len(y_pred):\n",
" raise ValueError(\n",
" 'the true values and prediction values do not have equal length.')\n",
" elif len(y_true) == 0:\n",
" raise ValueError(\n",
" 'y_true and y_pred are empty.')\n",
" # if there is any non-numeric element in the y_true or y_pred,\n",
" # the ValueError exception will be thrown.\n",
" y_true = np.array(y_true).astype(float)\n",
" y_pred = np.array(y_pred).astype(float)\n",
" if rm_na:\n",
" # remove entries both in y_true and y_pred where at least\n",
" # one element in y_true or y_pred is missing\n",
" y_true_rm_na = y_true[~(np.isnan(y_true) | np.isnan(y_pred))]\n",
" y_pred_rm_na = y_pred[~(np.isnan(y_true) | np.isnan(y_pred))]\n",
" return (y_true_rm_na, y_pred_rm_na)\n",
" else:\n",
" return y_true, y_pred"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Use the Check Data Function to remove the nan values from y_test to avoid error when calculate metrics "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_test,y_pred = _check_calc_input(y_test,y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculate metrics for the prediction\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
"# Explained variance score: 1 is perfect prediction\n",
"print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
"print('R2 score: %.2f' % r2_score(y_test, y_pred))\n",
"\n",
"\n",
"\n",
"# Plot outputs\n",
"%matplotlib notebook\n",
"test_pred = plt.scatter(y_test, y_pred, color='b')\n",
"test_test = plt.scatter(y_test, y_test, color='g')\n",
"plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
"plt.show()"
]
}
],
"metadata": {
"authors": [
{
"name": "xiaga"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"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": [
"# Automated Machine Learning\n",
"_**Orange Juice Sales Forecasting**_\n",
"\n",
"## Contents\n",
"1. [Introduction](#Introduction)\n",
"1. [Setup](#Setup)\n",
"1. [Data](#Data)\n",
"1. [Train](#Train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"In this example, we use AutoML to find and tune a time-series forecasting model.\n",
"\n",
"Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n",
"\n",
"In this notebook, you will:\n",
"1. Create an Experiment in an existing Workspace\n",
"2. Instantiate an AutoMLConfig \n",
"3. Find and train a forecasting model using local compute\n",
"4. Evaluate the performance of the model\n",
"\n",
"The examples in the follow code samples use the [University of Chicago's Dominick's Finer Foods dataset](https://research.chicagobooth.edu/kilts/marketing-databases/dominicks) to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment is a named object in a Workspace which represents a predictive task, the output of which is a trained model and a set of evaluation metrics for the model. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.core\n",
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import logging\n",
"import warnings\n",
"# Squash warning messages for cleaner output in the notebook\n",
"warnings.showwarning = lambda *args, **kwargs: None\n",
"\n",
"\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.train.automl import AutoMLConfig\n",
"from azureml.train.automl.run import AutoMLRun\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# choose a name for the run history container in the workspace\n",
"experiment_name = 'automl-ojsalesforecasting'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-local-ojsalesforecasting'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\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['Project Directory'] = project_folder\n",
"output['Run History Name'] = experiment_name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.DataFrame(data=output, index=['']).T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data\n",
"You are now ready to load the historical orange juice sales data. We will load the CSV file into a plain pandas DataFrame; the time column in the CSV is called _WeekStarting_, so it will be specially parsed into the datetime type."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"time_column_name = 'WeekStarting'\n",
"data = pd.read_csv(\"dominicks_OJ.csv\", parse_dates=[time_column_name])\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred. \n",
"\n",
"The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we thus define the **grain** - the columns whose values determine the boundaries between time-series: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"grain_column_names = ['Store', 'Brand']\n",
"nseries = data.groupby(grain_column_names).ngroups\n",
"print('Data contains {0} individual time-series.'.format(nseries))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data Splitting\n",
"For the purposes of demonstration and later forecast evaluation, we now split the data into a training and a testing set. The test set will contain the final 20 weeks of observed sales for each time-series."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ntest_periods = 20\n",
"\n",
"def split_last_n_by_grain(df, n):\n",
" \"\"\"\n",
" Group df by grain and split on last n rows for each group\n",
" \"\"\"\n",
" df_grouped = (df.sort_values(time_column_name) # Sort by ascending time\n",
" .groupby(grain_column_names, group_keys=False))\n",
" df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])\n",
" df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])\n",
" return df_head, df_tail\n",
"\n",
"X_train, X_test = split_last_n_by_grain(data, ntest_periods)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Modeling\n",
"\n",
"For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n",
"* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n",
"* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n",
"* Create grain-based features to enable fixed effects across different series\n",
"* Create time-based features to assist in learning seasonal patterns\n",
"* Encode categorical variables to numeric quantities\n",
"\n",
"AutoML will currently train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series.\n",
"\n",
"You are almost ready to start an AutoML training job. We will first need to create a validation set from the existing training set (i.e. for hyper-parameter tuning): "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nvalidation_periods = 20\n",
"X_train, X_validate = split_last_n_by_grain(X_train, nvalidation_periods)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We also need to separate the target column from the rest of the DataFrame: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"target_column_name = 'Quantity'\n",
"y_train = X_train.pop(target_column_name).values\n",
"y_validate = X_validate.pop(target_column_name).values "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train\n",
"\n",
"The AutoMLConfig object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, and the training and validation data. \n",
"\n",
"For forecasting tasks, there are some additional parameters that can be set: the name of the column holding the date/time and the grain column names. A time column is required for forecasting, while the grain is optional. If a grain is not given, the forecaster assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak. \n",
"\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",
"|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
"|**X**|Training matrix of features, shape = [n_training_samples, n_features]|\n",
"|**y**|Target values, shape = [n_training_samples, ]|\n",
"|**X_valid**|Validation matrix of features, shape = [n_validation_samples, n_features]|\n",
"|**y_valid**|Target values for validation, shape = [n_validation_samples, ]\n",
"|**enable_ensembling**|Allow AutoML to create ensembles of the best performing models\n",
"|**debug_log**|Log file path for writing debugging information\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_settings = {\n",
" 'time_column_name': time_column_name,\n",
" 'grain_column_names': grain_column_names,\n",
" 'drop_column_names': ['logQuantity']\n",
"}\n",
"\n",
"automl_config = AutoMLConfig(task='forecasting',\n",
" debug_log='automl_oj_sales_errors.log',\n",
" primary_metric='normalized_root_mean_squared_error',\n",
" iterations=10,\n",
" X=X_train,\n",
" y=y_train,\n",
" X_valid=X_validate,\n",
" y_valid=y_validate,\n",
" enable_ensembling=False,\n",
" path=project_folder,\n",
" verbosity=logging.INFO,\n",
" **automl_settings)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can now submit a new training run. For local runs, the execution is synchronous. Depending on the data and number of iterations this operation may take several minutes.\n",
"Information from each iteration will be printed to the console."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"local_run = experiment.submit(automl_config, show_output=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"local_run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Retrieve the Best Model\n",
"Each run within an Experiment stores serialized (i.e. pickled) pipelines from the AutoML iterations. We can now retrieve the pipeline with the best performance on the validation dataset:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"best_run, fitted_pipeline = local_run.get_output()\n",
"fitted_pipeline.steps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Make Predictions from the Best Fitted Model\n",
"Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_test = X_test.pop(target_column_name).values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_test.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data. \n",
"\n",
"The target predictions can be retrieved by calling the `predict` method on the best model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred = fitted_pipeline.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculate evaluation metrics for the prediction\n",
"To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def MAPE(actual, pred):\n",
" \"\"\"\n",
" Calculate mean absolute percentage error.\n",
" Remove NA and values where actual is close to zero\n",
" \"\"\"\n",
" not_na = ~(np.isnan(actual) | np.isnan(pred))\n",
" not_zero = ~np.isclose(actual, 0.0)\n",
" actual_safe = actual[not_na & not_zero]\n",
" pred_safe = pred[not_na & not_zero]\n",
" APE = 100*np.abs((actual_safe - pred_safe)/actual_safe)\n",
" return np.mean(APE)\n",
"\n",
"print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
"print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
"print('MAPE: %.2f' % MAPE(y_test, y_pred))"
]
}
],
"metadata": {
"authors": [
{
"name": "erwright"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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Azure Databricks is a managed Spark offering on Azure and customers already use it for advanced analytics. It provides a collaborative Notebook based environment with CPU or GPU based compute cluster.
