update samples from Release-62 as a part of 1.21.0 SDK stable release

CODE_OF_CONDUCT.md

@@ -1,9 +0,0 @@
-# Microsoft Open Source Code of Conduct
-
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
-
-Resources:
-
-- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
-- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
-- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns

README.md

@@ -1,43 +1,77 @@
-# Azure Machine Learning Python SDK notebooks
+# Azure Machine Learning service example notebooks
 
 > a community-driven repository of examples using mlflow for tracking can be found at https://github.com/Azure/azureml-examples
 
-Welcome to the Azure Machine Learning Python SDK notebooks repository!
+This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure.  The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.
 
-## Getting started
+![Azure ML Workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/media/concept-azure-machine-learning-architecture/workflow.png)
 
-These notebooks are recommended for use in an Azure Machine Learning [Compute Instance](https://docs.microsoft.com/azure/machine-learning/concept-compute-instance), where you can run them without any additional set up.
 
-However, the notebooks can be run in any development environment with the correct `azureml` packages installed.
+## Quick installation
+```sh
+pip install azureml-sdk
+```
+Read more detailed instructions on [how to set up your environment](./NBSETUP.md) using Azure Notebook service, your own Jupyter notebook server, or Docker.
 
-Install the `azureml.core` Python package:
+## How to navigate and use the example notebooks?
+If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, you should always run the [Configuration](./configuration.ipynb) notebook first when setting up a notebook library on a new machine or in a new environment. It configures your notebook library to connect to an Azure Machine Learning workspace, and sets up your workspace and compute to be used by many of the other examples. 
+This [index](./index.md) should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content. 
+
+If you want to...
+
+ * ...try out and explore Azure ML, start with image classification tutorials: [Part 1 (Training)](./tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb) and [Part 2 (Deployment)](./tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb).
+ * ...learn about experimentation and tracking run history: [track and monitor experiments](./how-to-use-azureml/track-and-monitor-experiments).
+ * ...train deep learning models at scale, first learn about [Machine Learning Compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and then try [distributed hyperparameter tuning](./how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) and [distributed training](./how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb).
+ * ...deploy models as a realtime scoring service, first learn the basics by [deploying to Azure Container Instance](./how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb), then learn how to [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
+ * ...deploy models as a batch scoring service: [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb) and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring).
+ * ...monitor your deployed models, learn about using [App Insights](./how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb).
+
+## Tutorials
+
+The [Tutorials](./tutorials) folder contains notebooks for the tutorials described in the [Azure Machine Learning documentation](https://aka.ms/aml-docs).
+  
+## How to use Azure ML
+
+The [How to use Azure ML](./how-to-use-azureml) folder contains specific examples demonstrating the features of the Azure Machine Learning SDK
+
+- [Training](./how-to-use-azureml/training) - Examples of how to build models using Azure ML's logging and execution capabilities on local and remote compute targets
+- [Training with ML and DL frameworks](./how-to-use-azureml/ml-frameworks) - Examples demonstrating how to build and train machine learning models at scale on Azure ML and perform hyperparameter tuning.
+- [Manage Azure ML Service](./how-to-use-azureml/manage-azureml-service) - Examples how to perform tasks, such as authenticate against Azure ML service in different ways.
+- [Automated Machine Learning](./how-to-use-azureml/automated-machine-learning) - Examples using Automated Machine Learning to automatically generate optimal machine learning pipelines and models
+- [Machine Learning Pipelines](./how-to-use-azureml/machine-learning-pipelines) - Examples showing how to create and use reusable pipelines for training and batch scoring
+- [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
+- [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
+- [Reinforcement Learning](./how-to-use-azureml/reinforcement-learning) - Examples showing how to train reinforcement learning agents
+
+---
+## Documentation
+
+ * Quickstarts, end-to-end tutorials, and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
+ * [Python SDK reference](https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py)
+ * Azure ML Data Prep SDK [overview](https://aka.ms/data-prep-sdk), [Python SDK reference](https://aka.ms/aml-data-prep-apiref), and [tutorials and how-tos](https://aka.ms/aml-data-prep-notebooks).
+
+---
+
+
+## Community Repository 
+Visit this [community repository](https://github.com/microsoft/MLOps/tree/master/examples) to find useful end-to-end sample notebooks. Also, please follow these [contribution guidelines](https://github.com/microsoft/MLOps/blob/master/contributing.md) when contributing to this repository.   
+
+## Projects using Azure Machine Learning
+
+Visit following repos to see projects contributed by Azure ML users:
+ - [Learn about Natural Language Processing best practices using Azure Machine Learning service](https://github.com/microsoft/nlp)
+ - [Pre-Train BERT models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
+ - [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)
+ - [UMass Amherst Student Samples](https://github.com/katiehouse3/microsoft-azure-ml-notebooks) - A number of end-to-end machine learning notebooks, including machine translation, image classification, and customer churn, created by students in the 696DS course at UMass Amherst.
+ 
+## Data/Telemetry 
+This repository collects usage data and sends it to Microsoft to help improve our products and services. Read Microsoft's [privacy statement to learn more](https://privacy.microsoft.com/en-US/privacystatement)
+
+To opt out of tracking, please go to the raw markdown or .ipynb files and remove the following line of code:
 
 ```sh
-pip install azureml-core
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/README.png)"
 ```
+This URL will be slightly different depending on the file. 
 
-Install additional packages as needed:
-
-```sh
-pip install azureml-mlflow
-pip install azureml-dataset-runtime
-pip install azureml-automl-runtime
-pip install azureml-pipeline
-pip install azureml-pipeline-steps
-...
-```
-
-We recommend starting with one of the [quickstarts](tutorials/compute-instance-quickstarts).
-
-## Contributing
-
-This repository is a push-only mirror. Pull requests are ignored.
-
-## Code of Conduct
-
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). Please see the [code of conduct](CODE_OF_CONDUCT.md) for details.
-
-## Reference
-
-- [Documentation](https://docs.microsoft.com/azure/machine-learning)
-
+ ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/README.png)

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -254,8 +254,6 @@
         "\n",
         "Many of the sample notebooks use Azure ML managed compute (AmlCompute) to train models using a dynamically scalable pool of compute. In this section you will create default compute clusters for use by the other notebooks and any other operations you choose.\n",
         "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
-        "\n",
         "To create a cluster, you need to specify a compute configuration that specifies the type of machine to be used and the scalability behaviors.  Then you choose a name for the cluster that is unique within the workspace that can be used to address the cluster later.\n",
         "\n",
         "The cluster parameters are:\n",

contrib/fairness/fairlearn-azureml-mitigation.ipynb

@@ -36,9 +36,9 @@
         "\n",
         "<a id=\"Introduction\"></a>\n",
         "## Introduction\n",
-        "This notebook shows how to use [Fairlearn (an open source fairness assessment and unfairness mitigation package)](http://fairlearn.org) and Azure Machine Learning Studio for a binary classification problem. This example uses the well-known adult census dataset. For the purposes of this notebook, we shall treat this as a loan decision problem. We will pretend that the label indicates whether or not each individual repaid a loan in the past. We will use the data to train a predictor to predict whether previously unseen individuals will repay a loan or not. The assumption is that the model predictions are used to decide whether an individual should be offered a loan. Its purpose is purely illustrative of a workflow including a fairness dashboard - in particular, we do **not** include a full discussion of the detailed issues which arise when considering fairness in machine learning. For such discussions, please [refer to the Fairlearn website](http://fairlearn.org/).\n",
+        "This notebook shows how to use [Fairlearn (an open source fairness assessment and unfairness mitigation package)](http://fairlearn.github.io) and Azure Machine Learning Studio for a binary classification problem. This example uses the well-known adult census dataset. For the purposes of this notebook, we shall treat this as a loan decision problem. We will pretend that the label indicates whether or not each individual repaid a loan in the past. We will use the data to train a predictor to predict whether previously unseen individuals will repay a loan or not. The assumption is that the model predictions are used to decide whether an individual should be offered a loan. Its purpose is purely illustrative of a workflow including a fairness dashboard - in particular, we do **not** include a full discussion of the detailed issues which arise when considering fairness in machine learning. For such discussions, please [refer to the Fairlearn website](http://fairlearn.github.io/).\n",
         "\n",
-        "We will apply the [grid search algorithm](https://fairlearn.org/v0.4.6/api_reference/fairlearn.reductions.html#fairlearn.reductions.GridSearch) from the Fairlearn package using a specific notion of fairness called Demographic Parity. This produces a set of models, and we will view these in a dashboard both locally and in the Azure Machine Learning Studio.\n",
+        "We will apply the [grid search algorithm](https://fairlearn.github.io/master/api_reference/fairlearn.reductions.html#fairlearn.reductions.GridSearch) from the Fairlearn package using a specific notion of fairness called Demographic Parity. This produces a set of models, and we will view these in a dashboard both locally and in the Azure Machine Learning Studio.\n",
         "\n",
         "### Setup\n",
         "\n",
@@ -46,10 +46,9 @@
         "Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
         "This notebook also requires the following packages:\n",
         "* `azureml-contrib-fairness`\n",
-        "* `fairlearn>=0.6.2` (pre-v0.5.0 will work with minor modifications)\n",
+        "* `fairlearn==0.4.6` (v0.5.0 will work with minor modifications)\n",
         "* `joblib`\n",
-        "* `liac-arff`\n",
-        "* `raiwidgets~=0.7.0`\n",
+        "* `shap`\n",
         "\n",
         "Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
       ]
@@ -86,9 +85,10 @@
       "outputs": [],
       "source": [
         "from fairlearn.reductions import GridSearch, DemographicParity, ErrorRate\n",
-        "from raiwidgets import FairnessDashboard\n",
+        "from fairlearn.widget import FairlearnDashboard\n",
         "\n",
         "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.datasets import fetch_openml\n",
         "from sklearn.impute import SimpleImputer\n",
         "from sklearn.linear_model import LogisticRegression\n",
         "from sklearn.model_selection import train_test_split\n",
@@ -112,9 +112,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from fairness_nb_utils import fetch_census_dataset\n",
+        "from fairness_nb_utils import fetch_openml_with_retries\n",
         "\n",
-        "data = fetch_census_dataset()\n",
+        "data = fetch_openml_with_retries(data_id=1590)\n",
         "    \n",
         "# Extract the items we want\n",
         "X_raw = data.data\n",
@@ -137,7 +137,7 @@
       "outputs": [],
       "source": [
         "A = X_raw[['sex','race']]\n",
-        "X_raw = X_raw.drop(labels=['sex', 'race'], axis = 1)"
+        "X_raw = X_raw.drop(labels=['sex', 'race'],axis = 1)"
       ]
     },
     {
@@ -257,9 +257,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "FairnessDashboard(sensitive_features=A_test,\n",
-        "                  y_true=y_test,\n",
-        "                  y_pred={\"unmitigated\": unmitigated_predictor.predict(X_test)})"
+        "FairlearnDashboard(sensitive_features=A_test, sensitive_feature_names=['Sex', 'Race'],\n",
+        "                   y_true=y_test,\n",
+        "                   y_pred={\"unmitigated\": unmitigated_predictor.predict(X_test)})"
       ]
     },
     {
@@ -312,8 +312,8 @@
         "sweep.fit(X_train, y_train,\n",
         "          sensitive_features=A_train.sex)\n",
         "\n",
-        "# For Fairlearn pre-v0.5.0, need sweep._predictors\n",
-        "predictors = sweep.predictors_"
+        "# For Fairlearn v0.5.0, need sweep.predictors_\n",
+        "predictors = sweep._predictors"
       ]
     },
     {
@@ -330,14 +330,16 @@
       "outputs": [],
       "source": [
         "errors, disparities = [], []\n",
-        "for predictor in predictors:\n",
+        "for m in predictors:\n",
+        "    classifier = lambda X: m.predict(X)\n",
+        "    \n",
         "    error = ErrorRate()\n",
         "    error.load_data(X_train, pd.Series(y_train), sensitive_features=A_train.sex)\n",
         "    disparity = DemographicParity()\n",
         "    disparity.load_data(X_train, pd.Series(y_train), sensitive_features=A_train.sex)\n",
         "    \n",
-        "    errors.append(error.gamma(predictor.predict)[0])\n",
-        "    disparities.append(disparity.gamma(predictor.predict).max())\n",
+        "    errors.append(error.gamma(classifier)[0])\n",
+        "    disparities.append(disparity.gamma(classifier).max())\n",
         "    \n",
         "all_results = pd.DataFrame( {\"predictor\": predictors, \"error\": errors, \"disparity\": disparities})\n",
         "\n",
@@ -386,9 +388,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "FairnessDashboard(sensitive_features=A_test, \n",
-        "                  y_true=y_test.tolist(),\n",
-        "                  y_pred=predictions_dominant)"
+        "FairlearnDashboard(sensitive_features=A_test, \n",
+        "                   sensitive_feature_names=['Sex', 'Race'],\n",
+        "                   y_true=y_test.tolist(),\n",
+        "                   y_pred=predictions_dominant)"
       ]
     },
     {
@@ -407,7 +410,7 @@
         "<a id=\"AzureUpload\"></a>\n",
         "## Uploading a Fairness Dashboard to Azure\n",
         "\n",
-        "Uploading a fairness dashboard to Azure is a two stage process. The `FairnessDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. By default, the dashboard in Azure Machine Learning Studio also requires the models to be registered. The required stages are therefore:\n",
+        "Uploading a fairness dashboard to Azure is a two stage process. The `FairlearnDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. By default, the dashboard in Azure Machine Learning Studio also requires the models to be registered. The required stages are therefore:\n",
         "1. Register the dominant models\n",
         "1. Precompute all the required metrics\n",
         "1. Upload to Azure\n",
@@ -581,7 +584,7 @@
         "<a id=\"Conclusion\"></a>\n",
         "## Conclusion\n",
         "\n",
-        "In this notebook we have demonstrated how to use the `GridSearch` algorithm from Fairlearn to generate a collection of models, and then present them in the fairness dashboard in Azure Machine Learning Studio. Please remember that this notebook has not attempted to discuss the many considerations which should be part of any approach to unfairness mitigation. The [Fairlearn website](http://fairlearn.org/) provides that discussion"
+        "In this notebook we have demonstrated how to use the `GridSearch` algorithm from Fairlearn to generate a collection of models, and then present them in the fairness dashboard in Azure Machine Learning Studio. Please remember that this notebook has not attempted to discuss the many considerations which should be part of any approach to unfairness mitigation. The [Fairlearn website](http://fairlearn.github.io/) provides that discussion"
       ]
     },
     {

contrib/fairness/fairlearn-azureml-mitigation.yml

@@ -3,7 +3,5 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-contrib-fairness
-  - fairlearn>=0.6.2
+  - fairlearn==0.4.6
   - joblib
-  - liac-arff
-  - raiwidgets~=0.7.0

contrib/fairness/fairness_nb_utils.py

@@ -4,13 +4,7 @@
 
 """Utilities for azureml-contrib-fairness notebooks."""
 