In this section, you will see sample notebooks on how to use Azure Machine Learning SDK with Azure Databricks. You can train a model using Spark MLlib and then deploy the model to ACI/AKS from within Azure Databricks. You can also use Automated ML capability (**public preview**) of Azure ML SDK with Azure Databricks.
- Customers who use Azure Databricks for advanced analytics can now use the same cluster to run experiments with or without automated machine learning.
- You can keep the data within the same cluster.
- You can leverage the local worker nodes with autoscale and auto termination capabilities.
- You can use multiple cores of your Azure Databricks cluster to perform simultenous training.
- You can further tune the model generated by automated machine learning if you chose to.
- Every run (including the best run) is available as a pipeline.
- The model trained using Azure Databricks can be registered in Azure ML SDK workspace and then deployed to Azure managed compute (ACI or AKS) using the Azure Machine learning SDK.
**Create Azure Databricks Cluster:**
Select New Cluster and fill in following detail:
- Cluster name: _yourclustername_
- Databricks Runtime: Any 4.x runtime.
- Python version: **3**
- Workers: 2 or higher.
These settings are only for using Automated Machine Learning on Databricks.
- Max. number of **concurrent iterations** in Automated ML settings is **<=** to the number of **worker nodes** in your Databricks cluster.
- Worker node VM types: **Memory optimized VM** preferred.
- Uncheck _Enable Autoscaling_
It will take few minutes to create the cluster. Please ensure that the cluster state is running before proceeding further.
**Install Azure ML SDK without Automated ML capability on your Azure Databricks cluster**
- Select Import library
- Source: Upload Python Egg or PyPI
- PyPi Name: **azureml-sdk[databricks]**
**Install Azure ML with Automated ML SDK on your Azure Databricks cluster**
- Select Import library
- Source: Upload Python Egg or PyPI
- PyPi Name: **azureml-sdk[automl_databricks]**
**For installation with or without Automated ML**
- Click Install Library
- Do not select _Attach automatically to all clusters_. In case you have selected earlier then you can go to your Home folder and deselect it.
- Select the check box _Attach_ next to your cluster name
(More details on how to attach and detach libs are here - [https://docs.databricks.com/user-guide/libraries.html#attach-a-library-to-a-cluster](https://docs.databricks.com/user-guide/libraries.html#attach-a-library-to-a-cluster) )
- Ensure that there are no errors until Status changes to _Attached_. It may take a couple of minutes.
**Note** - If you have the old build the please deselect it from cluster’s installed libs > move to trash. Install the new build and restart the cluster. And if still there is an issue then detach and reattach your cluster.
iPython Notebooks 1-4 have to be run sequentially after making changes based on your subscription. The corresponding DBC archive contains all the notebooks and can be imported into your Databricks workspace. You can the run notebooks after importing [databricks_amlsdk](Databricks_AMLSDK_1-4_6.dbc) instead of downloading individually.
Notebooks 1-4 are related to Income prediction experiment based on this [dataset](https://archive.ics.uci.edu/ml/datasets/adult) and demonstrate how to data prep, train and operationalize a Spark ML model with Azure ML Python SDK from within Azure Databricks. Notebook 6 is an Automated ML sample notebook.
For details on SDK concepts, please refer to [notebooks](https://github.com/Azure/MachineLearningNotebooks).
Learn more about [how to use Azure Databricks as a development environment](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment#azure-databricks) for Azure Machine Learning service.
You can also use Azure Databricks as a compute target for [training models with an Azure Machine Learning pipeline](https://docs.microsoft.com/machine-learning/service/how-to-set-up-training-targets#databricks).