-import arff
-from collections import OrderedDict
-from contextlib import closing
-import gzip
-import pandas as pd
 from sklearn.datasets import fetch_openml
-from sklearn.utils import Bunch
 import time
 
 
@@ -21,7 +15,7 @@ def fetch_openml_with_retries(data_id, max_retries=4, retry_delay=60):
             print("Download attempt {0} of {1}".format(i + 1, max_retries))
             data = fetch_openml(data_id=data_id, as_frame=True)
             break
-        except Exception as e:  # noqa: B902
+        except Exception as e:
             print("Download attempt failed with exception:")
             print(e)
             if i + 1 != max_retries:
@@ -32,80 +26,3 @@ def fetch_openml_with_retries(data_id, max_retries=4, retry_delay=60):
         raise RuntimeError("Unable to download dataset from OpenML")
 
     return data
-
-
-_categorical_columns = [
-    'workclass',
-    'education',
-    'marital-status',
-    'occupation',
-    'relationship',
-    'race',
-    'sex',
-    'native-country'
-]
-
-
-def fetch_census_dataset():
-    """Fetch the Adult Census Dataset.
-
-    This uses a particular URL for the Adult Census dataset. The code
-    is a simplified version of fetch_openml() in sklearn.
-
-    The data are copied from:
-    https://openml.org/data/v1/download/1595261.gz
-    (as of 2021-03-31)
-    """
-    try:
-        from urllib import urlretrieve
-    except ImportError:
-        from urllib.request import urlretrieve
-
-    filename = "1595261.gz"
-    data_url = "https://rainotebookscdn.blob.core.windows.net/datasets/"
-
-    remaining_attempts = 5
-    sleep_duration = 10
-    while remaining_attempts > 0:
-        try:
-            urlretrieve(data_url + filename, filename)
-
-            http_stream = gzip.GzipFile(filename=filename, mode='rb')
-
-            with closing(http_stream):
-                def _stream_generator(response):
-                    for line in response:
-                        yield line.decode('utf-8')
-
-                stream = _stream_generator(http_stream)
-                data = arff.load(stream)
-        except Exception as exc:  # noqa: B902
-            remaining_attempts -= 1
-            print("Error downloading dataset from {} ({} attempt(s) remaining)"
-                  .format(data_url, remaining_attempts))
-            print(exc)
-            time.sleep(sleep_duration)
-            sleep_duration *= 2
-            continue
-        else:
-            # dataset successfully downloaded
-            break
-    else:
-        raise Exception("Could not retrieve dataset from {}.".format(data_url))
-
-    attributes = OrderedDict(data['attributes'])
-    arff_columns = list(attributes)
-
-    raw_df = pd.DataFrame(data=data['data'], columns=arff_columns)
-
-    target_column_name = 'class'
-    target = raw_df.pop(target_column_name)
-    for col_name in _categorical_columns:
-        dtype = pd.api.types.CategoricalDtype(attributes[col_name])
-        raw_df[col_name] = raw_df[col_name].astype(dtype, copy=False)
-
-    result = Bunch()
-    result.data = raw_df
-    result.target = target
-
-    return result

contrib/fairness/upload-fairness-dashboard.ipynb

@@ -30,7 +30,7 @@
         "1. [Training Models](#TrainingModels)\n",
         "1. [Logging in to AzureML](#LoginAzureML)\n",
         "1. [Registering the Models](#RegisterModels)\n",
-        "1. [Using the Fairness Dashboard](#LocalDashboard)\n",
+        "1. [Using the Fairlearn Dashboard](#LocalDashboard)\n",
         "1. [Uploading a Fairness Dashboard to Azure](#AzureUpload)\n",
         "    1. Computing Fairness Metrics\n",
         "    1. Uploading to Azure\n",
@@ -48,10 +48,9 @@
         "Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
         "This notebook also requires the following packages:\n",
         "* `azureml-contrib-fairness`\n",
-        "* `fairlearn>=0.6.2` (also works for pre-v0.5.0 with slight modifications)\n",
+        "* `fairlearn==0.4.6` (should also work with v0.5.0)\n",
         "* `joblib`\n",
-        "* `liac-arff`\n",
-        "* `raiwidgets~=0.7.0`\n",
+        "* `shap`\n",
         "\n",
         "Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
       ]
@@ -89,6 +88,7 @@
       "source": [
         "from sklearn import svm\n",
         "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.datasets import fetch_openml\n",
         "from sklearn.impute import SimpleImputer\n",
         "from sklearn.linear_model import LogisticRegression\n",
         "from sklearn.model_selection import train_test_split\n",
@@ -110,9 +110,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from fairness_nb_utils import fetch_census_dataset\n",
+        "from fairness_nb_utils import fetch_openml_with_retries\n",
         "\n",
-        "data = fetch_census_dataset()\n",
+        "data = fetch_openml_with_retries(data_id=1590)\n",
         "    \n",
         "# Extract the items we want\n",
         "X_raw = data.data\n",
@@ -389,11 +389,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from raiwidgets import FairnessDashboard\n",
+        "from fairlearn.widget import FairlearnDashboard\n",
         "\n",
-        "FairnessDashboard(sensitive_features=A_test, \n",
-        "                  y_true=y_test.tolist(),\n",
-        "                  y_pred=ys_pred)"
+        "FairlearnDashboard(sensitive_features=A_test, \n",
+        "                   sensitive_feature_names=['Sex', 'Race'],\n",
+        "                   y_true=y_test.tolist(),\n",
+        "                   y_pred=ys_pred)"
       ]
     },
     {
@@ -403,7 +404,7 @@
         "<a id=\"AzureUpload\"></a>\n",
         "## Uploading a Fairness Dashboard to Azure\n",
         "\n",
-        "Uploading a fairness dashboard to Azure is a two stage process. The `FairnessDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. The required stages are therefore:\n",
+        "Uploading a fairness dashboard to Azure is a two stage process. The `FairlearnDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. The required stages are therefore:\n",
         "1. Precompute all the required metrics\n",
         "1. Upload to Azure\n",
         "\n",

contrib/fairness/upload-fairness-dashboard.yml

@@ -3,7 +3,5 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-contrib-fairness
-  - fairlearn>=0.6.2
+  - fairlearn==0.4.6
   - joblib
-  - liac-arff
-  - raiwidgets~=0.7.0

how-to-use-azureml/automated-machine-learning/automl_env.yml

@@ -2,7 +2,7 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
-- pip==21.1.2
+- pip==20.2.4
 - python>=3.5.2,<3.8
 - nb_conda
 - boto3==1.15.18
@@ -21,8 +21,8 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.32.0
+  - azureml-widgets~=1.21.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.32.0/validated_win32_requirements.txt [--no-deps]
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.21.0/validated_win32_requirements.txt [--no-deps]

how-to-use-azureml/automated-machine-learning/automl_env_linux.yml

@@ -2,7 +2,7 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
-- pip==21.1.2
+- pip==20.2.4
 - python>=3.5.2,<3.8
 - nb_conda
 - boto3==1.15.18
@@ -21,8 +21,9 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.32.0
+  - azureml-widgets~=1.21.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.32.0/validated_linux_requirements.txt [--no-deps]
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.21.0/validated_linux_requirements.txt [--no-deps]
+

how-to-use-azureml/automated-machine-learning/automl_env_mac.yml

@@ -2,7 +2,7 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
-- pip==21.1.2
+- pip==20.2.4
 - nomkl
 - python>=3.5.2,<3.8
 - nb_conda
@@ -22,8 +22,8 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.32.0
+  - azureml-widgets~=1.21.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
-  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.32.0/validated_darwin_requirements.txt [--no-deps]
+  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz  
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.21.0/validated_darwin_requirements.txt [--no-deps]

how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh

@@ -32,7 +32,6 @@ if [ $? -ne 0 ]; then
 fi
 
 sed -i '' 's/AZUREML-SDK-VERSION/latest/' $AUTOML_ENV_FILE
-brew install libomp
 
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then

how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb

@@ -105,7 +105,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -165,9 +165,6 @@
       "source": [
         "## Create or Attach existing AmlCompute\n",
         "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
-        "\n",
         "#### Creation of AmlCompute takes approximately 5 minutes. \n",
         "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
@@ -190,7 +187,7 @@
         "    compute_target = 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_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=6)\n",
         "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
         "\n",
@@ -377,6 +374,15 @@
         "remote_run = experiment.submit(automl_config, show_output = False)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb

@@ -93,7 +93,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -127,9 +127,6 @@
       "source": [
         "## Create or Attach existing AmlCompute\n",
         "A compute target is required to execute the Automated ML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
-        "\n",
         "#### Creation of AmlCompute takes approximately 5 minutes. \n",
         "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
@@ -258,6 +255,15 @@
         "#remote_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb

@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -138,8 +138,6 @@
         "## Set up a compute cluster\n",
         "This section uses a user-provided compute cluster (named \"dnntext-cluster\" in this example). If a cluster with this name does not exist in the user's workspace, the below code will create a new cluster. You can choose the parameters of the cluster as mentioned in the comments.\n",
         "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
-        "\n",
         "Whether you provide/select a CPU or GPU cluster, AutoML will choose the appropriate DNN for that setup - BiLSTM or BERT text featurizer will be included in the candidate featurizers on CPU and GPU respectively.  If your goal is to obtain the most accurate model, we recommend you use GPU clusters since BERT featurizers usually outperform BiLSTM featurizers."
       ]
     },
@@ -162,7 +160,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\", # CPU for BiLSTM, such as \"STANDARD_DS12_V2\" \n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\", # CPU for BiLSTM, such as \"STANDARD_D2_V2\" \n",
         "                                                           # To use BERT (this is recommended for best performance), select a GPU such as \"STANDARD_NC6\" \n",
         "                                                           # or similar GPU option\n",
         "                                                           # available in your workspace\n",
@@ -283,7 +281,7 @@
       "outputs": [],
       "source": [
         "automl_settings = {\n",
-        "    \"experiment_timeout_minutes\": 30,\n",
+        "    \"experiment_timeout_minutes\": 20,\n",
         "    \"primary_metric\": 'accuracy',\n",
         "    \"max_concurrent_iterations\": num_nodes, \n",
         "    \"max_cores_per_iteration\": -1,\n",
@@ -321,6 +319,15 @@
         "automl_run = experiment.submit(automl_config, show_output=True)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_run"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -487,7 +494,7 @@
       "outputs": [],
       "source": [
         "test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run,\n",
-        "                         test_dataset, target_column_name, model_name)"
+        "                         train_dataset, test_dataset, target_column_name, model_name)"
       ]
     },
     {

how-to-use-azureml/automated-machine-learning/classification-text-dnn/helper.py

@@ -5,7 +5,7 @@ from azureml.core.run import Run
 
 
 def run_inference(test_experiment, compute_target, script_folder, train_run,
-                  test_dataset, target_column_name, model_name):
+                  train_dataset, test_dataset, target_column_name, model_name):
 
     inference_env = train_run.get_environment()
 
@@ -16,6 +16,7 @@ def run_inference(test_experiment, compute_target, script_folder, train_run,
                         '--model_name': model_name
                     },
                     inputs=[
+                        train_dataset.as_named_input('train_data'),
                         test_dataset.as_named_input('test_data')
                     ],
                     compute_target=compute_target,

how-to-use-azureml/automated-machine-learning/classification-text-dnn/infer.py

@@ -1,6 +1,5 @@
 import argparse
 
-import pandas as pd
 import numpy as np
 
 from sklearn.externals import joblib
@@ -33,21 +32,22 @@ model = joblib.load(model_path)
 run = Run.get_context()
 # get input dataset by name
 test_dataset = run.input_datasets['test_data']
+train_dataset = run.input_datasets['train_data']
 
 X_test_df = test_dataset.drop_columns(columns=[target_column_name]) \
                         .to_pandas_dataframe()
 y_test_df = test_dataset.with_timestamp_columns(None) \
                         .keep_columns(columns=[target_column_name]) \
                         .to_pandas_dataframe()
+y_train_df = test_dataset.with_timestamp_columns(None) \
+                         .keep_columns(columns=[target_column_name]) \
+                         .to_pandas_dataframe()
 
 predicted = model.predict_proba(X_test_df)
 
-if isinstance(predicted, pd.DataFrame):
-    predicted = predicted.values
-
 # Use the AutoML scoring module
+class_labels = np.unique(np.concatenate((y_train_df.values, y_test_df.values)))
 train_labels = model.classes_
-class_labels = np.unique(np.concatenate((y_test_df.values, np.reshape(train_labels, (-1, 1)))))
 classification_metrics = list(constants.CLASSIFICATION_SCALAR_SET)
 scores = scoring.score_classification(y_test_df.values, predicted,
                                       classification_metrics,

how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb

@@ -81,7 +81,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -141,9 +141,6 @@
         "#### Create or Attach existing AmlCompute\n",
         "\n",
         "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
-        "\n",
         "#### Creation of AmlCompute takes approximately 5 minutes. \n",
         "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
@@ -166,7 +163,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=4)\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",

how-to-use-azureml/automated-machine-learning/continuous-retraining/upload_weather_data.py

@@ -49,24 +49,22 @@ print("Argument 1(ds_name): %s" % args.ds_name)
 
 dstor = ws.get_default_datastore()
 register_dataset = False
-end_time = datetime.utcnow()
-
 try:
     ds = Dataset.get_by_name(ws, args.ds_name)
     end_time_last_slice = ds.data_changed_time.replace(tzinfo=None)
     print("Dataset {0} last updated on {1}".format(args.ds_name,
                                                    end_time_last_slice))
-except Exception:
+except Exception as e:
     print(traceback.format_exc())
     print("Dataset with name {0} not found, registering new dataset.".format(args.ds_name))
     register_dataset = True
-    end_time = datetime(2021, 5, 1, 0, 0)
-    end_time_last_slice = end_time - relativedelta(weeks=2)
+    end_time_last_slice = datetime.today() - relativedelta(weeks=2)
 
+end_time = datetime.utcnow()
 train_df = get_noaa_data(end_time_last_slice, end_time)
 
 if train_df.size > 0:
-    print("Received {0} rows of new data after {1}.".format(
+    print("Received {0} rows of new data after {0}.".format(
         train_df.shape[0], end_time_last_slice))
     folder_name = "{}/{:04d}/{:02d}/{:02d}/{:02d}/{:02d}/{:02d}".format(args.ds_name, end_time.year,
                                                                         end_time.month, end_time.day,

how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client.cmd

@@ -5,7 +5,7 @@ set options=%3
 set PIP_NO_WARN_SCRIPT_LOCATION=0
 
 IF "%conda_env_name%"=="" SET conda_env_name="azure_automl_experimental"
-IF "%automl_env_file%"=="" SET automl_env_file="automl_thin_client_env.yml"
+IF "%automl_env_file%"=="" SET automl_env_file="automl_env.yml"
 
 IF NOT EXIST %automl_env_file% GOTO YmlMissing
 

how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client_linux.sh

@@ -12,7 +12,7 @@ fi
 
 if [ "$AUTOML_ENV_FILE" == "" ]
 then
-  AUTOML_ENV_FILE="automl_thin_client_env.yml"
+  AUTOML_ENV_FILE="automl_env.yml"
 fi
 
 if [ ! -f $AUTOML_ENV_FILE ]; then

how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client_mac.sh

@@ -12,7 +12,7 @@ fi
 
 if [ "$AUTOML_ENV_FILE" == "" ]
 then
-  AUTOML_ENV_FILE="automl_thin_client_env_mac.yml"
+  AUTOML_ENV_FILE="automl_env.yml"
 fi
 
 if [ ! -f $AUTOML_ENV_FILE ]; then

how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env.yml

@@ -5,14 +5,16 @@ dependencies:
 - pip<=19.3.1
 - python>=3.5.2,<3.8
 - nb_conda
+- matplotlib==2.1.0
+- numpy~=1.18.0
 - cython
 - urllib3<1.24
-- PyJWT < 2.0.0
-- numpy==1.18.5
+- scikit-learn==0.22.1
+- pandas==0.25.1
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
   - azureml-defaults
   - azureml-sdk
   - azureml-widgets
-  - pandas
+  - azureml-explain-model

how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env_mac.yml

@@ -6,14 +6,16 @@ dependencies:
 - nomkl
 - python>=3.5.2,<3.8
 - nb_conda
+- matplotlib==2.1.0
+- numpy~=1.18.0
 - cython
 - urllib3<1.24
-- PyJWT < 2.0.0
-- numpy==1.18.5
+- scikit-learn==0.22.1
+- pandas==0.25.1
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
   - azureml-defaults
   - azureml-sdk
   - azureml-widgets
-  - pandas
+  - azureml-explain-model

how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb

@@ -1,420 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Automated Machine Learning\n",
-        "_**Classification of credit card fraudulent transactions on local managed compute **_\n",
-        "\n",
-        "## Contents\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Train](#Train)\n",
-        "1. [Results](#Results)\n",
-        "1. [Test](#Test)\n",
-        "1. [Acknowledgements](#Acknowledgements)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "In this example we use the associated credit card dataset to showcase how you can use AutoML for a simple classification problem. The goal is to predict if a credit card transaction is considered a fraudulent charge.\n",
-        "\n",
-        "This notebook is using local managed compute to train the model.\n",
-        "\n",
-        "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
-        "\n",
-        "In this notebook you will learn how to:\n",
-        "1. Create an experiment using an existing workspace.\n",
-        "2. Configure AutoML using `AutoMLConfig`.\n",
-        "3. Train the model using local managed compute.\n",
-        "4. Explore the results.\n",
-        "5. Test the 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 Automated ML 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",
-        "\n",
-        "import pandas as pd\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.compute_target import LocalTarget\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.core.dataset import Dataset\n",
-        "from azureml.train.automl import AutoMLConfig"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
-        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# choose a name for experiment\n",
-        "experiment_name = 'automl-local-managed'\n",
-        "\n",
-        "experiment=Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\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['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
-        "outputDf.T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Determine if local docker is configured for Linux images\n",
-        "\n",
-        "Local managed runs will leverage a Linux docker container to submit the run to. Due to this, the docker needs to be configured to use Linux containers."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check if Docker is installed and Linux containers are enabled\n",
-        "import subprocess\n",
-        "from subprocess import CalledProcessError\n",
-        "try:\n",
-        "    assert subprocess.run(\"docker -v\", shell=True).returncode == 0, 'Local Managed runs require docker to be installed.'\n",
-        "    out = subprocess.check_output(\"docker system info\", shell=True).decode('ascii')\n",
-        "    assert \"OSType: linux\" in out, 'Docker engine needs to be configured to use Linux containers.' \\\n",
-        "        'https://docs.docker.com/docker-for-windows/#switch-between-windows-and-linux-containers'\n",
-        "except CalledProcessError as ex:\n",
-        "    raise Exception('Local Managed runs require docker to be installed.') from ex"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Load Data\n",
-        "\n",
-        "Load the credit card dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. Next, we'll split the data using random_split and extract the training data for the model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard.csv\"\n",
-        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
-        "training_data, validation_data = dataset.random_split(percentage=0.8, seed=223)\n",
-        "label_column_name = 'Class'"
-      ]
-    },
-    {
-      "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**|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",
-        "|**enable_early_stopping**|Stop the run if the metric score is not showing improvement.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**training_data**|Input dataset, containing both features and label column.|\n",
-        "|**label_column_name**|The name of the label column.|\n",
-        "|**enable_local_managed**|Enable the experimental local-managed scenario.|\n",
-        "\n",
-        "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_settings = {\n",
-        "    \"n_cross_validations\": 3,\n",
-        "    \"primary_metric\": 'average_precision_score_weighted',\n",
-        "    \"enable_early_stopping\": True,\n",
-        "    \"experiment_timeout_hours\": 0.3, #for real scenarios we recommend a timeout of at least one hour \n",
-        "    \"verbosity\": logging.INFO,\n",
-        "}\n",
-        "\n",
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             compute_target = LocalTarget(),\n",
-        "                             enable_local_managed = True,\n",
-        "                             training_data = training_data,\n",
-        "                             label_column_name = label_column_name,\n",
-        "                             **automl_settings\n",
-        "                            )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "parent_run = experiment.submit(automl_config, show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# If you need to retrieve a run that already started, use the following code\n",
-        "#from azureml.train.automl.run import AutoMLRun\n",
-        "#parent_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "parent_run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Results"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Explain model\n",
-        "\n",
-        "Automated ML models can be explained and visualized using the SDK Explainability library. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Analyze results\n",
-        "\n",
-        "### Retrieve the Best Child Run\n",
-        "\n",
-        "Below we select the best pipeline from our iterations. The `get_best_child` method returns the best run. Overloads on `get_best_child` allow you to retrieve the best run for *any* logged metric."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "best_run = parent_run.get_best_child()\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Test the fitted model\n",
-        "\n",
-        "Now that the model is trained, split the data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "X_test_df = validation_data.drop_columns(columns=[label_column_name])\n",
-        "y_test_df = validation_data.keep_columns(columns=[label_column_name], validate=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Creating ModelProxy for submitting prediction runs to the training environment.\n",
-        "We will create a ModelProxy for the best child run, which will allow us to submit a run that does the prediction in the training environment. Unlike the local client, which can have different versions of some libraries, the training environment will have all the compatible libraries for the model already."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.automl.model_proxy import ModelProxy\n",
-        "best_model_proxy = ModelProxy(best_run)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# call the predict functions on the model proxy\n",
-        "y_pred = best_model_proxy.predict(X_test_df).to_pandas_dataframe()\n",
-        "y_pred"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Acknowledgements"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This Credit Card fraud Detection dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ and is available at: https://www.kaggle.com/mlg-ulb/creditcardfraud\n",
-        "\n",
-        "\n",
-        "The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Universit\u00c3\u0192\u00c2\u00a9 Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project\n",
-        "Please cite the following works: \n",
-        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tAndrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\n",
-        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tDal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon\n",
-        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tDal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE\n",
-        "o\tDal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)\n",
-        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tCarcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-A\u00c3\u0192\u00c2\u00abl; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier\n",
-        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tCarcillo, Fabrizio; Le Borgne, Yann-A\u00c3\u0192\u00c2\u00abl; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sekrupa"
-      }
-    ],
-    "category": "tutorial",
-    "compute": [
-      "AML Compute"
-    ],
-    "datasets": [
-      "Creditcard"
-    ],
-    "deployment": [
-      "None"
-    ],
-    "exclude_from_index": false,
-    "file_extension": ".py",
-    "framework": [
-      "None"
-    ],
-    "friendly_name": "Classification of credit card fraudulent transactions using Automated ML",
-    "index_order": 5,
-    "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": "3.6.7"
-    },
-    "mimetype": "text/x-python",
-    "name": "python",
-    "nbconvert_exporter": "python",
-    "pygments_lexer": "ipython3",
-    "tags": [
-      "AutomatedML"
-    ],
-    "task": "Classification",
-    "version": "3.6.7"
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.yml

@@ -1,4 +0,0 @@
-name: auto-ml-classification-credit-card-fraud-local-managed
-dependencies:
-- pip:
-  - azureml-sdk

how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb

@@ -39,7 +39,6 @@
       "source": [
         "## Introduction\n",
         "In this example we use an experimental feature, Model Proxy, to do a predict on the best generated model without downloading the model locally. The prediction will happen on same compute and environment that was used to train the model. This feature is currently in the experimental state, which means that the API is prone to changing, please make sure to run on the latest version of this notebook if you face any issues.\n",
-        "This notebook will also leverage MLFlow for saving models, allowing for more portability of the resulting models. See https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow for more details around MLFlow is AzureML.\n",
         "\n",
         "If you are using an Azure Machine Learning Compute Instance, you are all set.  Otherwise, go through the [configuration](../../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
         "\n",
@@ -68,8 +67,11 @@
       "source": [
         "import logging\n",
         "\n",
+        "from matplotlib import pyplot as plt\n",
         "import json\n",
-        "\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        " \n",
         "\n",
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
@@ -91,7 +93,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -114,7 +116,9 @@
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
         "output['Run History Name'] = experiment_name\n",
-        "output"
+        "pd.set_option('display.max_colwidth', -1)\n",
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {
@@ -143,7 +147,7 @@
         "    compute_target = 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_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=4)\n",
         "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
         "\n",
@@ -195,6 +199,7 @@
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**training_data**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**label_column_name**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
+        "|**scenario**|We need to set this parameter to 'Latest' to enable some experimental features. This parameter should not be set outside of this experimental notebook.|\n",
         "\n",
         "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
       ]
@@ -213,17 +218,17 @@
         "    \"n_cross_validations\": 3,\n",
         "    \"primary_metric\": 'r2_score',\n",
         "    \"enable_early_stopping\": True, \n",
-        "    \"experiment_timeout_hours\": 0.3, #for real scenarios we recommend a timeout of at least one hour \n",
+        "    \"experiment_timeout_hours\": 0.3, #for real scenarios we reccommend a timeout of at least one hour \n",
         "    \"max_concurrent_iterations\": 4,\n",
         "    \"max_cores_per_iteration\": -1,\n",
         "    \"verbosity\": logging.INFO,\n",
-        "    \"save_mlflow\": True,\n",
         "}\n",
         "\n",
         "automl_config = AutoMLConfig(task = 'regression',\n",
         "                             compute_target = compute_target,\n",
         "                             training_data = train_data,\n",
         "                             label_column_name = label,\n",
+        "                             scenario='Latest',\n",
         "                             **automl_settings\n",
         "                            )"
       ]
@@ -271,13 +276,34 @@
         "## 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": [
-        "remote_run.wait_for_completion(show_output=True)"
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(remote_run).show() "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run.wait_for_completion()"
       ]
     },
     {
@@ -342,12 +368,18 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "y_test = test_data.keep_columns('ERP')\n",
-        "test_data = test_data.drop_columns('ERP')\n",
+        "# preview the first 3 rows of the dataset\n",
+        "\n",
+        "test_data = test_data.to_pandas_dataframe()\n",
+        "y_test = test_data['ERP'].fillna(0)\n",
+        "test_data = test_data.drop('ERP', 1)\n",
+        "test_data = test_data.fillna(0)\n",
         "\n",
         "\n",
-        "y_train = train_data.keep_columns('ERP')\n",
-        "train_data = train_data.drop_columns('ERP')\n"
+        "train_data = train_data.to_pandas_dataframe()\n",
+        "y_train = train_data['ERP'].fillna(0)\n",
+        "train_data = train_data.drop('ERP', 1)\n",
+        "train_data = train_data.fillna(0)\n"
       ]
     },
     {
@@ -365,16 +397,7 @@
       "outputs": [],
       "source": [
         "from azureml.train.automl.model_proxy import ModelProxy\n",
-        "best_model_proxy = ModelProxy(best_run)\n",
-        "y_pred_train = best_model_proxy.predict(train_data)\n",
-        "y_pred_test = best_model_proxy.predict(test_data)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Exploring results"
+        "best_model_proxy = ModelProxy(best_run)"
       ]
     },
     {
@@ -383,15 +406,60 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "y_pred_train = y_pred_train.to_pandas_dataframe().values.flatten()\n",
-        "y_train = y_train.to_pandas_dataframe().values.flatten()\n",
+        "y_pred_train = best_model_proxy.predict(train_data).to_pandas_dataframe().values.flatten()\n",
         "y_residual_train = y_train - y_pred_train\n",
         "\n",
-        "y_pred_test = y_pred_test.to_pandas_dataframe().values.flatten()\n",
-        "y_test = y_test.to_pandas_dataframe().values.flatten()\n",
-        "y_residual_test = y_test - y_pred_test\n",
-        "print(y_residual_train)\n",
-        "print(y_residual_test)"
+        "y_pred_test = best_model_proxy.predict(test_data).to_pandas_dataframe().values.flatten()\n",
+        "y_residual_test = y_test - y_pred_test"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "from sklearn.metrics import mean_squared_error, r2_score\n",
+        "\n",
+        "# Set up a multi-plot chart.\n",
+        "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[1, 1], 'wspace':0, 'hspace': 0})\n",
+        "f.suptitle('Regression Residual Values', fontsize = 18)\n",
+        "f.set_figheight(6)\n",
+        "f.set_figwidth(16)\n",
+        "\n",
+        "# Plot residual values of training set.\n",
+        "a0.axis([0, 360, -100, 100])\n",
+        "a0.plot(y_residual_train, 'bo', alpha = 0.5)\n",
+        "a0.plot([-10,360],[0,0], 'r-', lw = 3)\n",
+        "a0.text(16,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_train, y_pred_train))), fontsize = 12)\n",
+        "a0.text(16,140,'R2 score = {0:.2f}'.format(r2_score(y_train, y_pred_train)),fontsize = 12)\n",
+        "a0.set_xlabel('Training samples', fontsize = 12)\n",
+        "a0.set_ylabel('Residual Values', fontsize = 12)\n",
+        "\n",
+        "# Plot residual values of test set.\n",
+        "a1.axis([0, 90, -100, 100])\n",
+        "a1.plot(y_residual_test, 'bo', alpha = 0.5)\n",
+        "a1.plot([-10,360],[0,0], 'r-', lw = 3)\n",
+        "a1.text(5,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, y_pred_test))), fontsize = 12)\n",
+        "a1.text(5,140,'R2 score = {0:.2f}'.format(r2_score(y_test, y_pred_test)),fontsize = 12)\n",
+        "a1.set_xlabel('Test samples', fontsize = 12)\n",
+        "a1.set_yticklabels([])\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "test_pred = plt.scatter(y_test, y_pred_test, color='')\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()"
       ]
     },
     {

how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb

@@ -113,7 +113,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -162,9 +162,7 @@
       },
       "source": [
         "### Using AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you use `AmlCompute` as your training compute resource.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you use `AmlCompute` as your training compute resource."
       ]
     },
     {
@@ -187,7 +185,7 @@
         "    compute_target = 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_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=4)\n",
         "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
         "\n",
@@ -367,9 +365,7 @@
       "source": [
         "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
         "forecasting_parameters = ForecastingParameters(\n",
-        "    time_column_name=time_column_name,\n",
-        "    forecast_horizon=forecast_horizon,\n",
-        "    freq='MS' # Set the forecast frequency to be monthly (start of the month)\n",
+        "    time_column_name=time_column_name, forecast_horizon=forecast_horizon\n",
         ")\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
@@ -405,7 +401,8 @@
       },
       "outputs": [],
       "source": [
-        "remote_run = experiment.submit(automl_config, show_output= True)"
+        "remote_run = experiment.submit(automl_config, show_output= False)\n",
+        "remote_run"
       ]
     },
     {
@@ -422,6 +419,15 @@
         "# remote_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run.wait_for_completion()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -662,7 +668,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.9"
+      "version": "3.6.7"
     }
   },
   "nbformat": 4,

how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb

@@ -87,7 +87,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -129,9 +129,6 @@
       "source": [
         "## Compute\n",
         "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
-        "\n",
         "#### Creation of AmlCompute takes approximately 5 minutes. \n",
         "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
@@ -154,7 +151,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=4)\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
@@ -208,10 +205,6 @@
       "outputs": [],
       "source": [
         "dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, 'dataset/bike-no.csv')]).with_timestamp_columns(fine_grain_timestamp=time_column_name) \n",
-        "\n",
-        "# Drop the columns 'casual' and 'registered' as these columns are a breakdown of the total and therefore a leak.\n",
-        "dataset = dataset.drop_columns(columns=['casual', 'registered'])\n",
-        "\n",
         "dataset.take(5).to_pandas_dataframe().reset_index(drop=True)"
       ]
     },
@@ -258,6 +251,7 @@
         "|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n",
         "|**country_or_region_for_holidays**|The country/region used to generate holiday features. These should be ISO 3166 two-letter country/region codes (i.e. 'US', 'GB').|\n",
         "|**target_lags**|The target_lags specifies how far back we will construct the lags of the target variable.|\n",
+        "|**drop_column_names**|Name(s) of columns to drop prior to modeling|\n",
         "|**freq**|Forecast frequency. This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information."
       ]
     },
@@ -321,8 +315,8 @@
         "    time_column_name=time_column_name,\n",
         "    forecast_horizon=forecast_horizon,\n",
         "    country_or_region_for_holidays='US', # set country_or_region will trigger holiday featurizer\n",
-        "    target_lags='auto', # use heuristic based lag setting\n",
-        "    freq='D' # Set the forecast frequency to be daily\n",
+        "    target_lags='auto', # use heuristic based lag setting    \n",
+        "    drop_column_names=['casual', 'registered'] # these columns are a breakdown of the total and therefore a leak\n",
         ")\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
@@ -353,7 +347,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "remote_run = experiment.submit(automl_config, show_output=False)"
+        "remote_run = experiment.submit(automl_config, show_output=False)\n",
+        "remote_run"
       ]
     },
     {