**Please let us know your feedback.**

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
"\n",
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![04ACI](files/tables/image2.JPG)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Model Building"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pprint\n",
"import numpy as np\n",
"\n",
"from pyspark.ml import Pipeline, PipelineModel\n",
"from pyspark.ml.feature import OneHotEncoder, StringIndexer, VectorAssembler\n",
"from pyspark.ml.classification import LogisticRegression\n",
"from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
"from pyspark.ml.tuning import CrossValidator, ParamGridBuilder"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.core\n",
"\n",
"# Check core SDK version number\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"# import auth creds from notebook parameters\n",
"tenant = dbutils.widgets.get('tenant_id')\n",
"username = dbutils.widgets.get('service_principal_id')\n",
"password = dbutils.widgets.get('service_principal_password')\n",
"\n",
"auth = azureml.core.authentication.ServicePrincipalAuthentication(tenant, username, password)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# import the Workspace class and check the azureml SDK version\n",
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config(auth = auth)\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')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"## import the Workspace class and check the azureml SDK version\n",
"#from azureml.core import Workspace\n",
"#\n",
"#ws = Workspace.from_config()\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')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#get the train and test datasets\n",
"train_data_path = \"AdultCensusIncomeTrain\"\n",
"test_data_path = \"AdultCensusIncomeTest\"\n",
"\n",
"train = spark.read.parquet(train_data_path)\n",
"test = spark.read.parquet(test_data_path)\n",
"\n",
"print(\"train: ({}, {})\".format(train.count(), len(train.columns)))\n",
"print(\"test: ({}, {})\".format(test.count(), len(test.columns)))\n",
"\n",
"train.printSchema()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Define Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"label = \"income\"\n",
"dtypes = dict(train.dtypes)\n",
"dtypes.pop(label)\n",
"\n",
"si_xvars = []\n",
"ohe_xvars = []\n",
"featureCols = []\n",
"for idx,key in enumerate(dtypes):\n",
" if dtypes[key] == \"string\":\n",
" featureCol = \"-\".join([key, \"encoded\"])\n",
" featureCols.append(featureCol)\n",
" \n",
" tmpCol = \"-\".join([key, \"tmp\"])\n",
" # string-index and one-hot encode the string column\n",
" #https://spark.apache.org/docs/2.3.0/api/java/org/apache/spark/ml/feature/StringIndexer.html\n",
" #handleInvalid: Param for how to handle invalid data (unseen labels or NULL values). \n",
" #Options are 'skip' (filter out rows with invalid data), 'error' (throw an error), \n",
" #or 'keep' (put invalid data in a special additional bucket, at index numLabels). Default: \"error\"\n",
" si_xvars.append(StringIndexer(inputCol=key, outputCol=tmpCol, handleInvalid=\"skip\"))\n",
" ohe_xvars.append(OneHotEncoder(inputCol=tmpCol, outputCol=featureCol))\n",
" else:\n",
" featureCols.append(key)\n",
"\n",
"# string-index the label column into a column named \"label\"\n",
"si_label = StringIndexer(inputCol=label, outputCol='label')\n",
"\n",
"# assemble the encoded feature columns in to a column named \"features\"\n",
"assembler = VectorAssembler(inputCols=featureCols, outputCol=\"features\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.run import Run\n",
"from azureml.core.experiment import Experiment\n",
"import numpy as np\n",
"import os\n",
"import shutil\n",
"\n",
"model_name = \"AdultCensus_runHistory.mml\"\n",
"model_dbfs = os.path.join(\"/dbfs\", model_name)\n",
"run_history_name = 'spark-ml-notebook'\n",
"\n",
"# start a training run by defining an experiment\n",
"myexperiment = Experiment(ws, \"Ignite_AI_Talk\")\n",
"root_run = myexperiment.start_logging()\n",
"\n",
"# Regularization Rates - \n",
"regs = [0.0001, 0.001, 0.01, 0.1]\n",
" \n",
"# try a bunch of regularization rate in a Logistic Regression model\n",
"for reg in regs:\n",
" print(\"Regularization rate: {}\".format(reg))\n",
" # create a bunch of child runs\n",
" with root_run.child_run(\"reg-\" + str(reg)) as run:\n",
" # create a new Logistic Regression model.\n",
" lr = LogisticRegression(regParam=reg)\n",
" \n",
" # put together the pipeline\n",
" pipe = Pipeline(stages=[*si_xvars, *ohe_xvars, si_label, assembler, lr])\n",
"\n",
" # train the model\n",
" model_p = pipe.fit(train)\n",
" \n",
" # make prediction\n",
" pred = model_p.transform(test)\n",
" \n",
" # evaluate. note only 2 metrics are supported out of the box by Spark ML.\n",
" bce = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction')\n",
" au_roc = bce.setMetricName('areaUnderROC').evaluate(pred)\n",
" au_prc = bce.setMetricName('areaUnderPR').evaluate(pred)\n",
"\n",
" print(\"Area under ROC: {}\".format(au_roc))\n",
" print(\"Area Under PR: {}\".format(au_prc))\n",
" \n",
" # log reg, au_roc, au_prc and feature names in run history\n",
" run.log(\"reg\", reg)\n",
" run.log(\"au_roc\", au_roc)\n",
" run.log(\"au_prc\", au_prc)\n",
" run.log_list(\"columns\", train.columns)\n",
"\n",
" # save model\n",
" model_p.write().overwrite().save(model_name)\n",
" \n",
" # upload the serialized model into run history record\n",
" mdl, ext = model_name.split(\".\")\n",
" model_zip = mdl + \".zip\"\n",
" shutil.make_archive(mdl, 'zip', model_dbfs)\n",
" run.upload_file(\"outputs/\" + model_name, model_zip) \n",
" #run.upload_file(\"outputs/\" + model_name, path_or_stream = model_dbfs) #cannot deal with folders\n",
"\n",
" # now delete the serialized model from local folder since it is already uploaded to run history \n",
" shutil.rmtree(model_dbfs)\n",
" os.remove(model_zip)\n",
" \n",
"# Declare run completed\n",
"root_run.complete()\n",
"root_run_id = root_run.id\n",
"print (\"run id:\", root_run.id)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"metrics = root_run.get_metrics(recursive=True)\n",
"best_run_id = max(metrics, key = lambda k: metrics[k]['au_roc'])\n",
"print(best_run_id, metrics[best_run_id]['au_roc'], metrics[best_run_id]['reg'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Get the best run\n",
"child_runs = {}\n",
"\n",
"for r in root_run.get_children():\n",
" child_runs[r.id] = r\n",
" \n",
"best_run = child_runs[best_run_id]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Download the model from the best run to a local folder\n",
"best_model_file_name = \"best_model.zip\"\n",
"best_run.download_file(name = 'outputs/' + model_name, output_file_path = best_model_file_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Model Evaluation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##unzip the model to dbfs (as load() seems to require that) and load it.\n",
"if os.path.isfile(model_dbfs) or os.path.isdir(model_dbfs):\n",
" shutil.rmtree(model_dbfs)\n",
"shutil.unpack_archive(best_model_file_name, model_dbfs)\n",
"\n",
"model_p_best = PipelineModel.load(model_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# make prediction\n",
"pred = model_p_best.transform(test)\n",
"output = pred[['hours_per_week','age','workclass','marital_status','income','prediction']]\n",
"display(output.limit(5))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# evaluate. note only 2 metrics are supported out of the box by Spark ML.\n",
"bce = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction')\n",
"au_roc = bce.setMetricName('areaUnderROC').evaluate(pred)\n",
"au_prc = bce.setMetricName('areaUnderPR').evaluate(pred)\n",
"\n",
"print(\"Area under ROC: {}\".format(au_roc))\n",
"print(\"Area Under PR: {}\".format(au_prc))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Model Persistence"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##NOTE: by default the model is saved to and loaded from /dbfs/ instead of cwd!\n",
"model_p_best.write().overwrite().save(model_name)\n",
"print(\"saved model to {}\".format(model_dbfs))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%sh\n",
"\n",
"ls -la /dbfs/AdultCensus_runHistory.mml/*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dbutils.notebook.exit(\"success\")"
]
}
],
"metadata": {
"authors": [
{
"name": "pasha"
},
{
"name": "wamartin"
}
],
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.0"
},