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb

@@ -24,11 +24,10 @@
         "_**Forecasting using the Energy Demand Dataset**_\n",
         "\n",
         "## Contents\n",
-        "1. [Introduction](#introduction)\n",
-        "1. [Setup](#setup)\n",
-        "1. [Data and Forecasting Configurations](#data)\n",
-        "1. [Train](#train)\n",
-        "1. [Generate and Evaluate the Forecast](#forecast)\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data and Forecasting Configurations](#Data)\n",
+        "1. [Train](#Train)\n",
         "\n",
         "Advanced Forecasting\n",
         "1. [Advanced Training](#advanced_training)\n",
@@ -39,7 +38,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Introduction<a id=\"introduction\"></a>\n",
+        "## Introduction\n",
         "\n",
         "In this example we use the associated New York City energy demand dataset to showcase how you can use AutoML for a simple forecasting problem and explore the results. The goal is predict the energy demand for the next 48 hours based on historic time-series data.\n",
         "\n",
@@ -50,16 +49,15 @@
         "1. Configure AutoML using 'AutoMLConfig'\n",
         "1. Train the model using AmlCompute\n",
         "1. Explore the engineered features and results\n",
-        "1. Generate the forecast and compute the out-of-sample accuracy metrics\n",
         "1. Configuration and remote run of AutoML for a time-series model with lag and rolling window features\n",
-        "1. Run and explore the forecast with lagging features"
+        "1. Run and explore the forecast"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Setup<a id=\"setup\"></a>"
+        "## Setup"
       ]
     },
     {
@@ -99,7 +97,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -179,7 +177,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Data<a id=\"data\"></a>\n",
+        "# Data\n",
         "\n",
         "We will use energy consumption [data from New York City](http://mis.nyiso.com/public/P-58Blist.htm) for model training. The data is stored in a tabular format and includes energy demand and basic weather data at an hourly frequency. \n",
         "\n",
@@ -311,7 +309,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Train<a id=\"train\"></a>\n",
+        "## Train\n",
         "\n",
         "Instantiate an AutoMLConfig object. This config defines the settings and data used to run the experiment. We can provide extra configurations within 'automl_settings', for this forecasting task we add the forecasting parameters to hold all the additional forecasting parameters.\n",
         "\n",
@@ -344,9 +342,7 @@
       "source": [
         "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
         "forecasting_parameters = ForecastingParameters(\n",
-        "    time_column_name=time_column_name,\n",
-        "    forecast_horizon=forecast_horizon,\n",
-        "    freq='H' # Set the forecast frequency to be hourly\n",
+        "    time_column_name=time_column_name, forecast_horizon=forecast_horizon\n",
         ")\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
@@ -379,6 +375,15 @@
         "remote_run = experiment.submit(automl_config, show_output=False)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -453,11 +458,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Forecasting<a id=\"forecast\"></a>\n",
+        "## Forecasting\n",
         "\n",
-        "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. We will do batch scoring on the test dataset which should have the same schema as training dataset.\n",
-        "\n",
-        "The inference will run on a remote compute. In this example, it will re-use the training compute."
+        "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:"
       ]
     },
     {
@@ -466,15 +469,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "test_experiment = Experiment(ws, experiment_name + \"_inference\")"
+        "X_test = test.to_pandas_dataframe().reset_index(drop=True)\n",
+        "y_test = X_test.pop(target_column_name).values"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Retreiving forecasts from the model\n",
-        "We have created a function called `run_forecast` that submits the test data to the best model determined during the training run and retrieves forecasts. This function uses a helper script `forecasting_script` which is uploaded and expecuted on the remote compute."
+        "### Forecast Function\n",
+        "For forecasting, we will use the forecast function instead of the predict function. Using the predict method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use. Forecast function also can handle more complicated scenarios, see the [forecast function notebook](../forecasting-forecast-function/auto-ml-forecasting-function.ipynb)."
       ]
     },
     {
@@ -483,16 +487,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from run_forecast import run_remote_inference\n",
-        "remote_run_infer = run_remote_inference(test_experiment=test_experiment,\n",
-        "                                        compute_target=compute_target,\n",
-        "                                        train_run=best_run,\n",
-        "                                        test_dataset=test,\n",
-        "                                        target_column_name=target_column_name)\n",
-        "remote_run_infer.wait_for_completion(show_output=False)\n",
-        "\n",
-        "# download the inference output file to the local machine\n",
-        "remote_run_infer.download_file('outputs/predictions.csv', 'predictions.csv')"
+        "# The featurized data, aligned to y, will also be returned.\n",
+        "# This contains the assumptions that were made in the forecast\n",
+        "# and helps align the forecast to the original data\n",
+        "y_predictions, X_trans = fitted_model.forecast(X_test)"
       ]
     },
     {
@@ -500,7 +498,9 @@
       "metadata": {},
       "source": [
         "### Evaluate\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). For more metrics that can be used for evaluation after training, please see [supported metrics](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#regressionforecasting-metrics), and [how to calculate residuals](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#residuals)."
+        "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). For more metrics that can be used for evaluation after training, please see [supported metrics](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#regressionforecasting-metrics), and [how to calculate residuals](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#residuals).\n",
+        "\n",
+        "It is a good practice to always align the output explicitly to the input, as the count and order of the rows may have changed during transformations that span multiple rows."
       ]
     },
     {
@@ -509,9 +509,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# load forecast data frame\n",
-        "fcst_df = pd.read_csv('predictions.csv', parse_dates=[time_column_name])\n",
-        "fcst_df.head()"
+        "from forecasting_helper import align_outputs\n",
+        "\n",
+        "df_all = align_outputs(y_predictions, X_trans, X_test, y_test, target_column_name)"
       ]
     },
     {
@@ -526,8 +526,8 @@
         "\n",
         "# use automl metrics module\n",
         "scores = scoring.score_regression(\n",
-        "    y_test=fcst_df[target_column_name],\n",
-        "    y_pred=fcst_df['predicted'],\n",
+        "    y_test=df_all[target_column_name],\n",
+        "    y_pred=df_all['predicted'],\n",
         "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
@@ -536,8 +536,8 @@
         "    \n",
         "# Plot outputs\n",
         "%matplotlib inline\n",
-        "test_pred = plt.scatter(fcst_df[target_column_name], fcst_df['predicted'], color='b')\n",
-        "test_test = plt.scatter(fcst_df[target_column_name], fcst_df[target_column_name], color='g')\n",
+        "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
+        "test_test = plt.scatter(df_all[target_column_name], df_all[target_column_name], color='g')\n",
         "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
         "plt.show()"
       ]
@@ -546,7 +546,23 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Advanced Training <a id=\"advanced_training\"></a>\n",
+        "Looking at `X_trans` is also useful to see what featurization happened to the data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_trans"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Advanced Training <a id=\"advanced_training\"></a>\n",
         "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, time series identifier columns and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
       ]
     },
@@ -629,7 +645,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Advanced Results<a id=\"advanced_results\"></a>\n",
+        "## Advanced Results<a id=\"advanced_results\"></a>\n",
         "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, time series identifier columns and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
       ]
     },
@@ -639,17 +655,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "test_experiment_advanced = Experiment(ws, experiment_name + \"_inference_advanced\")\n",
-        "advanced_remote_run_infer = run_remote_inference(test_experiment=test_experiment_advanced,\n",
-        "                                                 compute_target=compute_target,\n",
-        "                                                 train_run=best_run_lags,\n",
-        "                                                 test_dataset=test,\n",
-        "                                                 target_column_name=target_column_name,\n",
-        "                                                 inference_folder='./forecast_advanced')\n",
-        "advanced_remote_run_infer.wait_for_completion(show_output=False)\n",
-        "\n",
-        "# download the inference output file to the local machine\n",
-        "advanced_remote_run_infer.download_file('outputs/predictions.csv', 'predictions_advanced.csv')"
+        "# The featurized data, aligned to y, will also be returned.\n",
+        "# This contains the assumptions that were made in the forecast\n",
+        "# and helps align the forecast to the original data\n",
+        "y_predictions, X_trans = fitted_model_lags.forecast(X_test)"
       ]
     },
     {
@@ -658,8 +667,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "fcst_adv_df = pd.read_csv('predictions_advanced.csv', parse_dates=[time_column_name])\n",
-        "fcst_adv_df.head()"
+        "from forecasting_helper import align_outputs\n",
+        "\n",
+        "df_all = align_outputs(y_predictions, X_trans, X_test, y_test, target_column_name)"
       ]
     },
     {
@@ -674,8 +684,8 @@
         "\n",
         "# use automl metrics module\n",
         "scores = scoring.score_regression(\n",
-        "    y_test=fcst_adv_df[target_column_name],\n",
-        "    y_pred=fcst_adv_df['predicted'],\n",
+        "    y_test=df_all[target_column_name],\n",
+        "    y_pred=df_all['predicted'],\n",
         "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
@@ -684,8 +694,8 @@
         "    \n",
         "# Plot outputs\n",
         "%matplotlib inline\n",
-        "test_pred = plt.scatter(fcst_adv_df[target_column_name], fcst_adv_df['predicted'], color='b')\n",
-        "test_test = plt.scatter(fcst_adv_df[target_column_name], fcst_adv_df[target_column_name], color='g')\n",
+        "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
+        "test_test = plt.scatter(df_all[target_column_name], df_all[target_column_name], color='g')\n",
         "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
         "plt.show()"
       ]
@@ -716,7 +726,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.9"
+      "version": "3.6.8"
     }
   },
   "nbformat": 4,

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/forecasting_helper.py

@@ -1,15 +1,5 @@
-"""
-This is the script that is executed on the compute instance. It relies
-on the model.pkl file which is uploaded along with this script to the
-compute instance.
-"""
-
-import argparse
 import pandas as pd
 import numpy as np
-from azureml.core import Dataset, Run
-from azureml.automl.core.shared.constants import TimeSeriesInternal
-from sklearn.externals import joblib
 from pandas.tseries.frequencies import to_offset
 
 
@@ -52,38 +42,3 @@ def align_outputs(y_predicted, X_trans, X_test, y_test, target_column_name,
     clean = together[together[[target_column_name,
                                predicted_column_name]].notnull().all(axis=1)]
     return(clean)
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument(
-    '--target_column_name', type=str, dest='target_column_name',
-    help='Target Column Name')
-parser.add_argument(
-    '--test_dataset', type=str, dest='test_dataset',
-    help='Test Dataset')
-
-args = parser.parse_args()
-target_column_name = args.target_column_name
-test_dataset_id = args.test_dataset
-
-run = Run.get_context()
-ws = run.experiment.workspace
-
-# get the input dataset by id
-test_dataset = Dataset.get_by_id(ws, id=test_dataset_id)
-
-X_test = test_dataset.to_pandas_dataframe().reset_index(drop=True)
-y_test = X_test.pop(target_column_name).values
-
-# generate forecast
-fitted_model = joblib.load('model.pkl')
-y_predictions, X_trans = fitted_model.forecast(X_test)
-
-# align output
-df_all = align_outputs(y_predictions, X_trans, X_test, y_test, target_column_name)
-
-file_name = 'outputs/predictions.csv'
-export_csv = df_all.to_csv(file_name, header=True, index=False)  # added Index
-
-# Upload the predictions into artifacts
-run.upload_file(name=file_name, path_or_stream=file_name)

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/forecasting_script.py

@@ -1,89 +0,0 @@
-"""
-This is the script that is executed on the compute instance. It relies
-on the model.pkl file which is uploaded along with this script to the
-compute instance.
-"""
-
-import argparse
-import pandas as pd
-import numpy as np
-from azureml.core import Dataset, Run
-from azureml.automl.core.shared.constants import TimeSeriesInternal
-from sklearn.externals import joblib
-from pandas.tseries.frequencies import to_offset
-
-
-def align_outputs(y_predicted, X_trans, X_test, y_test, target_column_name,
-                  predicted_column_name='predicted',
-                  horizon_colname='horizon_origin'):
-    """
-    Demonstrates how to get the output aligned to the inputs
-    using pandas indexes. Helps understand what happened if
-    the output's shape differs from the input shape, or if
-    the data got re-sorted by time and grain during forecasting.
-
-    Typical causes of misalignment are:
-    * we predicted some periods that were missing in actuals -> drop from eval
-    * model was asked to predict past max_horizon -> increase max horizon
-    * data at start of X_test was needed for lags -> provide previous periods
-    """
-
-    if (horizon_colname in X_trans):
-        df_fcst = pd.DataFrame({predicted_column_name: y_predicted,
-                                horizon_colname: X_trans[horizon_colname]})
-    else:
-        df_fcst = pd.DataFrame({predicted_column_name: y_predicted})
-
-    # y and X outputs are aligned by forecast() function contract
-    df_fcst.index = X_trans.index
-
-    # align original X_test to y_test
-    X_test_full = X_test.copy()
-    X_test_full[target_column_name] = y_test
-
-    # X_test_full's index does not include origin, so reset for merge
-    df_fcst.reset_index(inplace=True)
-    X_test_full = X_test_full.reset_index().drop(columns='index')
-    together = df_fcst.merge(X_test_full, how='right')
-
-    # drop rows where prediction or actuals are nan
-    # happens because of missing actuals
-    # or at edges of time due to lags/rolling windows
-    clean = together[together[[target_column_name,
-                               predicted_column_name]].notnull().all(axis=1)]
-    return(clean)
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument(
-    '--target_column_name', type=str, dest='target_column_name',
-    help='Target Column Name')
-parser.add_argument(
-    '--test_dataset', type=str, dest='test_dataset',
-    help='Test Dataset')
-
-args = parser.parse_args()
-target_column_name = args.target_column_name
-test_dataset_id = args.test_dataset
-
-run = Run.get_context()
-ws = run.experiment.workspace
-
-# get the input dataset by id
-test_dataset = Dataset.get_by_id(ws, id=test_dataset_id)
-
-X_test = test_dataset.to_pandas_dataframe().reset_index(drop=True)
-y_test = X_test.pop(target_column_name).values
-
-# generate forecast
-fitted_model = joblib.load('model.pkl')
-y_predictions, X_trans = fitted_model.forecast(X_test)
-
-# align output
-df_all = align_outputs(y_predictions, X_trans, X_test, y_test, target_column_name)
-
-file_name = 'outputs/predictions.csv'
-export_csv = df_all.to_csv(file_name, header=True, index=False)  # added Index
-
-# Upload the predictions into artifacts
-run.upload_file(name=file_name, path_or_stream=file_name)

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/metrics_helper.py

@@ -0,0 +1,22 @@
+import pandas as pd
+import numpy as np
+
+
+def APE(actual, pred):
+    """
+    Calculate absolute percentage error.
+    Returns a vector of APE values with same length as actual/pred.
+    """
+    return 100 * np.abs((actual - pred) / actual)
+
+
+def MAPE(actual, pred):
+    """
+    Calculate mean absolute percentage error.
+    Remove NA and values where actual is close to zero
+    """
+    not_na = ~(np.isnan(actual) | np.isnan(pred))
+    not_zero = ~np.isclose(actual, 0.0)
+    actual_safe = actual[not_na & not_zero]
+    pred_safe = pred[not_na & not_zero]
+    return np.mean(APE(actual_safe, pred_safe))

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/run_forecast.py

@@ -1,38 +0,0 @@
-import os
-import shutil
-from azureml.core import ScriptRunConfig
-
-
-def run_remote_inference(test_experiment, compute_target, train_run,
-                         test_dataset, target_column_name, inference_folder='./forecast'):
-    # Create local directory to copy the model.pkl and forecsting_script.py files into.
-    # These files will be uploaded to and executed on the compute instance.
-    os.makedirs(inference_folder, exist_ok=True)
-    shutil.copy('forecasting_script.py', inference_folder)
-
-    train_run.download_file('outputs/model.pkl',
-                            os.path.join(inference_folder, 'model.pkl'))
-
-    inference_env = train_run.get_environment()
-
-    config = ScriptRunConfig(source_directory=inference_folder,
-                             script='forecasting_script.py',
-                             arguments=['--target_column_name',
-                                        target_column_name,
-                                        '--test_dataset',
-                                        test_dataset.as_named_input(test_dataset.name)],
-                             compute_target=compute_target,
-                             environment=inference_env)
-
-    run = test_experiment.submit(config,
-                                 tags={'training_run_id':
-                                       train_run.id,
-                                       'run_algorithm':
-                                       train_run.properties['run_algorithm'],
-                                       'valid_score':
-                                       train_run.properties['score'],
-                                       'primary_metric':
-                                       train_run.properties['primary_metric']})
-
-    run.log("run_algorithm", run.tags['run_algorithm'])
-    return run

how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb

@@ -94,7 +94,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -263,9 +263,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+        "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource."
       ]
     },
     {
@@ -285,7 +283,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=6)\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
@@ -321,8 +319,7 @@
         "    time_column_name=TIME_COLUMN_NAME,\n",
         "    forecast_horizon=forecast_horizon,\n",
         "    time_series_id_column_names=[ TIME_SERIES_ID_COLUMN_NAME ],\n",
-        "    target_lags=lags,\n",
-        "    freq='H' # Set the forecast frequency to be hourly\n",
+        "    target_lags=lags\n",
         ")"
       ]
     },

how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb

@@ -24,20 +24,20 @@
         "_**Orange Juice Sales Forecasting**_\n",
         "\n",
         "## Contents\n",
-        "1. [Introduction](#introduction)\n",
-        "1. [Setup](#setup)\n",
-        "1. [Compute](#compute)\n",
-        "1. [Data](#data)\n",
-        "1. [Train](#train)\n",
-        "1. [Forecast](#forecast)\n",
-        "1. [Operationalize](#operationalize)"
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Compute](#Compute)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Predict](#Predict)\n",
+        "1. [Operationalize](#Operationalize)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Introduction<a id=\"introduction\"></a>\n",
+        "## Introduction\n",
         "In this example, we use AutoML to train, select, and operationalize a time-series forecasting model for multiple time-series.\n",
         "\n",
         "Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n",
@@ -49,7 +49,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Setup<a id=\"setup\"></a>"
+        "## Setup"
       ]
     },
     {
@@ -82,7 +82,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -122,11 +122,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Compute<a id=\"compute\"></a>\n",
+        "## Compute\n",
         "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
-        "\n",
         "#### Creation of AmlCompute takes approximately 5 minutes. \n",
         "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
@@ -149,7 +146,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=6)\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
@@ -160,7 +157,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Data<a id=\"data\"></a>\n",
+        "## 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."
       ]
     },
@@ -172,10 +169,6 @@
       "source": [
         "time_column_name = 'WeekStarting'\n",
         "data = pd.read_csv(\"dominicks_OJ.csv\", parse_dates=[time_column_name])\n",
-        "\n",
-        "# Drop the columns 'logQuantity' as it is a leaky feature.\n",
-        "data.drop('logQuantity', axis=1, inplace=True)\n",
-        "\n",
         "data.head()"
       ]
     },
@@ -287,8 +280,7 @@
       "outputs": [],
       "source": [
         "from azureml.core.dataset import Dataset\n",
-        "train_dataset = Dataset.Tabular.from_delimited_files(path=datastore.path('dataset/dominicks_OJ_train.csv'))\n",
-        "test_dataset = Dataset.Tabular.from_delimited_files(path=datastore.path('dataset/dominicks_OJ_test.csv'))"
+        "train_dataset = Dataset.Tabular.from_delimited_files(path=datastore.path('dataset/dominicks_OJ_train.csv'))"
       ]
     },
     {
@@ -351,6 +343,7 @@
       "outputs": [],
       "source": [
         "featurization_config = FeaturizationConfig()\n",
+        "featurization_config.drop_columns = ['logQuantity']  # 'logQuantity' is a leaky feature, so we remove it.\n",
         "# Force the CPWVOL5 feature to be numeric type.\n",
         "featurization_config.add_column_purpose('CPWVOL5', 'Numeric')\n",
         "# Fill missing values in the target column, Quantity, with zeros.\n",
@@ -381,7 +374,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Train<a id=\"train\"></a>\n",
+        "## Train\n",
         "\n",
         "The [AutoMLConfig](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py) 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, the training data, and cross-validation parameters.\n",
         "\n",
@@ -427,8 +420,7 @@
         "forecasting_parameters = ForecastingParameters(\n",
         "    time_column_name=time_column_name,\n",
         "    forecast_horizon=n_test_periods,\n",
-        "    time_series_id_column_names=time_series_id_column_names,\n",
-        "    freq='W-THU' # Set the forecast frequency to be weekly (start on each Thursday)\n",
+        "    time_series_id_column_names=time_series_id_column_names\n",
         ")\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',\n",
@@ -460,7 +452,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "remote_run = experiment.submit(automl_config, show_output=False)"
+        "remote_run = experiment.submit(automl_config, show_output=False)\n",
+        "remote_run"
       ]
     },
     {
@@ -522,11 +515,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Forecast<a id=\"forecast\"></a>\n",
+        "# Forecasting\n",
         "\n",
-        "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. We will do batch scoring on the test dataset which should have the same schema as training dataset.\n",
-        "\n",
-        "The inference will run on a remote compute. In this example, it will re-use the training compute."
+        "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:"
       ]
     },
     {
@@ -535,15 +526,17 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "test_experiment = Experiment(ws, experiment_name + \"_inference\")"
+        "X_test = test\n",
+        "y_test = X_test.pop(target_column_name).values"
       ]
     },
     {
-      "cell_type": "markdown",
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
-        "### Retreiving forecasts from the model\n",
-        "We have created a function called `run_forecast` that submits the test data to the best model determined during the training run and retrieves forecasts. This function uses a helper script `forecasting_script` which is uploaded and expecuted on the remote compute."
+        "X_test.head()"
       ]
     },
     {
@@ -559,16 +552,18 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from run_forecast import run_remote_inference\n",
-        "remote_run_infer = run_remote_inference(test_experiment=test_experiment, \n",
-        "                                        compute_target=compute_target,\n",
-        "                                        train_run=best_run,\n",
-        "                                        test_dataset=test_dataset,\n",
-        "                                        target_column_name=target_column_name)\n",
-        "remote_run_infer.wait_for_completion(show_output=False)\n",
+        "# forecast returns the predictions and the featurized data, aligned to X_test.\n",
+        "# This contains the assumptions that were made in the forecast\n",
+        "y_predictions, X_trans = fitted_model.forecast(X_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If you are used to scikit pipelines, perhaps you expected `predict(X_test)`. However, forecasting requires a more general interface that also supplies the past target `y` values. Please use `forecast(X,y)` as `predict(X)` is reserved for internal purposes on forecasting models.\n",
         "\n",
-        "# download the forecast file to the local machine\n",
-        "remote_run_infer.download_file('outputs/predictions.csv', 'predictions.csv')"
+        "The [forecast function notebook](../forecasting-forecast-function/auto-ml-forecasting-function.ipynb)."
       ]
     },
     {
@@ -588,9 +583,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# load forecast data frame\n",
-        "fcst_df = pd.read_csv('predictions.csv', parse_dates=[time_column_name])\n",
-        "fcst_df.head()"
+        "assign_dict = {'predicted': y_predictions, target_column_name: y_test}\n",
+        "df_all = X_test.assign(**assign_dict)"
       ]
     },
     {
@@ -605,8 +599,8 @@
         "\n",
         "# use automl scoring module\n",
         "scores = scoring.score_regression(\n",
-        "    y_test=fcst_df[target_column_name],\n",
-        "    y_pred=fcst_df['predicted'],\n",
+        "    y_test=df_all[target_column_name],\n",
+        "    y_pred=df_all['predicted'],\n",
         "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
@@ -615,8 +609,8 @@
         "    \n",
         "# Plot outputs\n",
         "%matplotlib inline\n",
-        "test_pred = plt.scatter(fcst_df[target_column_name], fcst_df['predicted'], color='b')\n",
-        "test_test = plt.scatter(fcst_df[target_column_name], fcst_df[target_column_name], color='g')\n",
+        "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
+        "test_test = plt.scatter(df_all[target_column_name], df_all[target_column_name], color='g')\n",
         "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
         "plt.show()"
       ]
@@ -625,7 +619,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Operationalize<a id=\"operationalize\"></a>"
+        "# Operationalize"
       ]
     },
     {
@@ -723,13 +717,12 @@
       "outputs": [],
       "source": [
         "import json\n",
-        "X_query = test.copy()\n",
-        "X_query.pop(target_column_name)\n",
+        "X_query = X_test.copy()\n",
         "# We have to convert datetime to string, because Timestamps cannot be serialized to JSON.\n",
         "X_query[time_column_name] = X_query[time_column_name].astype(str)\n",
         "# The Service object accept the complex dictionary, which is internally converted to JSON string.\n",
         "# The section 'data' contains the data frame in the form of dictionary.\n",
-        "test_sample = json.dumps({\"data\": json.loads(X_query.to_json(orient=\"records\"))})\n",
+        "test_sample = json.dumps({'data': X_query.to_dict(orient='records')})\n",
         "response = aci_service.run(input_data = test_sample)\n",
         "# translate from networkese to datascientese\n",
         "try: \n",
@@ -806,7 +799,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.9"
+      "version": "3.6.8"
     },
     "tags": [
       "None"

how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/run_forecast.py

@@ -1,38 +0,0 @@
-import os
-import shutil
-from azureml.core import ScriptRunConfig
-
-
-def run_remote_inference(test_experiment, compute_target, train_run,
-                         test_dataset, target_column_name, inference_folder='./forecast'):
-    # Create local directory to copy the model.pkl and forecsting_script.py files into.
-    # These files will be uploaded to and executed on the compute instance.
-    os.makedirs(inference_folder, exist_ok=True)
-    shutil.copy('forecasting_script.py', inference_folder)
-
-    train_run.download_file('outputs/model.pkl',
-                            os.path.join(inference_folder, 'model.pkl'))
-
-    inference_env = train_run.get_environment()
-
-    config = ScriptRunConfig(source_directory=inference_folder,
-                             script='forecasting_script.py',
-                             arguments=['--target_column_name',
-                                        target_column_name,
-                                        '--test_dataset',
-                                        test_dataset.as_named_input(test_dataset.name)],
-                             compute_target=compute_target,
-                             environment=inference_env)
-
-    run = test_experiment.submit(config,
-                                 tags={'training_run_id':
-                                       train_run.id,
-                                       'run_algorithm':
-                                       train_run.properties['run_algorithm'],
-                                       'valid_score':
-                                       train_run.properties['score'],
-                                       'primary_metric':
-                                       train_run.properties['primary_metric']})
-
-    run.log("run_algorithm", run.tags['run_algorithm'])
-    return run

how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb

@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -215,6 +215,15 @@
         "#local_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "local_run"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -436,8 +445,7 @@
         "\n",
         "automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, X=X_train, \n",
         "                                                             X_test=X_test, y=y_train, \n",
-        "                                                             task='classification',\n",
-        "                                                             automl_run=automl_run)"
+        "                                                             task='classification')"
       ]
     },
     {
@@ -454,10 +462,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from interpret.ext.glassbox import LGBMExplainableModel\n",
         "from azureml.interpret.mimic_wrapper import MimicWrapper\n",
         "explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator,\n",
         "                         explainable_model=automl_explainer_setup_obj.surrogate_model, \n",
-        "                         init_dataset=automl_explainer_setup_obj.X_transform, run=automl_explainer_setup_obj.automl_run,\n",
+        "                         init_dataset=automl_explainer_setup_obj.X_transform, run=automl_run,\n",
         "                         features=automl_explainer_setup_obj.engineered_feature_names, \n",
         "                         feature_maps=[automl_explainer_setup_obj.feature_map],\n",
         "                         classes=automl_explainer_setup_obj.classes,\n",

how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb

@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -130,8 +130,6 @@
         "### Create or Attach existing AmlCompute\n",
         "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
         "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
-        "\n",
         "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
@@ -154,7 +152,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=4)\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
@@ -307,6 +305,15 @@
         "remote_run = experiment.submit(automl_config, show_output = False)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -441,7 +448,7 @@
         "\n",
         "### Retrieve any AutoML Model for explanations\n",
         "\n",
-        "Below we select an AutoML pipeline from our iterations. The `get_output` method returns the a AutoML run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for any logged `metric` or for a particular `iteration`."
+        "Below we select the some AutoML pipeline from our iterations. The `get_output` method returns the a AutoML run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
       ]
     },
     {
@@ -450,8 +457,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#automl_run, fitted_model = remote_run.get_output(metric='r2_score')\n",
-        "automl_run, fitted_model = remote_run.get_output(iteration=2)"
+        "automl_run, fitted_model = remote_run.get_output(metric='r2_score')"
       ]
     },
     {

how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/train_explainer.py

@@ -27,7 +27,7 @@ automl_run = Run(experiment=experiment, run_id='<<run_id>>')
 
 # Check if this AutoML model is explainable
 if not automl_check_model_if_explainable(automl_run):
-    raise Exception("Model explanations are currently not supported for " + automl_run.get_properties().get(
+    raise Exception("Model explanations is currently not supported for " + automl_run.get_properties().get(
         'run_algorithm'))
 
 # Download the best model from the artifact store
@@ -38,25 +38,23 @@ fitted_model = joblib.load('model.pkl')
 
 # Get the train dataset from the workspace
 train_dataset = Dataset.get_by_name(workspace=ws, name='<<train_dataset_name>>')
-# Drop the labeled column to get the training set.
+# Drop the lablled column to get the training set.
 X_train = train_dataset.drop_columns(columns=['<<target_column_name>>'])
 y_train = train_dataset.keep_columns(columns=['<<target_column_name>>'], validate=True)
 
-# Get the test dataset from the workspace
+# Get the train dataset from the workspace
 test_dataset = Dataset.get_by_name(workspace=ws, name='<<test_dataset_name>>')
-# Drop the labeled column to get the testing set.
+# Drop the lablled column to get the testing set.
 X_test = test_dataset.drop_columns(columns=['<<target_column_name>>'])
 
-# Setup the class for explaining the AutoML models
+# Setup the class for explaining the AtuoML models
 automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, '<<task>>',
                                                              X=X_train, X_test=X_test,
-                                                             y=y_train,
-                                                             automl_run=automl_run)
+                                                             y=y_train)
 
 # Initialize the Mimic Explainer
 explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMExplainableModel,
-                         init_dataset=automl_explainer_setup_obj.X_transform,
-                         run=automl_explainer_setup_obj.automl_run,
+                         init_dataset=automl_explainer_setup_obj.X_transform, run=automl_run,
                          features=automl_explainer_setup_obj.engineered_feature_names,
                          feature_maps=[automl_explainer_setup_obj.feature_map],
                          classes=automl_explainer_setup_obj.classes)

how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb

@@ -92,7 +92,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -145,7 +145,7 @@
         "    compute_target = 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_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=4)\n",
         "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
         "\n",
@@ -256,6 +256,15 @@
         "#remote_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -366,12 +375,18 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "y_test = test_data.keep_columns('ERP').to_pandas_dataframe()\n",
-        "test_data = test_data.drop_columns('ERP').to_pandas_dataframe()\n",
+        "# preview the first 3 rows of the dataset\n",
+        "\n",
+        "test_data = test_data.to_pandas_dataframe()\n",
+        "y_test = test_data['ERP'].fillna(0)\n",
+        "test_data = test_data.drop('ERP', 1)\n",
+        "test_data = test_data.fillna(0)\n",
         "\n",
         "\n",
-        "y_train = train_data.keep_columns('ERP').to_pandas_dataframe()\n",
-        "train_data = train_data.drop_columns('ERP').to_pandas_dataframe()\n"
+        "train_data = train_data.to_pandas_dataframe()\n",
+        "y_train = train_data['ERP'].fillna(0)\n",
+        "train_data = train_data.drop('ERP', 1)\n",
+        "train_data = train_data.fillna(0)\n"
       ]
     },
     {
@@ -381,10 +396,10 @@
       "outputs": [],
       "source": [
         "y_pred_train = fitted_model.predict(train_data)\n",
-        "y_residual_train = y_train.values - y_pred_train\n",
+        "y_residual_train = y_train - y_pred_train\n",
         "\n",
         "y_pred_test = fitted_model.predict(test_data)\n",
-        "y_residual_test = y_test.values - y_pred_test"
+        "y_residual_test = y_test - y_pred_test"
       ]
     },
     {

how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb

@@ -350,6 +350,32 @@
         "displayHTML(\"<a href={} target='_blank'>Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
       ]
     },
+    {
+      "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",
+        "    #print(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": {},

how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb

@@ -352,6 +352,32 @@
         "displayHTML(\"<a href={} target='_blank'>Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
       ]
     },
+    {
+      "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",
+        "    #print(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": {},

how-to-use-azureml/azure-synapse/README.md

@@ -1,84 +0,0 @@
-Azure Synapse Analytics is a limitless analytics service that brings together data integration, enterprise data warehousing, and big data analytics. It gives you the freedom to query data on your terms, using either serverless or dedicated resources—at scale. Azure Synapse brings these worlds together with a unified experience to ingest, explore, prepare, manage, and serve data for immediate BI and machine learning needs. A core offering within Azure Synapse Analytics are serverless Apache Spark pools enhanced for big data workloads.  
-
-Synapse in Aml integration is for customers who want to use Apache Spark in Azure Synapse Analytics to prepare data at scale in Azure ML before training their ML model. This will allow customers to work on their end-to-end ML lifecycle including large-scale data preparation, model training and deployment within Azure ML workspace without having to use suboptimal tools for machine learning or switch between multiple tools for data preparation and model training. The ability to perform all ML tasks within Azure ML will reduce time required for customers to iterate on a machine learning project which typically includes multiple rounds of data preparation and training.
-
-In the public preview, the capabilities are provided:
-
-- Link Azure Synapse Analytics workspace to Azure Machine Learning workspace (via ARM, UI or SDK) 
-- Attach Apache Spark pools powered by Azure Synapse Analytics as Azure Machine Learning compute targets (via ARM, UI or SDK) 
-- Launch Apache Spark sessions in notebooks and perform interactive data exploration and preparation.  This interactive experience leverages Apache Spark magic and customers will have session-level Conda support to install packages. 
-- Productionize ML pipelines by leveraging Apache Spark pools to pre-process big data 
-
-# Using Synapse in Azure machine learning
-
-## Create synapse resources
-
-Follow up the documents to create Synapse workspace and resource-setup.sh is available for you to create the resources.
-
-- Create from [Portal](https://docs.microsoft.com/en-us/azure/synapse-analytics/quickstart-create-workspace)
-- Create from [Cli](https://docs.microsoft.com/en-us/azure/synapse-analytics/quickstart-create-workspace-cli)
-
-Follow up the documents to create Synapse spark pool
-
-- Create from [Portal](https://docs.microsoft.com/en-us/azure/synapse-analytics/quickstart-create-apache-spark-pool-portal)
-- Create from [Cli](https://docs.microsoft.com/en-us/cli/azure/ext/synapse/synapse/spark/pool?view=azure-cli-latest)
-
-## Link Synapse Workspace
-
-Make sure you are the owner of synapse workspace so that you can link synapse workspace into AML.
-You can run resource-setup.py to link the synapse workspace and attach compute
-
-```python
-from azureml.core import Workspace
-ws = Workspace.from_config()
-
-from azureml.core import LinkedService, SynapseWorkspaceLinkedServiceConfiguration
-synapse_link_config = SynapseWorkspaceLinkedServiceConfiguration(
-    subscription_id="<subscription id>",
-    resource_group="<resource group",
-    name="<synapse workspace name>"
-)
-
-linked_service = LinkedService.register(
-    workspace=ws,
-    name='<link name>',
-    linked_service_config=synapse_link_config)
-
-```
-
-## Attach synapse spark pool as AzureML compute
-
-```python
-
-from azureml.core.compute import SynapseCompute, ComputeTarget
-spark_pool_name = "<spark pool name>"
-attached_synapse_name = "<attached compute name>"
-
-attach_config = SynapseCompute.attach_configuration(
-        linked_service,
-        type="SynapseSpark",
-        pool_name=spark_pool_name)
-
-synapse_compute=ComputeTarget.attach(
-        workspace=ws,
-        name=attached_synapse_name,
-        attach_configuration=attach_config)
-
-synapse_compute.wait_for_completion()
-```
-
-## Set up permission
-
-Grant Spark admin role to system assigned identity of the linked service so that the user can submit experiment run or pipeline run from AML workspace to synapse spark pool.
-
-Grant Spark admin role to the specific user so that the user can start spark session to synapse spark pool.
-
-You can get the system assigned identity information by running
-
-```python
-print(linked_service.system_assigned_identity_principal_id)
-```
-
-- Launch synapse studio of the synapse workspace and grant linked service MSI "Synapse Apache Spark administrator" role.
-
-- In azure portal grant linked service MSI "Storage Blob Data Contributor" role of the primary adlsgen2 account of synapse workspace to use the library management feature.