"name": "03.Build_model_runHistory",
"notebookId": 3836944406456339
},
"nbformat": 4,
"nbformat_minor": 1
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
"\n",
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Please ensure you have run all previous notebooks in sequence before running this.\n",
"\n",
"Please Register Azure Container Instance(ACI) using Azure Portal: https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-manager-supported-services#portal in your subscription before using the SDK to deploy your ML model to ACI."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![04ACI](files/tables/image3.JPG)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.core\n",
"\n",
"# Check core SDK version number\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"# import auth creds from notebook parameters\n",
"tenant = dbutils.widgets.get('tenant_id')\n",
"username = dbutils.widgets.get('service_principal_id')\n",
"password = dbutils.widgets.get('service_principal_password')\n",
"\n",
"auth = azureml.core.authentication.ServicePrincipalAuthentication(tenant, username, password)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"\n",
"#'''\n",
"ws = Workspace.from_config(auth = auth)\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",
"#'''"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"#from azureml.core import Workspace\n",
"#import azureml.core\n",
"#\n",
"## Check core SDK version number\n",
"#print(\"SDK version:\", azureml.core.VERSION)\n",
"#\n",
"##'''\n",
"#ws = Workspace.from_config()\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",
"##'''"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##NOTE: service deployment always gets the model from the current working dir.\n",
"import os\n",
"\n",
"model_name = \"AdultCensus_runHistory.mml\" # \n",
"model_name_dbfs = os.path.join(\"/dbfs\", model_name)\n",
"\n",
"print(\"copy model from dbfs to local\")\n",
"model_local = \"file:\" + os.getcwd() + \"/\" + model_name\n",
"dbutils.fs.cp(model_name, model_local, True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Register the model\n",
"from azureml.core.model import Model\n",
"mymodel = Model.register(model_path = model_name, # this points to a local file\n",
" model_name = model_name, # this is the name the model is registered as, am using same name for both path and name. \n",
" description = \"ADB trained model by Parashar\",\n",
" workspace = ws)\n",
"\n",
"print(mymodel.name, mymodel.description, mymodel.version)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#%%writefile score_sparkml.py\n",
"score_sparkml = \"\"\"\n",
" \n",
"import json\n",
" \n",
"def init():\n",
" # One-time initialization of PySpark and predictive model\n",
" import pyspark\n",
" from azureml.core.model import Model\n",
" from pyspark.ml import PipelineModel\n",
" \n",
" global trainedModel\n",
" global spark\n",
" \n",
" spark = pyspark.sql.SparkSession.builder.appName(\"ADB and AML notebook by Parashar\").getOrCreate()\n",
" model_name = \"{model_name}\" #interpolated\n",
" model_path = Model.get_model_path(model_name)\n",
" trainedModel = PipelineModel.load(model_path)\n",
" \n",
"def run(input_json):\n",
" if isinstance(trainedModel, Exception):\n",
" return json.dumps({{\"trainedModel\":str(trainedModel)}})\n",
" \n",
" try:\n",
" sc = spark.sparkContext\n",
" input_list = json.loads(input_json)\n",
" input_rdd = sc.parallelize(input_list)\n",
" input_df = spark.read.json(input_rdd)\n",
" \n",
" # Compute prediction\n",
" prediction = trainedModel.transform(input_df)\n",
" #result = prediction.first().prediction\n",
" predictions = prediction.collect()\n",
" \n",
" #Get each scored result\n",
" preds = [str(x['prediction']) for x in predictions]\n",
" result = \",\".join(preds)\n",
" # you can return any data type as long as it is JSON-serializable\n",
" return result.tolist()\n",
" except Exception as e:\n",
" result = str(e)\n",
" return result\n",
" \n",
"\"\"\".format(model_name=model_name)\n",
" \n",
"exec(score_sparkml)\n",
" \n",
"with open(\"score_sparkml.py\", \"w\") as file:\n",
" file.write(score_sparkml)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"myacienv = CondaDependencies.create(conda_packages=['scikit-learn','numpy','pandas']) #showing how to add libs as an eg. - not needed for this model.\n",
"\n",
"with open(\"mydeployenv.yml\",\"w\") as f:\n",
" f.write(myacienv.serialize_to_string())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#deploy to ACI\n",
"from azureml.core.webservice import AciWebservice, Webservice\n",
"\n",
"myaci_config = AciWebservice.deploy_configuration(\n",
" cpu_cores = 2, \n",
" memory_gb = 2, \n",
" tags = {'name':'Databricks Azure ML ACI'}, \n",
" description = 'This is for ADB and AML example. Azure Databricks & Azure ML SDK demo with ACI by Parashar.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# this will take 10-15 minutes to finish\n",
"\n",
"service_name = \"aciws\"\n",
"runtime = \"spark-py\" \n",
"driver_file = \"score_sparkml.py\"\n",
"my_conda_file = \"mydeployenv.yml\"\n",
"\n",
"# image creation\n",
"from azureml.core.image import ContainerImage\n",
"myimage_config = ContainerImage.image_configuration(execution_script = driver_file, \n",
" runtime = runtime, \n",
" conda_file = my_conda_file)\n",
"\n",
"# Webservice creation\n",
"myservice = Webservice.deploy_from_model(\n",
" workspace=ws, \n",
" name=service_name,\n",
" deployment_config = myaci_config,\n",
" models = [mymodel],\n",
" image_config = myimage_config\n",
" )\n",
"\n",
"myservice.wait_for_deployment(show_output=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"help(Webservice)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# List images by ws\n",
"\n",
"for i in ContainerImage.list(workspace = ws):\n",
" print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#for using the Web HTTP API \n",
"print(myservice.scoring_uri)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"#get the some sample data\n",
"test_data_path = \"AdultCensusIncomeTest\"\n",
"test = spark.read.parquet(test_data_path).limit(5)\n",
"\n",
"test_json = json.dumps(test.toJSON().collect())\n",
"\n",
"print(test_json)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#using data defined above predict if income is >50K (1) or <=50K (0)\n",
"myservice.run(input_data=test_json)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#comment to not delete the web service\n",
"#myservice.delete()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"authors": [
{
"name": "pasha"
},
{
"name": "wamartin"
}
],
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.0"
},
"name": "04.DeploytoACI",
"notebookId": 3836944406456376
},
"nbformat": 4,
"nbformat_minor": 1
}

182
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
"\n",
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![04ACI](files/tables/image1.JPG)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Data Ingestion"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import urllib"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Download AdultCensusIncome.csv from Azure CDN. This file has 32,561 rows.\n",
"basedataurl = \"https://amldockerdatasets.azureedge.net\"\n",
"datafile = \"AdultCensusIncome.csv\"\n",
"datafile_dbfs = os.path.join(\"/dbfs\", datafile)\n",
"\n",
"if os.path.isfile(datafile_dbfs):\n",
" print(\"found {} at {}\".format(datafile, datafile_dbfs))\n",
"else:\n",
" print(\"downloading {} to {}\".format(datafile, datafile_dbfs))\n",
" urllib.request.urlretrieve(os.path.join(basedataurl, datafile), datafile_dbfs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create a Spark dataframe out of the csv file.\n",
"data_all = sqlContext.read.format('csv').options(header='true', inferSchema='true', ignoreLeadingWhiteSpace='true', ignoreTrailingWhiteSpace='true').load(datafile)\n",
"print(\"({}, {})\".format(data_all.count(), len(data_all.columns)))\n",
"data_all.printSchema()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#renaming columns\n",
"columns_new = [col.replace(\"-\", \"_\") for col in data_all.columns]\n",
"data_all = data_all.toDF(*columns_new)\n",
"data_all.printSchema()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"display(data_all.limit(5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Data Preparation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Choose feature columns and the label column.\n",
"label = \"income\"\n",
"xvars = set(data_all.columns) - {label}\n",
"\n",
"print(\"label = {}\".format(label))\n",
"print(\"features = {}\".format(xvars))\n",
"\n",
"data = data_all.select([*xvars, label])\n",
"\n",
"# Split data into train and test.\n",
"train, test = data.randomSplit([0.75, 0.25], seed=123)\n",
"\n",
"print(\"train ({}, {})\".format(train.count(), len(train.columns)))\n",
"print(\"test ({}, {})\".format(test.count(), len(test.columns)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Data Persistence"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Write the train and test data sets to intermediate storage\n",
"train_data_path = \"AdultCensusIncomeTrain\"\n",
"test_data_path = \"AdultCensusIncomeTest\"\n",
"\n",
"train_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTrain\")\n",
"test_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTest\")\n",
"\n",
"train.write.mode('overwrite').parquet(train_data_path)\n",
"test.write.mode('overwrite').parquet(test_data_path)\n",
"print(\"train and test datasets saved to {} and {}\".format(train_data_path_dbfs, test_data_path_dbfs))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"authors": [
{
"name": "pasha"
},
{
"name": "wamartin"
}
],
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.0"
},
"name": "02.Ingest_data",
"notebookId": 3836944406456362
},
"nbformat": 4,
"nbformat_minor": 1
}

264
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
"\n",
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
"\n",
"**install azureml-sdk**\n",
"* Source: Upload Python Egg or PyPi\n",
"* PyPi Name: `azureml-sdk[databricks]`\n",
"* Select Install Library"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.core\n",
"\n",
"# Check core SDK version number - based on build number of preview/master.\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![04ACI](files/tables/image2b.JPG)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Please specify the Azure subscription Id, resource group name, workspace name, and the region in which you want to create the Azure Machine Learning Workspace.\n",
"\n",
"You can get the value of your Azure subscription ID from the Azure Portal, and then selecting Subscriptions from the menu on the left.\n",
"\n",
"For the resource_group, use the name of the resource group that contains your Azure Databricks Workspace.\n",
"\n",
"NOTE: If you provide a resource group name that does not exist, the resource group will be automatically created. This may or may not succeed in your environment, depending on the permissions you have on your Azure Subscription."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# subscription_id = \"<your-subscription-id>\"\n",
"# resource_group = \"<your-existing-resource-group>\"\n",
"# workspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\n",
"# workspace_region = \"<your-resource group-region>\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"# import auth creds from notebook parameters\n",
"tenant = dbutils.widgets.get('tenant_id')\n",
"username = dbutils.widgets.get('service_principal_id')\n",
"password = dbutils.widgets.get('service_principal_password')\n",
"\n",
"auth = azureml.core.authentication.ServicePrincipalAuthentication(tenant, username, password)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"subscription_id = dbutils.widgets.get('subscription_id')\n",
"resource_group = dbutils.widgets.get('resource_group')\n",
"workspace_name = dbutils.widgets.get('workspace_name')\n",
"workspace_region = dbutils.widgets.get('workspace_region')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"# import the Workspace class and check the azureml SDK version\n",
"# exist_ok checks if workspace exists or not.\n",
"\n",
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.create(name = workspace_name,\n",
" subscription_id = subscription_id,\n",
" resource_group = resource_group, \n",
" location = workspace_region,\n",
" auth = auth,\n",
" exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"## import the Workspace class and check the azureml SDK version\n",
"## exist_ok checks if workspace exists or not.\n",
"#\n",
"#from azureml.core import Workspace\n",
"#\n",
"#ws = Workspace.create(name = workspace_name,\n",
"# subscription_id = subscription_id,\n",
"# resource_group = resource_group, \n",
"# location = workspace_region,\n",
"# exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#get workspace details\n",
"ws.get_details()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"ws = Workspace(workspace_name = workspace_name,\n",
" subscription_id = subscription_id,\n",
" resource_group = resource_group,\n",
" auth = auth)\n",
"\n",
"# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
"ws.write_config()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"#ws = Workspace(workspace_name = workspace_name,\n",
"# subscription_id = subscription_id,\n",
"# resource_group = resource_group)\n",
"#\n",
"## persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
"#ws.write_config()\n",
"###if you need to give a different path/filename please use this\n",
"###write_config(path=\"/databricks/driver/aml_config/\",file_name=<alias_conf.cfg>)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"help(Workspace)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"# import the Workspace class and check the azureml SDK version\n",
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config(auth = auth)\n",
"#ws = Workspace.from_config(<full path>)\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')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"## import the Workspace class and check the azureml SDK version\n",
"#from azureml.core import Workspace\n",
"#\n",
"#ws = Workspace.from_config()\n",
"##ws = Workspace.from_config(<full path>)\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')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"authors": [
{
"name": "pasha"
},
{
"name": "wamartin"
}
],
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.0"
},
"name": "01.Installation_and_Configuration",
"notebookId": 3836944406456490
},
"nbformat": 4,
"nbformat_minor": 1
}

653
how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb Normal file
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{
"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": [
"We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
"\n",
"**install azureml-sdk with Automated ML**\n",
"* Source: Upload Python Egg or PyPi\n",
"* PyPi Name: `azureml-sdk[automl_databricks]`\n",
"* Select Install Library"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# AutoML : Classification with Local Compute on Azure DataBricks\n",
"\n",
"In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
"\n",
"In this notebook you will learn how to:\n",
"1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
"2. Create an `Experiment` in an existing `Workspace`.\n",
"3. Configure AutoML using `AutoMLConfig`.\n",
"4. Train the model using AzureDataBricks.\n",
"5. Explore the results.\n",
"6. Test the best fitted model.\n",
"\n",
"Prerequisites:\n",
"Before running this notebook, please follow the readme for installing necessary libraries to your cluster."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Register Machine Learning Services Resource Provider\n",
"Microsoft.MachineLearningServices only needs to be registed once in the subscription. To register it:\n",
"Start the Azure portal.\n",
"Select your All services and then Subscription.\n",
"Select the subscription that you want to use.\n",
"Click on Resource providers\n",
"Click the Register link next to Microsoft.MachineLearningServices"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check the Azure ML Core SDK Version to Validate Your Installation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.core\n",
"\n",
"print(\"SDK Version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize an Azure ML Workspace\n",
"### What is an Azure ML Workspace and Why Do I Need One?\n",
"\n",
"An Azure ML workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows. In particular, an Azure ML workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models.\n",
"\n",
"\n",
"### What do I Need?\n",
"\n",
"To create or access an Azure ML workspace, you will need to import the Azure ML library and specify following information:\n",
"* A name for your workspace. You can choose one.\n",
"* Your subscription id. Use the `id` value from the `az account show` command output above.\n",