how-to-use-azureml/azure-synapse/Synapse_Job_Scala_Support.ipynb

@@ -1,186 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved. \n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-arcadia/Synapse_Job_Scala_Support.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Get AML workspace which has synapse spark pool attached"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace, Experiment, Dataset, Environment\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": [
-        "## Leverage ScriptRunConfig to submit scala job to an attached synapse spark cluster"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Prepare data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.datastore import Datastore\n",
-        "# Use the default blob storage\n",
-        "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
-        "\n",
-        "# We are uploading a sample file in the local directory to be used as a datasource\n",
-        "file_name = \"shakespeare.txt\"\n",
-        "def_blob_store.upload_files(files=[\"./{}\".format(file_name)], overwrite=False)\n",
-        "\n",
-        "# Create file dataset\n",
-        "file_dataset = Dataset.File.from_files(path=[(def_blob_store, file_name)])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.data import HDFSOutputDatasetConfig\n",
-        "import uuid\n",
-        "\n",
-        "run_config = RunConfiguration(framework=\"pyspark\")\n",
-        "run_config.target = \"link-pool\"\n",
-        "run_config.spark.configuration[\"spark.driver.memory\"] = \"2g\"\n",
-        "run_config.spark.configuration[\"spark.driver.cores\"] = 2\n",
-        "run_config.spark.configuration[\"spark.executor.memory\"] = \"2g\"\n",
-        "run_config.spark.configuration[\"spark.executor.cores\"] = 1\n",
-        "run_config.spark.configuration[\"spark.executor.instances\"] = 1\n",
-        "# This can be removed if you are using local jars in source folder\n",
-        "run_config.spark.configuration[\"spark.yarn.dist.jars\"]=\"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/wordcount.jar\"\n",
-        "\n",
-        "dir_name = \"wordcount-{}\".format(str(uuid.uuid4()))\n",
-        "input = file_dataset.as_named_input(\"input\").as_hdfs()\n",
-        "output = HDFSOutputDatasetConfig(destination=(ws.get_default_datastore(), \"{}/result\".format(dir_name)))\n",
-        "\n",
-        "from azureml.core import ScriptRunConfig\n",
-        "args = ['--input', input, '--output', output]\n",
-        "script_run_config = ScriptRunConfig(source_directory = '.',\n",
-        "                                    script= 'start_script.py',\n",
-        "                                    arguments= args,\n",
-        "                                    run_config = run_config)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Experiment\n",
-        "exp = Experiment(workspace=ws, name='synapse-spark')\n",
-        "run = exp.submit(config=script_run_config)\n",
-        "run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Leverage SynapseSparkStep in an AML pipeline to add dataprep step on synapse spark cluster"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.pipeline.core import Pipeline\n",
-        "from azureml.pipeline.steps import SynapseSparkStep\n",
-        "\n",
-        "configs = {}\n",
-        "#configs[\"spark.yarn.dist.jars\"] = \"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/wordcount.jar\"\n",
-        "step_1 = SynapseSparkStep(name = 'synapse-spark',\n",
-        "                          file = 'start_script.py',\n",
-        "                          jars = \"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/wordcount.jar\",\n",
-        "                          source_directory=\".\",\n",
-        "                          arguments = args,\n",
-        "                          compute_target = 'link-pool',\n",
-        "                          driver_memory = \"2g\",\n",
-        "                          driver_cores = 2,\n",
-        "                          executor_memory = \"2g\",\n",
-        "                          executor_cores = 1,\n",
-        "                          num_executors = 1,\n",
-        "                          conf = configs)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "pipeline = Pipeline(workspace=ws, steps=[step_1])\n",
-        "pipeline_run = pipeline.submit('synapse-pipeline', regenerate_outputs=True)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "feli1"
-      }
-    ],
-    "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": "3.6.5"
-    },
-    "nteract": {
-      "version": "0.28.0"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/azure-synapse/Synapse_Session_Scala_Support.ipynb

@@ -1,240 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved. \n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-arcadia/Synapse_Session_Scala_Support.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Interactive Spark Session on Synapse Spark Pool"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "!pip install -U \"azureml-synapse\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "For JupyterLab, please additionally run:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "!jupyter lab build --minimize=False"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## PLEASE restart kernel and then refresh web page before starting spark session."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 0. Magic Usage"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2020-06-05T03:22:14.965395Z",
-          "iopub.status.busy": "2020-06-05T03:22:14.965395Z",
-          "iopub.status.idle": "2020-06-05T03:22:14.970398Z",
-          "shell.execute_reply": "2020-06-05T03:22:14.969397Z",
-          "shell.execute_reply.started": "2020-06-05T03:22:14.965395Z"
-        },
-        "gather": {
-          "logged": 1615594584642
-        }
-      },
-      "outputs": [],
-      "source": [
-        "# show help\n",
-        "%synapse ?"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 1. Start Synapse Session"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "gather": {
-          "logged": 1615577715289
-        }
-      },
-      "outputs": [],
-      "source": [
-        "%synapse start -c linktestpool --start-timeout 1000"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "nteract": {
-          "transient": {
-            "deleting": false
-          }
-        }
-      },
-      "source": [
-        "# 2. Use Scala"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "nteract": {
-          "transient": {
-            "deleting": false
-          }
-        }
-      },
-      "source": [
-        "## (1) Read Data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "jupyter": {
-          "outputs_hidden": false,
-          "source_hidden": false
-        },
-        "nteract": {
-          "transient": {
-            "deleting": false
-          }
-        }
-      },
-      "outputs": [],
-      "source": [
-        "%%synapse scala\n",
-        "\n",
-        "var df = spark.read.option(\"header\", \"true\").csv(\"wasbs://demo@dprepdata.blob.core.windows.net/Titanic.csv\")\n",
-        "df.show(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## (2) Use Scala Sql"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "jupyter": {
-          "outputs_hidden": false,
-          "source_hidden": false
-        },
-        "nteract": {
-          "transient": {
-            "deleting": false
-          }
-        }
-      },
-      "outputs": [],
-      "source": [
-        "%%synapse scala\n",
-        "\n",
-        "df.createOrReplaceTempView(\"titanic\")\n",
-        "var sqlDF = spark.sql(\"SELECT Name, Fare from titanic\")\n",
-        "sqlDF.show(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Stop Session"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "jupyter": {
-          "outputs_hidden": false,
-          "source_hidden": false
-        },
-        "nteract": {
-          "transient": {
-            "deleting": false
-          }
-        }
-      },
-      "outputs": [],
-      "source": [
-        "%synapse stop"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "feli1"
-      }
-    ],
-    "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": "3.6.5"
-    },
-    "nteract": {
-      "version": "0.28.0"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 4
-}