"* The resource group name. The resource group organizes Azure resources and provides a default region for the resources in the group. The resource group will be created if it doesn't exist. Resource groups can be created and viewed in the [Azure portal](https://portal.azure.com)\n",
"* Supported regions include `eastus2`, `eastus`,`westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"#subscription_id = \"<Your SubscriptionId>\"\n",
"#resource_group = \"<Resource group - new or existing>\"\n",
"#workspace_name = \"<workspace to be created>\"\n",
"#workspace_region = \"<azureregion>\" #eg. eastus2, westcentralus, westeurope"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating a Workspace\n",
"If you already have access to an Azure ML workspace you want to use, you can skip this cell. Otherwise, this cell will create an Azure ML workspace for you in the specified subscription, provided you have the correct permissions for the given `subscription_id`.\n",
"\n",
"This will fail when:\n",
"1. The workspace already exists.\n",
"2. You do not have permission to create a workspace in the resource group.\n",
"3. You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription.\n",
"\n",
"If workspace creation fails for any reason other than already existing, please work with your IT administrator to provide you with the appropriate permissions or to provision the required resources.\n",
"\n",
"**Note:** Creation of a new workspace can take several minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"# import auth creds from notebook parameters\n",
"tenant = dbutils.widgets.get('tenant_id')\n",
"username = dbutils.widgets.get('service_principal_id')\n",
"password = dbutils.widgets.get('service_principal_password')\n",
"\n",
"auth = azureml.core.authentication.ServicePrincipalAuthentication(tenant, username, password)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"subscription_id = dbutils.widgets.get('subscription_id')\n",
"resource_group = dbutils.widgets.get('resource_group')\n",
"workspace_name = dbutils.widgets.get('workspace_name')\n",
"workspace_region = dbutils.widgets.get('workspace_region')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Import the Workspace class and check the Azure ML SDK version.\n",
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.create(name = workspace_name,\n",
" subscription_id = subscription_id,\n",
" resource_group = resource_group, \n",
" location = workspace_region,\n",
" auth = auth,\n",
" exist_ok=True)\n",
"ws.get_details()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"#from azureml.core import Workspace\n",
"#import azureml.core\n",
"#\n",
"## Check core SDK version number\n",
"#print(\"SDK version:\", azureml.core.VERSION)\n",
"#\n",
"##'''\n",
"#ws = Workspace.from_config()\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",
"##'''"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configuring Your Local Environment\n",
"You can validate that you have access to the specified workspace and write a configuration file to the default configuration location, `./aml_config/config.json`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace(workspace_name = workspace_name,\n",
" subscription_id = subscription_id,\n",
" resource_group = resource_group,\n",
" auth = auth)\n",
"\n",
"# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
"ws.write_config()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"#from azureml.core import Workspace\n",
"#\n",
"#ws = Workspace(workspace_name = workspace_name,\n",
"# subscription_id = subscription_id,\n",
"# resource_group = resource_group)\n",
"#\n",
"## Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
"#ws.write_config()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a Folder to Host Sample Projects\n",
"Finally, create a folder where all the sample projects will be hosted."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"sample_projects_folder = './sample_projects'\n",
"\n",
"if not os.path.isdir(sample_projects_folder):\n",
" os.mkdir(sample_projects_folder)\n",
" \n",
"print('Sample projects will be created in {}.'.format(sample_projects_folder))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create an Experiment\n",
"\n",
"As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"import os\n",
"import random\n",
"import time\n",
"\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib.pyplot import imshow\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import azureml.core\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.train.automl import AutoMLConfig\n",
"from azureml.train.automl.run import AutoMLRun"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"ws = Workspace.from_config(auth = auth)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"#ws = Workspace.from_config(auth = auth)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Choose a name for the experiment and specify the project folder.\n",
"experiment_name = 'automl-local-classification'\n",
"project_folder = './sample_projects/automl-local-classification'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
"output = {}\n",
"output['SDK version'] = azureml.core.VERSION\n",
"output['Subscription ID'] = ws.subscription_id\n",
"output['Workspace Name'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.DataFrame(data = output, index = ['']).T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Diagnostics\n",
"\n",
"Opt-in diagnostics for better experience, quality, and security of future releases."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.telemetry import set_diagnostics_collection\n",
"set_diagnostics_collection(send_diagnostics = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Training Data Using DataPrep"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.dataprep as dprep\n",
"# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
"# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
"simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
"X_train = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1) # Remove the header row.\n",
"\n",
"# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
"# and convert column types manually.\n",
"# Here we read a comma delimited file and convert all columns to integers.\n",
"y_train = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Review the Data Preparation Result\n",
"You can peek the result of a Dataflow at any range using skip(i) and head(j). Doing so evaluates only j records for all the steps in the Dataflow, which makes it fast even against large datasets."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train.skip(1).head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure AutoML\n",
"\n",
"Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
"\n",
"|Property|Description|\n",
"|-|-|\n",
"|**task**|classification or regression|\n",
"|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
"|**primary_metric**|This is the metric that you want to optimize. Regression 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",
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
"|**n_cross_validations**|Number of cross validation splits.|\n",
"|**spark_context**|Spark Context object. for Databricks, use spark_context=sc|\n",
"|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be <= number of worker nodes in your Azure Databricks cluster.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
"|**preprocess**|set this to True to enable pre-processing of data eg. string to numeric using one-hot encoding|\n",
"|**exit_score**|Target score for experiment. It is associated with the metric. eg. exit_score=0.995 will exit experiment after that|"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_config = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
" primary_metric = 'AUC_weighted',\n",
" iteration_timeout_minutes = 10,\n",
" iterations = 30,\n",
" n_cross_validations = 10,\n",
" max_concurrent_iterations = 2, #change it based on number of worker nodes\n",
" verbosity = logging.INFO,\n",
" spark_context=sc, #databricks/spark related\n",
" X = X_train, \n",
" y = y_train,\n",
" enable_cache=False,\n",
" path = project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Models\n",
"\n",
"Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
"In this example, we specify `show_output = True` to print currently running iterations to the console."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"local_run = experiment.submit(automl_config, show_output = True) # for higher runs please use show_output=False and use the below"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore the Results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Portal URL for Monitoring Runs\n",
"\n",
"The following will provide a link to the web interface to explore individual run details and status. In the future we might support output displayed in the notebook."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(local_run.get_portal_url())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following will show the child runs and waits for the parent run to complete."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Retrieve All Child Runs after the experiment is completed (in portal)\n",
"You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"children = list(local_run.get_children())\n",
"metricslist = {}\n",
"for run in children:\n",
" properties = run.get_properties()\n",
" metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)} \n",
" metricslist[int(properties['iteration'])] = metrics\n",
"\n",
"rundata = pd.DataFrame(metricslist).sort_index(1)\n",
"rundata"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Retrieve the Best Model after the above run is complete \n",
"\n",
"Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"best_run, fitted_model = local_run.get_output()\n",
"print(best_run)\n",
"print(fitted_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Best Model Based on Any Other Metric after the above run is complete based on the child run\n",
"Show the run and the model that has the smallest `log_loss` value:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lookup_metric = \"log_loss\"\n",
"best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
"print(best_run)\n",
"print(fitted_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test the Best Fitted Model\n",
"\n",
"#### Load Test Data - you can split the dataset beforehand & pass Train dataset to AutoML and use Test dataset to evaluate the best model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import datasets\n",
"digits = datasets.load_digits()\n",
"X_test = digits.data[:10, :]\n",
"y_test = digits.target[:10]\n",
"images = digits.images[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Testing Our Best Fitted Model\n",
"We will try to predict digits and see how our model works. This is just an example to show you."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Randomly select digits and test.\n",
"for index in np.random.choice(len(y_test), 2, replace = False):\n",
" print(index)\n",
" predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
" label = y_test[index]\n",
" title = \"Label value = %d Predicted value = %d \" % (label, predicted)\n",
" fig = plt.figure(1, figsize = (3,3))\n",
" ax1 = fig.add_axes((0,0,.8,.8))\n",
" ax1.set_title(title)\n",
" plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
" display(fig)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When deploying an automated ML trained model, please specify _pip_packages=['azureml-sdk[automl]']_ in your CondaDependencies."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"authors": [
{
"name": "savitam"
},
{
"name": "wamartin"
}
],
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.0"
},
"name": "auto-ml-classification-local-adb",
"notebookId": 3836944406456411
},
"nbformat": 4,
"nbformat_minor": 1
}

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how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb Normal file
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{
"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": [
"# Train using Azure Machine Learning Compute\n",
"\n",
"* Initialize a Workspace\n",
"* Create an Experiment\n",
"* Introduction to AmlCompute\n",
"* Submit an AmlCompute run in a few different ways\n",
" - Provision as a run based compute target \n",
" - Provision as a persistent compute target (Basic)\n",
" - Provision as a persistent compute target (Advanced)\n",
"* Additional operations to perform on AmlCompute\n",
"* Find the best model in the run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"Make sure you go through the [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) Notebook first if you haven't."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check core SDK version number\n",
"import azureml.core\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize a Workspace\n",
"\n",
"Initialize a workspace object from persisted configuration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create workspace"
]
},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create An Experiment\n",
"\n",
"**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"experiment_name = 'train-on-amlcompute'\n",
"experiment = Experiment(workspace = ws, name = experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction to AmlCompute\n",
"\n",
"Azure Machine Learning Compute is managed compute infrastructure that allows the user to easily create single to multi-node compute of the appropriate VM Family. It is created **within your workspace region** and is a resource that can be used by other users in your workspace. It autoscales by default to the max_nodes, when a job is submitted, and executes in a containerized environment packaging the dependencies as specified by the user. \n",
"\n",
"Since it is managed compute, job scheduling and cluster management are handled internally by Azure Machine Learning service. \n",
"\n",
"For more information on Azure Machine Learning Compute, please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)\n",
"\n",
"If you are an existing BatchAI customer who is migrating to Azure Machine Learning, please read [this article](https://aka.ms/batchai-retirement)\n",
"\n",
"**Note**: As with other Azure services, there are limits on certain resources (for eg. AmlCompute quota) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n",
"\n",
"\n",
"The training script `train.py` is already created for you. Let's have a look."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Submit an AmlCompute run in a few different ways\n",
"\n",
"First lets check which VM families are available in your region. Azure is a regional service and some specialized SKUs (especially GPUs) are only available in certain regions. Since AmlCompute is created in the region of your workspace, we will use the supported_vms () function to see if the VM family we want to use ('STANDARD_D2_V2') is supported.\n",
"\n",
"You can also pass a different region to check availability and then re-create your workspace in that region through the [00. Installation and Configuration](00.configuration.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import ComputeTarget, AmlCompute\n",
"\n",
"AmlCompute.supported_vmsizes(workspace = ws)\n",
"#AmlCompute.supported_vmsizes(workspace = ws, location='southcentralus')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create project directory\n",
"\n",
"Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import shutil\n",
"\n",
"project_folder = './train-on-amlcompute'\n",
"os.makedirs(project_folder, exist_ok=True)\n",
"shutil.copy('train.py', project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Provision as a run based compute target\n",
"\n",
"You can provision AmlCompute as a compute target at run-time. In this case, the compute is auto-created for your run, scales up to max_nodes that you specify, and then **deleted automatically** after the run completes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n",
"\n",
"# create a new runconfig object\n",
"run_config = RunConfiguration()\n",
"\n",
"# signal that you want to use AmlCompute to execute script.\n",
"run_config.target = \"amlcompute\"\n",
"\n",
"# AmlCompute will be created in the same region as workspace\n",
"# Set vm size for AmlCompute\n",
"run_config.amlcompute.vm_size = 'STANDARD_D2_V2'\n",
"\n",
"# enable Docker \n",
"run_config.environment.docker.enabled = True\n",
"\n",
"# set Docker base image to the default CPU-based image\n",
"run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n",
"\n",
"# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
"run_config.environment.python.user_managed_dependencies = False\n",
"\n",
"# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
"run_config.auto_prepare_environment = True\n",
"\n",
"# specify CondaDependencies obj\n",
"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
"\n",
"# Now submit a run on AmlCompute\n",