how-to-use-azureml/azure-synapse/Titanic.csv

@@ -1,892 +0,0 @@
-PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
-1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
-2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
-3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
-4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
-5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
-6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
-7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
-8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
-9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
-10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
-11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
-12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
-13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
-14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
-15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
-16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
-17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
-18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
-19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
-20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
-21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
-22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
-23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
-24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
-25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
-26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
-27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
-28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
-29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
-30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
-31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
-32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
-33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
-34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
-35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
-36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
-37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
-38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
-39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
-40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
-41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
-42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
-43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
-44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
-45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
-46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
-47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
-48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
-49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
-50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
-51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
-52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
-53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
-54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
-55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
-56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
-57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
-58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
-59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
-60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
-61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
-62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
-63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
-64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
-65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
-66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
-67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
-68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
-69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
-70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
-71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
-72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
-73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
-74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
-75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
-76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
-77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
-78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
-79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
-80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
-81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
-82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
-83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
-84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
-85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
-86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
-87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
-88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
-89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
-90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
-91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
-92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
-93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
-94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
-95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
-96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
-97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
-98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
-99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
-100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
-101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
-102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
-103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S
-104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S
-105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S
-106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
-107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
-108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
-109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
-110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
-111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
-112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
-113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
-114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
-115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
-116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
-117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
-118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
-119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
-120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
-121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
-122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
-123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
-124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
-125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
-126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
-127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
-128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
-129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
-130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
-131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
-132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
-133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S
-134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S
-135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S
-136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C
-137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S
-138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S
-139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S
-140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C
-141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
-142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
-143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
-144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
-145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
-146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
-147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S
-148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S
-149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S
-150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S
-151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S
-152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S
-153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S
-154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S
-155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S
-156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
-157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q
-158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S
-159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S
-160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S
-161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S
-162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S
-163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S
-164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S
-165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S
-166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S
-167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S
-168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S
-169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S
-170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S
-171,0,1,"Van der hoef, Mr. Wyckoff",male,61,0,0,111240,33.5,B19,S
-172,0,3,"Rice, Master. Arthur",male,4,4,1,382652,29.125,,Q
-173,1,3,"Johnson, Miss. Eleanor Ileen",female,1,1,1,347742,11.1333,,S
-174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S
-175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C
-176,0,3,"Klasen, Mr. Klas Albin",male,18,1,1,350404,7.8542,,S
-177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S
-178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C
-179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S
-180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S
-181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S
-182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C
-183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S
-184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S
-185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4,0,2,315153,22.025,,S
-186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50,A32,S
-187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q
-188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S
-189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q
-190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S
-191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S
-192,0,2,"Carbines, Mr. William",male,19,0,0,28424,13,,S
-193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19,1,0,350046,7.8542,,S
-194,1,2,"Navratil, Master. Michel M",male,3,1,1,230080,26,F2,S
-195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44,0,0,PC 17610,27.7208,B4,C
-196,1,1,"Lurette, Miss. Elise",female,58,0,0,PC 17569,146.5208,B80,C
-197,0,3,"Mernagh, Mr. Robert",male,,0,0,368703,7.75,,Q
-198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42,0,1,4579,8.4042,,S
-199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q
-200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S
-201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28,0,0,345770,9.5,,S
-202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S
-203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S
-204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C
-205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S
-206,0,3,"Strom, Miss. Telma Matilda",female,2,0,1,347054,10.4625,G6,S
-207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S
-208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C
-209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q
-210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C
-211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S
-212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S
-213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S
-214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S
-215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q
-216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C
-217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S
-218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42,1,0,243847,27,,S
-219,1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C
-220,0,2,"Harris, Mr. Walter",male,30,0,0,W/C 14208,10.5,,S
-221,1,3,"Sunderland, Mr. Victor Francis",male,16,0,0,SOTON/OQ 392089,8.05,,S
-222,0,2,"Bracken, Mr. James H",male,27,0,0,220367,13,,S
-223,0,3,"Green, Mr. George Henry",male,51,0,0,21440,8.05,,S
-224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S
-225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38,1,0,19943,90,C93,S
-226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22,0,0,PP 4348,9.35,,S
-227,1,2,"Mellors, Mr. William John",male,19,0,0,SW/PP 751,10.5,,S
-228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S
-229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18,0,0,236171,13,,S
-230,0,3,"Lefebre, Miss. Mathilde",female,,3,1,4133,25.4667,,S
-231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35,1,0,36973,83.475,C83,S
-232,0,3,"Larsson, Mr. Bengt Edvin",male,29,0,0,347067,7.775,,S
-233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59,0,0,237442,13.5,,S
-234,1,3,"Asplund, Miss. Lillian Gertrud",female,5,4,2,347077,31.3875,,S
-235,0,2,"Leyson, Mr. Robert William Norman",male,24,0,0,C.A. 29566,10.5,,S
-236,0,3,"Harknett, Miss. Alice Phoebe",female,,0,0,W./C. 6609,7.55,,S
-237,0,2,"Hold, Mr. Stephen",male,44,1,0,26707,26,,S
-238,1,2,"Collyer, Miss. Marjorie ""Lottie""",female,8,0,2,C.A. 31921,26.25,,S
-239,0,2,"Pengelly, Mr. Frederick William",male,19,0,0,28665,10.5,,S
-240,0,2,"Hunt, Mr. George Henry",male,33,0,0,SCO/W 1585,12.275,,S
-241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C
-242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q
-243,0,2,"Coleridge, Mr. Reginald Charles",male,29,0,0,W./C. 14263,10.5,,S
-244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22,0,0,STON/O 2. 3101275,7.125,,S
-245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C
-246,0,1,"Minahan, Dr. William Edward",male,44,2,0,19928,90,C78,Q
-247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25,0,0,347071,7.775,,S
-248,1,2,"Hamalainen, Mrs. William (Anna)",female,24,0,2,250649,14.5,,S
-249,1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S
-250,0,2,"Carter, Rev. Ernest Courtenay",male,54,1,0,244252,26,,S
-251,0,3,"Reed, Mr. James George",male,,0,0,362316,7.25,,S
-252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S
-253,0,1,"Stead, Mr. William Thomas",male,62,0,0,113514,26.55,C87,S
-254,0,3,"Lobb, Mr. William Arthur",male,30,1,0,A/5. 3336,16.1,,S
-255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S
-256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C
-257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C
-258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S
-259,1,1,"Ward, Miss. Anna",female,35,0,0,PC 17755,512.3292,,C
-260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S
-261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q
-262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S
-263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S
-264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S
-265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q
-266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S
-267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S
-268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S
-269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S
-270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S
-271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S
-272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S
-273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S
-274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C
-275,1,3,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q
-276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S
-277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S
-278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S
-279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q
-280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S
-281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q
-282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S
-283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S
-284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S
-285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S
-286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C
-287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S
-288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S
-289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S
-290,1,3,"Connolly, Miss. Kate",female,22,0,0,370373,7.75,,Q
-291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S
-292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C
-293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C
-294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S
-295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S
-296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C
-297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C
-298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S
-299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S
-300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C
-301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q
-302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q
-303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S
-304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q
-305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S
-306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S
-307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C
-308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17,1,0,PC 17758,108.9,C65,C
-309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C
-310,1,1,"Francatelli, Miss. Laura Mabel",female,30,0,0,PC 17485,56.9292,E36,C
-311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C
-312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C
-313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S
-314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S
-315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S
-316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S
-317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S
-318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S
-319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S
-320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C
-321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S
-322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S
-323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q
-324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S
-325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S
-326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C
-327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S
-328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S
-329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S
-330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C
-331,1,3,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q
-332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S
-333,0,1,"Graham, Mr. George Edward",male,38,0,1,PC 17582,153.4625,C91,S
-334,0,3,"Vander Planke, Mr. Leo Edmondus",male,16,2,0,345764,18,,S
-335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S
-336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S
-337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S
-338,1,1,"Burns, Miss. Elizabeth Margaret",female,41,0,0,16966,134.5,E40,C
-339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S
-340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S
-341,1,2,"Navratil, Master. Edmond Roger",male,2,1,1,230080,26,F2,S
-342,1,1,"Fortune, Miss. Alice Elizabeth",female,24,3,2,19950,263,C23 C25 C27,S
-343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S
-344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S
-345,0,2,"Fox, Mr. Stanley Hubert",male,36,0,0,229236,13,,S
-346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S
-347,1,2,"Smith, Miss. Marion Elsie",female,40,0,0,31418,13,,S
-348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S
-349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S
-350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S
-351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S
-352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S
-353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C
-354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S
-355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C
-356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S
-357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S
-358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S
-359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q
-360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q
-361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S
-362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C
-363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C
-364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S
-365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q
-366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S
-367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60,1,0,110813,75.25,D37,C
-368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C
-369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q
-370,1,1,"Aubart, Mme. Leontine Pauline",female,24,0,0,PC 17477,69.3,B35,C
-371,1,1,"Harder, Mr. George Achilles",male,25,1,0,11765,55.4417,E50,C
-372,0,3,"Wiklund, Mr. Jakob Alfred",male,18,1,0,3101267,6.4958,,S
-373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S
-374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C
-375,0,3,"Palsson, Miss. Stina Viola",female,3,3,1,349909,21.075,,S
-376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C
-377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S
-378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C
-379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C
-380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S
-381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C
-382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C
-383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S
-384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35,1,0,113789,52,,S
-385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S
-386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S
-387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S
-388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S
-389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q
-390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C
-391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S
-392,1,3,"Jansson, Mr. Carl Olof",male,21,0,0,350034,7.7958,,S
-393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S
-394,1,1,"Newell, Miss. Marjorie",female,23,1,0,35273,113.275,D36,C
-395,1,3,"Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)",female,24,0,2,PP 9549,16.7,G6,S
-396,0,3,"Johansson, Mr. Erik",male,22,0,0,350052,7.7958,,S
-397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S
-398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S
-399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S
-400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S
-401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S
-402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S
-403,0,3,"Jussila, Miss. Mari Aina",female,21,1,0,4137,9.825,,S
-404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S
-405,0,3,"Oreskovic, Miss. Marija",female,20,0,0,315096,8.6625,,S
-406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S
-407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.75,,S
-408,1,2,"Richards, Master. William Rowe",male,3,1,1,29106,18.75,,S
-409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S
-410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S
-411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S
-412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q
-413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q
-414,0,2,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0,,S
-415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S
-416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S
-417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S
-418,1,2,"Silven, Miss. Lyyli Karoliina",female,18,0,2,250652,13,,S
-419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S
-420,0,3,"Van Impe, Miss. Catharina",female,10,0,2,345773,24.15,,S
-421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C
-422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q
-423,0,3,"Zimmerman, Mr. Leo",male,29,0,0,315082,7.875,,S
-424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S
-425,0,3,"Rosblom, Mr. Viktor Richard",male,18,1,1,370129,20.2125,,S
-426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S
-427,1,2,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28,1,0,2003,26,,S
-428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S
-429,0,3,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q
-430,1,3,"Pickard, Mr. Berk (Berk Trembisky)",male,32,0,0,SOTON/O.Q. 392078,8.05,E10,S
-431,1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28,0,0,110564,26.55,C52,S
-432,1,3,"Thorneycroft, Mrs. Percival (Florence Kate White)",female,,1,0,376564,16.1,,S
-433,1,2,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",female,42,1,0,SC/AH 3085,26,,S
-434,0,3,"Kallio, Mr. Nikolai Erland",male,17,0,0,STON/O 2. 3101274,7.125,,S
-435,0,1,"Silvey, Mr. William Baird",male,50,1,0,13507,55.9,E44,S
-436,1,1,"Carter, Miss. Lucile Polk",female,14,1,2,113760,120,B96 B98,S
-437,0,3,"Ford, Miss. Doolina Margaret ""Daisy""",female,21,2,2,W./C. 6608,34.375,,S
-438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S
-439,0,1,"Fortune, Mr. Mark",male,64,1,4,19950,263,C23 C25 C27,S
-440,0,2,"Kvillner, Mr. Johan Henrik Johannesson",male,31,0,0,C.A. 18723,10.5,,S
-441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45,1,1,F.C.C. 13529,26.25,,S
-442,0,3,"Hampe, Mr. Leon",male,20,0,0,345769,9.5,,S
-443,0,3,"Petterson, Mr. Johan Emil",male,25,1,0,347076,7.775,,S
-444,1,2,"Reynaldo, Ms. Encarnacion",female,28,0,0,230434,13,,S
-445,1,3,"Johannesen-Bratthammer, Mr. Bernt",male,,0,0,65306,8.1125,,S
-446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S
-447,1,2,"Mellinger, Miss. Madeleine Violet",female,13,0,1,250644,19.5,,S
-448,1,1,"Seward, Mr. Frederic Kimber",male,34,0,0,113794,26.55,,S
-449,1,3,"Baclini, Miss. Marie Catherine",female,5,2,1,2666,19.2583,,C
-450,1,1,"Peuchen, Major. Arthur Godfrey",male,52,0,0,113786,30.5,C104,S
-451,0,2,"West, Mr. Edwy Arthur",male,36,1,2,C.A. 34651,27.75,,S
-452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S
-453,0,1,"Foreman, Mr. Benjamin Laventall",male,30,0,0,113051,27.75,C111,C
-454,1,1,"Goldenberg, Mr. Samuel L",male,49,1,0,17453,89.1042,C92,C
-455,0,3,"Peduzzi, Mr. Joseph",male,,0,0,A/5 2817,8.05,,S
-456,1,3,"Jalsevac, Mr. Ivan",male,29,0,0,349240,7.8958,,C
-457,0,1,"Millet, Mr. Francis Davis",male,65,0,0,13509,26.55,E38,S
-458,1,1,"Kenyon, Mrs. Frederick R (Marion)",female,,1,0,17464,51.8625,D21,S
-459,1,2,"Toomey, Miss. Ellen",female,50,0,0,F.C.C. 13531,10.5,,S
-460,0,3,"O'Connor, Mr. Maurice",male,,0,0,371060,7.75,,Q
-461,1,1,"Anderson, Mr. Harry",male,48,0,0,19952,26.55,E12,S
-462,0,3,"Morley, Mr. William",male,34,0,0,364506,8.05,,S
-463,0,1,"Gee, Mr. Arthur H",male,47,0,0,111320,38.5,E63,S
-464,0,2,"Milling, Mr. Jacob Christian",male,48,0,0,234360,13,,S
-465,0,3,"Maisner, Mr. Simon",male,,0,0,A/S 2816,8.05,,S
-466,0,3,"Goncalves, Mr. Manuel Estanslas",male,38,0,0,SOTON/O.Q. 3101306,7.05,,S
-467,0,2,"Campbell, Mr. William",male,,0,0,239853,0,,S
-468,0,1,"Smart, Mr. John Montgomery",male,56,0,0,113792,26.55,,S
-469,0,3,"Scanlan, Mr. James",male,,0,0,36209,7.725,,Q
-470,1,3,"Baclini, Miss. Helene Barbara",female,0.75,2,1,2666,19.2583,,C
-471,0,3,"Keefe, Mr. Arthur",male,,0,0,323592,7.25,,S
-472,0,3,"Cacic, Mr. Luka",male,38,0,0,315089,8.6625,,S
-473,1,2,"West, Mrs. Edwy Arthur (Ada Mary Worth)",female,33,1,2,C.A. 34651,27.75,,S
-474,1,2,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23,0,0,SC/AH Basle 541,13.7917,D,C
-475,0,3,"Strandberg, Miss. Ida Sofia",female,22,0,0,7553,9.8375,,S
-476,0,1,"Clifford, Mr. George Quincy",male,,0,0,110465,52,A14,S
-477,0,2,"Renouf, Mr. Peter Henry",male,34,1,0,31027,21,,S
-478,0,3,"Braund, Mr. Lewis Richard",male,29,1,0,3460,7.0458,,S
-479,0,3,"Karlsson, Mr. Nils August",male,22,0,0,350060,7.5208,,S
-480,1,3,"Hirvonen, Miss. Hildur E",female,2,0,1,3101298,12.2875,,S
-481,0,3,"Goodwin, Master. Harold Victor",male,9,5,2,CA 2144,46.9,,S
-482,0,2,"Frost, Mr. Anthony Wood ""Archie""",male,,0,0,239854,0,,S
-483,0,3,"Rouse, Mr. Richard Henry",male,50,0,0,A/5 3594,8.05,,S
-484,1,3,"Turkula, Mrs. (Hedwig)",female,63,0,0,4134,9.5875,,S
-485,1,1,"Bishop, Mr. Dickinson H",male,25,1,0,11967,91.0792,B49,C
-486,0,3,"Lefebre, Miss. Jeannie",female,,3,1,4133,25.4667,,S
-487,1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35,1,0,19943,90,C93,S
-488,0,1,"Kent, Mr. Edward Austin",male,58,0,0,11771,29.7,B37,C
-489,0,3,"Somerton, Mr. Francis William",male,30,0,0,A.5. 18509,8.05,,S
-490,1,3,"Coutts, Master. Eden Leslie ""Neville""",male,9,1,1,C.A. 37671,15.9,,S
-491,0,3,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S
-492,0,3,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.25,,S
-493,0,1,"Molson, Mr. Harry Markland",male,55,0,0,113787,30.5,C30,S
-494,0,1,"Artagaveytia, Mr. Ramon",male,71,0,0,PC 17609,49.5042,,C
-495,0,3,"Stanley, Mr. Edward Roland",male,21,0,0,A/4 45380,8.05,,S
-496,0,3,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C
-497,1,1,"Eustis, Miss. Elizabeth Mussey",female,54,1,0,36947,78.2667,D20,C
-498,0,3,"Shellard, Mr. Frederick William",male,,0,0,C.A. 6212,15.1,,S
-499,0,1,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25,1,2,113781,151.55,C22 C26,S
-500,0,3,"Svensson, Mr. Olof",male,24,0,0,350035,7.7958,,S
-501,0,3,"Calic, Mr. Petar",male,17,0,0,315086,8.6625,,S
-502,0,3,"Canavan, Miss. Mary",female,21,0,0,364846,7.75,,Q
-503,0,3,"O'Sullivan, Miss. Bridget Mary",female,,0,0,330909,7.6292,,Q
-504,0,3,"Laitinen, Miss. Kristina Sofia",female,37,0,0,4135,9.5875,,S
-505,1,1,"Maioni, Miss. Roberta",female,16,0,0,110152,86.5,B79,S
-506,0,1,"Penasco y Castellana, Mr. Victor de Satode",male,18,1,0,PC 17758,108.9,C65,C
-507,1,2,"Quick, Mrs. Frederick Charles (Jane Richards)",female,33,0,2,26360,26,,S
-508,1,1,"Bradley, Mr. George (""George Arthur Brayton"")",male,,0,0,111427,26.55,,S
-509,0,3,"Olsen, Mr. Henry Margido",male,28,0,0,C 4001,22.525,,S
-510,1,3,"Lang, Mr. Fang",male,26,0,0,1601,56.4958,,S
-511,1,3,"Daly, Mr. Eugene Patrick",male,29,0,0,382651,7.75,,Q
-512,0,3,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.05,,S
-513,1,1,"McGough, Mr. James Robert",male,36,0,0,PC 17473,26.2875,E25,S
-514,1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54,1,0,PC 17603,59.4,,C
-515,0,3,"Coleff, Mr. Satio",male,24,0,0,349209,7.4958,,S
-516,0,1,"Walker, Mr. William Anderson",male,47,0,0,36967,34.0208,D46,S
-517,1,2,"Lemore, Mrs. (Amelia Milley)",female,34,0,0,C.A. 34260,10.5,F33,S
-518,0,3,"Ryan, Mr. Patrick",male,,0,0,371110,24.15,,Q
-519,1,2,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36,1,0,226875,26,,S
-520,0,3,"Pavlovic, Mr. Stefo",male,32,0,0,349242,7.8958,,S
-521,1,1,"Perreault, Miss. Anne",female,30,0,0,12749,93.5,B73,S
-522,0,3,"Vovk, Mr. Janko",male,22,0,0,349252,7.8958,,S
-523,0,3,"Lahoud, Mr. Sarkis",male,,0,0,2624,7.225,,C
-524,1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44,0,1,111361,57.9792,B18,C
-525,0,3,"Kassem, Mr. Fared",male,,0,0,2700,7.2292,,C
-526,0,3,"Farrell, Mr. James",male,40.5,0,0,367232,7.75,,Q
-527,1,2,"Ridsdale, Miss. Lucy",female,50,0,0,W./C. 14258,10.5,,S
-528,0,1,"Farthing, Mr. John",male,,0,0,PC 17483,221.7792,C95,S
-529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S
-530,0,2,"Hocking, Mr. Richard George",male,23,2,1,29104,11.5,,S
-531,1,2,"Quick, Miss. Phyllis May",female,2,1,1,26360,26,,S
-532,0,3,"Toufik, Mr. Nakli",male,,0,0,2641,7.2292,,C
-533,0,3,"Elias, Mr. Joseph Jr",male,17,1,1,2690,7.2292,,C
-534,1,3,"Peter, Mrs. Catherine (Catherine Rizk)",female,,0,2,2668,22.3583,,C
-535,0,3,"Cacic, Miss. Marija",female,30,0,0,315084,8.6625,,S
-536,1,2,"Hart, Miss. Eva Miriam",female,7,0,2,F.C.C. 13529,26.25,,S
-537,0,1,"Butt, Major. Archibald Willingham",male,45,0,0,113050,26.55,B38,S
-538,1,1,"LeRoy, Miss. Bertha",female,30,0,0,PC 17761,106.425,,C
-539,0,3,"Risien, Mr. Samuel Beard",male,,0,0,364498,14.5,,S
-540,1,1,"Frolicher, Miss. Hedwig Margaritha",female,22,0,2,13568,49.5,B39,C
-541,1,1,"Crosby, Miss. Harriet R",female,36,0,2,WE/P 5735,71,B22,S
-542,0,3,"Andersson, Miss. Ingeborg Constanzia",female,9,4,2,347082,31.275,,S
-543,0,3,"Andersson, Miss. Sigrid Elisabeth",female,11,4,2,347082,31.275,,S
-544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S
-545,0,1,"Douglas, Mr. Walter Donald",male,50,1,0,PC 17761,106.425,C86,C
-546,0,1,"Nicholson, Mr. Arthur Ernest",male,64,0,0,693,26,,S
-547,1,2,"Beane, Mrs. Edward (Ethel Clarke)",female,19,1,0,2908,26,,S
-548,1,2,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C
-549,0,3,"Goldsmith, Mr. Frank John",male,33,1,1,363291,20.525,,S
-550,1,2,"Davies, Master. John Morgan Jr",male,8,1,1,C.A. 33112,36.75,,S
-551,1,1,"Thayer, Mr. John Borland Jr",male,17,0,2,17421,110.8833,C70,C
-552,0,2,"Sharp, Mr. Percival James R",male,27,0,0,244358,26,,S
-553,0,3,"O'Brien, Mr. Timothy",male,,0,0,330979,7.8292,,Q
-554,1,3,"Leeni, Mr. Fahim (""Philip Zenni"")",male,22,0,0,2620,7.225,,C
-555,1,3,"Ohman, Miss. Velin",female,22,0,0,347085,7.775,,S
-556,0,1,"Wright, Mr. George",male,62,0,0,113807,26.55,,S
-557,1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48,1,0,11755,39.6,A16,C
-558,0,1,"Robbins, Mr. Victor",male,,0,0,PC 17757,227.525,,C
-559,1,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",female,39,1,1,110413,79.65,E67,S
-560,1,3,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",female,36,1,0,345572,17.4,,S
-561,0,3,"Morrow, Mr. Thomas Rowan",male,,0,0,372622,7.75,,Q
-562,0,3,"Sivic, Mr. Husein",male,40,0,0,349251,7.8958,,S
-563,0,2,"Norman, Mr. Robert Douglas",male,28,0,0,218629,13.5,,S
-564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S
-565,0,3,"Meanwell, Miss. (Marion Ogden)",female,,0,0,SOTON/O.Q. 392087,8.05,,S
-566,0,3,"Davies, Mr. Alfred J",male,24,2,0,A/4 48871,24.15,,S
-567,0,3,"Stoytcheff, Mr. Ilia",male,19,0,0,349205,7.8958,,S
-568,0,3,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",female,29,0,4,349909,21.075,,S
-569,0,3,"Doharr, Mr. Tannous",male,,0,0,2686,7.2292,,C
-570,1,3,"Jonsson, Mr. Carl",male,32,0,0,350417,7.8542,,S
-571,1,2,"Harris, Mr. George",male,62,0,0,S.W./PP 752,10.5,,S
-572,1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53,2,0,11769,51.4792,C101,S
-573,1,1,"Flynn, Mr. John Irwin (""Irving"")",male,36,0,0,PC 17474,26.3875,E25,S
-574,1,3,"Kelly, Miss. Mary",female,,0,0,14312,7.75,,Q
-575,0,3,"Rush, Mr. Alfred George John",male,16,0,0,A/4. 20589,8.05,,S
-576,0,3,"Patchett, Mr. George",male,19,0,0,358585,14.5,,S
-577,1,2,"Garside, Miss. Ethel",female,34,0,0,243880,13,,S
-578,1,1,"Silvey, Mrs. William Baird (Alice Munger)",female,39,1,0,13507,55.9,E44,S
-579,0,3,"Caram, Mrs. Joseph (Maria Elias)",female,,1,0,2689,14.4583,,C
-580,1,3,"Jussila, Mr. Eiriik",male,32,0,0,STON/O 2. 3101286,7.925,,S
-581,1,2,"Christy, Miss. Julie Rachel",female,25,1,1,237789,30,,S
-582,1,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",female,39,1,1,17421,110.8833,C68,C
-583,0,2,"Downton, Mr. William James",male,54,0,0,28403,26,,S
-584,0,1,"Ross, Mr. John Hugo",male,36,0,0,13049,40.125,A10,C
-585,0,3,"Paulner, Mr. Uscher",male,,0,0,3411,8.7125,,C
-586,1,1,"Taussig, Miss. Ruth",female,18,0,2,110413,79.65,E68,S
-587,0,2,"Jarvis, Mr. John Denzil",male,47,0,0,237565,15,,S
-588,1,1,"Frolicher-Stehli, Mr. Maxmillian",male,60,1,1,13567,79.2,B41,C
-589,0,3,"Gilinski, Mr. Eliezer",male,22,0,0,14973,8.05,,S
-590,0,3,"Murdlin, Mr. Joseph",male,,0,0,A./5. 3235,8.05,,S
-591,0,3,"Rintamaki, Mr. Matti",male,35,0,0,STON/O 2. 3101273,7.125,,S
-592,1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52,1,0,36947,78.2667,D20,C
-593,0,3,"Elsbury, Mr. William James",male,47,0,0,A/5 3902,7.25,,S
-594,0,3,"Bourke, Miss. Mary",female,,0,2,364848,7.75,,Q
-595,0,2,"Chapman, Mr. John Henry",male,37,1,0,SC/AH 29037,26,,S
-596,0,3,"Van Impe, Mr. Jean Baptiste",male,36,1,1,345773,24.15,,S
-597,1,2,"Leitch, Miss. Jessie Wills",female,,0,0,248727,33,,S
-598,0,3,"Johnson, Mr. Alfred",male,49,0,0,LINE,0,,S
-599,0,3,"Boulos, Mr. Hanna",male,,0,0,2664,7.225,,C
-600,1,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",male,49,1,0,PC 17485,56.9292,A20,C
-601,1,2,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",female,24,2,1,243847,27,,S
-602,0,3,"Slabenoff, Mr. Petco",male,,0,0,349214,7.8958,,S
-603,0,1,"Harrington, Mr. Charles H",male,,0,0,113796,42.4,,S
-604,0,3,"Torber, Mr. Ernst William",male,44,0,0,364511,8.05,,S
-605,1,1,"Homer, Mr. Harry (""Mr E Haven"")",male,35,0,0,111426,26.55,,C
-606,0,3,"Lindell, Mr. Edvard Bengtsson",male,36,1,0,349910,15.55,,S
-607,0,3,"Karaic, Mr. Milan",male,30,0,0,349246,7.8958,,S
-608,1,1,"Daniel, Mr. Robert Williams",male,27,0,0,113804,30.5,,S
-609,1,2,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",female,22,1,2,SC/Paris 2123,41.5792,,C
-610,1,1,"Shutes, Miss. Elizabeth W",female,40,0,0,PC 17582,153.4625,C125,S
-611,0,3,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",female,39,1,5,347082,31.275,,S
-612,0,3,"Jardin, Mr. Jose Neto",male,,0,0,SOTON/O.Q. 3101305,7.05,,S
-613,1,3,"Murphy, Miss. Margaret Jane",female,,1,0,367230,15.5,,Q
-614,0,3,"Horgan, Mr. John",male,,0,0,370377,7.75,,Q
-615,0,3,"Brocklebank, Mr. William Alfred",male,35,0,0,364512,8.05,,S
-616,1,2,"Herman, Miss. Alice",female,24,1,2,220845,65,,S
-617,0,3,"Danbom, Mr. Ernst Gilbert",male,34,1,1,347080,14.4,,S
-618,0,3,"Lobb, Mrs. William Arthur (Cordelia K Stanlick)",female,26,1,0,A/5. 3336,16.1,,S
-619,1,2,"Becker, Miss. Marion Louise",female,4,2,1,230136,39,F4,S
-620,0,2,"Gavey, Mr. Lawrence",male,26,0,0,31028,10.5,,S
-621,0,3,"Yasbeck, Mr. Antoni",male,27,1,0,2659,14.4542,,C
-622,1,1,"Kimball, Mr. Edwin Nelson Jr",male,42,1,0,11753,52.5542,D19,S
-623,1,3,"Nakid, Mr. Sahid",male,20,1,1,2653,15.7417,,C
-624,0,3,"Hansen, Mr. Henry Damsgaard",male,21,0,0,350029,7.8542,,S
-625,0,3,"Bowen, Mr. David John ""Dai""",male,21,0,0,54636,16.1,,S
-626,0,1,"Sutton, Mr. Frederick",male,61,0,0,36963,32.3208,D50,S
-627,0,2,"Kirkland, Rev. Charles Leonard",male,57,0,0,219533,12.35,,Q
-628,1,1,"Longley, Miss. Gretchen Fiske",female,21,0,0,13502,77.9583,D9,S
-629,0,3,"Bostandyeff, Mr. Guentcho",male,26,0,0,349224,7.8958,,S
-630,0,3,"O'Connell, Mr. Patrick D",male,,0,0,334912,7.7333,,Q
-631,1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80,0,0,27042,30,A23,S
-632,0,3,"Lundahl, Mr. Johan Svensson",male,51,0,0,347743,7.0542,,S
-633,1,1,"Stahelin-Maeglin, Dr. Max",male,32,0,0,13214,30.5,B50,C
-634,0,1,"Parr, Mr. William Henry Marsh",male,,0,0,112052,0,,S
-635,0,3,"Skoog, Miss. Mabel",female,9,3,2,347088,27.9,,S
-636,1,2,"Davis, Miss. Mary",female,28,0,0,237668,13,,S
-637,0,3,"Leinonen, Mr. Antti Gustaf",male,32,0,0,STON/O 2. 3101292,7.925,,S
-638,0,2,"Collyer, Mr. Harvey",male,31,1,1,C.A. 31921,26.25,,S
-639,0,3,"Panula, Mrs. Juha (Maria Emilia Ojala)",female,41,0,5,3101295,39.6875,,S
-640,0,3,"Thorneycroft, Mr. Percival",male,,1,0,376564,16.1,,S
-641,0,3,"Jensen, Mr. Hans Peder",male,20,0,0,350050,7.8542,,S
-642,1,1,"Sagesser, Mlle. Emma",female,24,0,0,PC 17477,69.3,B35,C
-643,0,3,"Skoog, Miss. Margit Elizabeth",female,2,3,2,347088,27.9,,S
-644,1,3,"Foo, Mr. Choong",male,,0,0,1601,56.4958,,S
-645,1,3,"Baclini, Miss. Eugenie",female,0.75,2,1,2666,19.2583,,C
-646,1,1,"Harper, Mr. Henry Sleeper",male,48,1,0,PC 17572,76.7292,D33,C
-647,0,3,"Cor, Mr. Liudevit",male,19,0,0,349231,7.8958,,S
-648,1,1,"Simonius-Blumer, Col. Oberst Alfons",male,56,0,0,13213,35.5,A26,C
-649,0,3,"Willey, Mr. Edward",male,,0,0,S.O./P.P. 751,7.55,,S
-650,1,3,"Stanley, Miss. Amy Zillah Elsie",female,23,0,0,CA. 2314,7.55,,S
-651,0,3,"Mitkoff, Mr. Mito",male,,0,0,349221,7.8958,,S
-652,1,2,"Doling, Miss. Elsie",female,18,0,1,231919,23,,S
-653,0,3,"Kalvik, Mr. Johannes Halvorsen",male,21,0,0,8475,8.4333,,S
-654,1,3,"O'Leary, Miss. Hanora ""Norah""",female,,0,0,330919,7.8292,,Q
-655,0,3,"Hegarty, Miss. Hanora ""Nora""",female,18,0,0,365226,6.75,,Q
-656,0,2,"Hickman, Mr. Leonard Mark",male,24,2,0,S.O.C. 14879,73.5,,S
-657,0,3,"Radeff, Mr. Alexander",male,,0,0,349223,7.8958,,S
-658,0,3,"Bourke, Mrs. John (Catherine)",female,32,1,1,364849,15.5,,Q
-659,0,2,"Eitemiller, Mr. George Floyd",male,23,0,0,29751,13,,S
-660,0,1,"Newell, Mr. Arthur Webster",male,58,0,2,35273,113.275,D48,C
-661,1,1,"Frauenthal, Dr. Henry William",male,50,2,0,PC 17611,133.65,,S
-662,0,3,"Badt, Mr. Mohamed",male,40,0,0,2623,7.225,,C
-663,0,1,"Colley, Mr. Edward Pomeroy",male,47,0,0,5727,25.5875,E58,S
-664,0,3,"Coleff, Mr. Peju",male,36,0,0,349210,7.4958,,S
-665,1,3,"Lindqvist, Mr. Eino William",male,20,1,0,STON/O 2. 3101285,7.925,,S
-666,0,2,"Hickman, Mr. Lewis",male,32,2,0,S.O.C. 14879,73.5,,S
-667,0,2,"Butler, Mr. Reginald Fenton",male,25,0,0,234686,13,,S
-668,0,3,"Rommetvedt, Mr. Knud Paust",male,,0,0,312993,7.775,,S
-669,0,3,"Cook, Mr. Jacob",male,43,0,0,A/5 3536,8.05,,S
-670,1,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",female,,1,0,19996,52,C126,S
-671,1,2,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",female,40,1,1,29750,39,,S
-672,0,1,"Davidson, Mr. Thornton",male,31,1,0,F.C. 12750,52,B71,S
-673,0,2,"Mitchell, Mr. Henry Michael",male,70,0,0,C.A. 24580,10.5,,S
-674,1,2,"Wilhelms, Mr. Charles",male,31,0,0,244270,13,,S
-675,0,2,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0,,S
-676,0,3,"Edvardsson, Mr. Gustaf Hjalmar",male,18,0,0,349912,7.775,,S
-677,0,3,"Sawyer, Mr. Frederick Charles",male,24.5,0,0,342826,8.05,,S
-678,1,3,"Turja, Miss. Anna Sofia",female,18,0,0,4138,9.8417,,S
-679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43,1,6,CA 2144,46.9,,S
-680,1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36,0,1,PC 17755,512.3292,B51 B53 B55,C
-681,0,3,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q
-682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C
-683,0,3,"Olsvigen, Mr. Thor Anderson",male,20,0,0,6563,9.225,,S
-684,0,3,"Goodwin, Mr. Charles Edward",male,14,5,2,CA 2144,46.9,,S
-685,0,2,"Brown, Mr. Thomas William Solomon",male,60,1,1,29750,39,,S
-686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25,1,2,SC/Paris 2123,41.5792,,C
-687,0,3,"Panula, Mr. Jaako Arnold",male,14,4,1,3101295,39.6875,,S
-688,0,3,"Dakic, Mr. Branko",male,19,0,0,349228,10.1708,,S
-689,0,3,"Fischer, Mr. Eberhard Thelander",male,18,0,0,350036,7.7958,,S
-690,1,1,"Madill, Miss. Georgette Alexandra",female,15,0,1,24160,211.3375,B5,S
-691,1,1,"Dick, Mr. Albert Adrian",male,31,1,0,17474,57,B20,S
-692,1,3,"Karun, Miss. Manca",female,4,0,1,349256,13.4167,,C
-693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S
-694,0,3,"Saad, Mr. Khalil",male,25,0,0,2672,7.225,,C
-695,0,1,"Weir, Col. John",male,60,0,0,113800,26.55,,S
-696,0,2,"Chapman, Mr. Charles Henry",male,52,0,0,248731,13.5,,S
-697,0,3,"Kelly, Mr. James",male,44,0,0,363592,8.05,,S
-698,1,3,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q
-699,0,1,"Thayer, Mr. John Borland",male,49,1,1,17421,110.8833,C68,C
-700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42,0,0,348121,7.65,F G63,S
-701,1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18,1,0,PC 17757,227.525,C62 C64,C
-702,1,1,"Silverthorne, Mr. Spencer Victor",male,35,0,0,PC 17475,26.2875,E24,S
-703,0,3,"Barbara, Miss. Saiide",female,18,0,1,2691,14.4542,,C
-704,0,3,"Gallagher, Mr. Martin",male,25,0,0,36864,7.7417,,Q
-705,0,3,"Hansen, Mr. Henrik Juul",male,26,1,0,350025,7.8542,,S
-706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39,0,0,250655,26,,S
-707,1,2,"Kelly, Mrs. Florence ""Fannie""",female,45,0,0,223596,13.5,,S
-708,1,1,"Calderhead, Mr. Edward Pennington",male,42,0,0,PC 17476,26.2875,E24,S
-709,1,1,"Cleaver, Miss. Alice",female,22,0,0,113781,151.55,,S
-710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C
-711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C
-712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S
-713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S
-714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S
-715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S
-716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S
-717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C
-718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S
-719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q
-720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S
-721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S
-722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S
-723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S
-724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S
-725,1,1,"Chambers, Mr. Norman Campbell",male,27,1,0,113806,53.1,E8,S
-726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S
-727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S
-728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q
-729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S
-730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S
-731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S
-732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C
-733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S
-734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S
-735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S
-736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S
-737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S
-738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C
-739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S
-740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S
-741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S
-742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S
-743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C
-744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S
-745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S
-746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S
-747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S
-748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S
-749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S
-750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q
-751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S
-752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S
-753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S
-754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S
-755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S
-756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S
-757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S
-758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S
-759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S
-760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S
-761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S
-762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S
-763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C
-764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36,1,2,113760,120,B96 B98,S
-765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S
-766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S
-767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C
-768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q
-769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q
-770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S
-771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S
-772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S
-773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S
-774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C
-775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S
-776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S
-777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q
-778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S
-779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q
-780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S
-781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C
-782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S
-783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S
-784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S
-785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S
-786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S
-787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S
-788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q
-789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S
-790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C
-791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q
-792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S
-793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S
-794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C
-795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S
-796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S
-797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S
-798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S
-799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C
-800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S
-801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S
-802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S
-803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S
-804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C
-805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S
-806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S
-807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S
-808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S
-809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S
-810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S
-811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S
-812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S
-813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S
-814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S
-815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S
-816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S
-817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S
-818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C
-819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S
-820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S
-821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
-822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
-823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S
-824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S
-825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
-826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
-827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
-828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C
-829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
-830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
-831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C
-832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
-833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
-834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S
-835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S
-836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
-837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
-838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
-839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
-840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
-841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
-842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
-843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
-844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
-845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
-846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
-847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
-848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
-849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
-850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
-851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
-852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
-853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
-854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
-855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
-856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
-857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
-858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
-859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
-860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
-861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
-862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
-863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
-864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
-865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
-866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
-867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
-868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
-869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
-870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
-871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
-872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
-873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
-874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
-875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
-876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
-877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
-878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
-879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
-880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
-881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
-882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
-883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
-884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
-885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
-886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
-887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
-888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
-889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
-890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
-891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q