"from azureml.core.script_run_config import ScriptRunConfig\n",
"\n",
"script_run_config = ScriptRunConfig(source_directory=project_folder,\n",
" script='train.py',\n",
" run_config=run_config)\n",
"\n",
"run = experiment.submit(script_run_config)\n",
"\n",
"# Show run details\n",
"run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"# Shows output of the run on stdout.\n",
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.get_metrics()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Provision as a persistent compute target (Basic)\n",
"\n",
"You can provision a persistent AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
"\n",
"* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n",
"* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import ComputeTarget, AmlCompute\n",
"from azureml.core.compute_target import ComputeTargetException\n",
"\n",
"# Choose a name for your CPU cluster\n",
"cpu_cluster_name = \"cpucluster\"\n",
"\n",
"# Verify that cluster does not exist already\n",
"try:\n",
" cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
" print('Found existing cluster, use it.')\n",
"except ComputeTargetException:\n",
" compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
" max_nodes=4)\n",
" cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
"\n",
"cpu_cluster.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Configure & Run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"# create a new RunConfig object\n",
"run_config = RunConfiguration(framework=\"python\")\n",
"\n",
"# Set compute target to AmlCompute target created in previous step\n",
"run_config.target = cpu_cluster.name\n",
"\n",
"# enable Docker \n",
"run_config.environment.docker.enabled = True\n",
"\n",
"# specify CondaDependencies obj\n",
"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
"\n",
"from azureml.core import Run\n",
"from azureml.core import ScriptRunConfig\n",
"\n",
"src = ScriptRunConfig(source_directory=project_folder, \n",
" script='train.py', \n",
" run_config=run_config) \n",
"run = experiment.submit(config=src)\n",
"run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"# Shows output of the run on stdout.\n",
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.get_metrics()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Provision as a persistent compute target (Advanced)\n",
"\n",
"You can also specify additional properties or change defaults while provisioning AmlCompute using a more advanced configuration. This is useful when you want a dedicated cluster of 4 nodes (for example you can set the min_nodes and max_nodes to 4), or want the compute to be within an existing VNet in your subscription.\n",
"\n",
"In addition to `vm_size` and `max_nodes`, you can specify:\n",
"* `min_nodes`: Minimum nodes (default 0 nodes) to downscale to while running a job on AmlCompute\n",
"* `vm_priority`: Choose between 'dedicated' (default) and 'lowpriority' VMs when provisioning AmlCompute. Low Priority VMs use Azure's excess capacity and are thus cheaper but risk your run being pre-empted\n",
"* `idle_seconds_before_scaledown`: Idle time (default 120 seconds) to wait after run completion before auto-scaling to min_nodes\n",
"* `vnet_resourcegroup_name`: Resource group of the **existing** VNet within which AmlCompute should be provisioned\n",
"* `vnet_name`: Name of VNet\n",
"* `subnet_name`: Name of SubNet within the VNet"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import ComputeTarget, AmlCompute\n",
"from azureml.core.compute_target import ComputeTargetException\n",
"\n",
"# Choose a name for your CPU cluster\n",
"cpu_cluster_name = \"cpucluster\"\n",
"\n",
"# Verify that cluster does not exist already\n",
"try:\n",
" cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
" print('Found existing cluster, use it.')\n",
"except ComputeTargetException:\n",
" compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
" vm_priority='lowpriority',\n",
" min_nodes=2,\n",
" max_nodes=4,\n",
" idle_seconds_before_scaledown='300',\n",
" vnet_resourcegroup_name='<my-resource-group>',\n",
" vnet_name='<my-vnet-name>',\n",
" subnet_name='<my-subnet-name>')\n",
" cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
"\n",
"cpu_cluster.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Configure & Run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"# create a new RunConfig object\n",
"run_config = RunConfiguration(framework=\"python\")\n",
"\n",
"# Set compute target to AmlCompute target created in previous step\n",
"run_config.target = cpu_cluster.name\n",
"\n",
"# enable Docker \n",
"run_config.environment.docker.enabled = True\n",
"\n",
"# specify CondaDependencies obj\n",
"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
"\n",
"from azureml.core import Run\n",
"from azureml.core import ScriptRunConfig\n",
"\n",
"src = ScriptRunConfig(source_directory=project_folder, \n",
" script='train.py', \n",
" run_config=run_config) \n",
"run = experiment.submit(config=src)\n",
"run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"# Shows output of the run on stdout.\n",
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.get_metrics()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Additional operations to perform on AmlCompute\n",
"\n",
"You can perform more operations on AmlCompute such as updating the node counts or deleting the compute. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Get_status () gets the latest status of the AmlCompute target\n",
"cpu_cluster.get_status()\n",
"cpu_cluster.serialize()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Update () takes in the min_nodes, max_nodes and idle_seconds_before_scaledown and updates the AmlCompute target\n",
"#cpu_cluster.update(min_nodes=1)\n",
"#cpu_cluster.update(max_nodes=10)\n",
"cpu_cluster.update(idle_seconds_before_scaledown=300)\n",
"#cpu_cluster.update(min_nodes=2, max_nodes=4, idle_seconds_before_scaledown=600)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Delete () is used to deprovision and delete the AmlCompute target. Useful if you want to re-use the compute name \n",
"#'cpucluster' in this case but use a different VM family for instance.\n",
"\n",
"#cpu_cluster.delete()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Success!\n",
"Great, you are ready to move on to the remaining notebooks."
]
}
],
"metadata": {
"authors": [
{
"name": "nigup"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license.
from sklearn.datasets import load_diabetes
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from azureml.core.run import Run
from sklearn.externals import joblib
import os
import numpy as np
os.makedirs('./outputs', exist_ok=True)
X, y = load_diabetes(return_X_y=True)
run = Run.get_context()
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.2,
random_state=0)
data = {"train": {"X": X_train, "y": y_train},
"test": {"X": X_test, "y": y_test}}
# list of numbers from 0.0 to 1.0 with a 0.05 interval
alphas = np.arange(0.0, 1.0, 0.05)
for alpha in alphas:
# Use Ridge algorithm to create a regression model
reg = Ridge(alpha=alpha)
reg.fit(data["train"]["X"], data["train"]["y"])
preds = reg.predict(data["test"]["X"])
mse = mean_squared_error(preds, data["test"]["y"])
run.log('alpha', alpha)
run.log('mse', mse)
model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
# save model in the outputs folder so it automatically get uploaded
with open(model_file_name, "wb") as file:
joblib.dump(value=reg, filename=os.path.join('./outputs/',
model_file_name))
print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))

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## Azure Machine Learning service Tutorial
Complete these tutorials to learn how to train and deploy models using Azure Machine Learning services and Python SDK. These Notebooks accompany the [tutorial articles starting here]([https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-train-models-with-aml]).
As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
* [Tutorial #1](img-classification-part1-training.ipynb): Train an image classification model with Azure Machine Learning
* [Tutorial #2](img-classification-part2-deploy.ipynb): Deploy an image classification model from first tutorial in Azure Container Instance (ACI)
* [Tutorial #3](regression-part1-data-prep.ipynb): Use data preparation.
Also find quickstarts and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).

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