how-to-use-azureml/azure-synapse/shakespeare.txt

@@ -1,270 +0,0 @@
-This is the 100th Etext file presented by Project Gutenberg, and
-is presented in cooperation with World Library, Inc., from their
-Library of the Future and Shakespeare CDROMS.  Project Gutenberg
-often releases Etexts that are NOT placed in the Public Domain!!
-
-Shakespeare
-
-*This Etext has certain copyright implications you should read!*
-
-<<THIS ELECTRONIC VERSION OF THE COMPLETE WORKS OF WILLIAM
-SHAKESPEARE IS COPYRIGHT 1990-1993 BY WORLD LIBRARY, INC., AND IS
-PROVIDED BY PROJECT GUTENBERG ETEXT OF ILLINOIS BENEDICTINE COLLEGE
-WITH PERMISSION.  ELECTRONIC AND MACHINE READABLE COPIES MAY BE
-DISTRIBUTED SO LONG AS SUCH COPIES (1) ARE FOR YOUR OR OTHERS
-PERSONAL USE ONLY, AND (2) ARE NOT DISTRIBUTED OR USED
-COMMERCIALLY.  PROHIBITED COMMERCIAL DISTRIBUTION INCLUDES BY ANY
-SERVICE THAT CHARGES FOR DOWNLOAD TIME OR FOR MEMBERSHIP.>>
-
-*Project Gutenberg is proud to cooperate with The World Library*
-in the presentation of The Complete Works of William Shakespeare
-for your reading for education and entertainment.  HOWEVER, THIS
-IS NEITHER SHAREWARE NOR PUBLIC DOMAIN. . .AND UNDER THE LIBRARY
-OF THE FUTURE CONDITIONS OF THIS PRESENTATION. . .NO CHARGES MAY
-BE MADE FOR *ANY* ACCESS TO THIS MATERIAL.  YOU ARE ENCOURAGED!!
-TO GIVE IT AWAY TO ANYONE YOU LIKE, BUT NO CHARGES ARE ALLOWED!!
-
-
-**Welcome To The World of Free Plain Vanilla Electronic Texts**
-
-**Etexts Readable By Both Humans and By Computers, Since 1971**
-
-*These Etexts Prepared By Hundreds of Volunteers and Donations*
-
-Information on contacting Project Gutenberg to get Etexts, and
-further information is included below.  We need your donations.
-
-
-The Complete Works of William Shakespeare 
-
-January, 1994  [Etext #100]
-
-
-The Library of the Future Complete Works of William Shakespeare 
-Library of the Future is a TradeMark (TM) of World Library Inc.
-******This file should be named shaks12.txt or shaks12.zip*****
-
-Corrected EDITIONS of our etexts get a new NUMBER, shaks13.txt
-VERSIONS based on separate sources get new LETTER, shaks10a.txt
-
-If you would like further information about World Library, Inc.
-Please call them at 1-800-443-0238 or email julianc@netcom.com
-Please give them our thanks for their Shakespeare cooperation!
-
-
-The official release date of all Project Gutenberg Etexts is at
-Midnight, Central Time, of the last day of the stated month.  A
-preliminary version may often be posted for suggestion, comment
-and editing by those who wish to do so.  To be sure you have an
-up to date first edition [xxxxx10x.xxx] please check file sizes
-in the first week of the next month.  Since our ftp program has
-a bug in it that scrambles the date [tried to fix and failed] a
-look at the file size will have to do, but we will try to see a
-new copy has at least one byte more or less.
-
-
-Information about Project Gutenberg (one page)
-
-We produce about two million dollars for each hour we work.  The
-fifty hours is one conservative estimate for how long it we take
-to get any etext selected, entered, proofread, edited, copyright
-searched and analyzed, the copyright letters written, etc.  This
-projected audience is one hundred million readers.  If our value
-per text is nominally estimated at one dollar, then we produce 2
-million dollars per hour this year we, will have to do four text
-files per month:  thus upping our productivity from one million.
-The Goal of Project Gutenberg is to Give Away One Trillion Etext
-Files by the December 31, 2001.  [10,000 x 100,000,000=Trillion]
-This is ten thousand titles each to one hundred million readers,
-which is 10% of the expected number of computer users by the end
-of the year 2001.
-
-We need your donations more than ever!
-
-All donations should be made to "Project Gutenberg/IBC", and are
-tax deductible to the extent allowable by law ("IBC" is Illinois
-Benedictine College).  (Subscriptions to our paper newsletter go
-to IBC, too)
-
-For these and other matters, please mail to:
-
-Project Gutenberg
-P. O. Box  2782
-Champaign, IL 61825
-
-When all other email fails try our Michael S. Hart, Executive Director:
-hart@vmd.cso.uiuc.edu (internet)   hart@uiucvmd   (bitnet)
-
-We would prefer to send you this information by email
-(Internet, Bitnet, Compuserve, ATTMAIL or MCImail).
-
-******
-If you have an FTP program (or emulator), please
-FTP directly to the Project Gutenberg archives:
-[Mac users, do NOT point and click. . .type]
-
-ftp mrcnext.cso.uiuc.edu
-login:  anonymous
-password:  your@login
-cd etext/etext91
-or cd etext92
-or cd etext93 [for new books]  [now also in cd etext/etext93]
-or cd etext/articles [get suggest gut for more information]
-dir [to see files]
-get or mget [to get files. . .set bin for zip files]
-GET 0INDEX.GUT
-for a list of books
-and
-GET NEW GUT for general information
-and
-MGET GUT* for newsletters.
-
-**Information prepared by the Project Gutenberg legal advisor**
-
-
-***** SMALL PRINT! for COMPLETE SHAKESPEARE *****
-
-THIS ELECTRONIC VERSION OF THE COMPLETE WORKS OF WILLIAM
-SHAKESPEARE IS COPYRIGHT 1990-1993 BY WORLD LIBRARY, INC.,
-AND IS PROVIDED BY PROJECT GUTENBERG ETEXT OF
-ILLINOIS BENEDICTINE COLLEGE WITH PERMISSION.
-
-Since unlike many other Project Gutenberg-tm etexts, this etext
-is copyright protected, and since the materials and methods you
-use will effect the Project's reputation, your right to copy and
-distribute it is limited by the copyright and other laws, and by
-the conditions of this "Small Print!" statement.
-
-1.  LICENSE
-
-  A) YOU MAY (AND ARE ENCOURAGED) TO DISTRIBUTE ELECTRONIC AND
-MACHINE READABLE COPIES OF THIS ETEXT, SO LONG AS SUCH COPIES
-(1) ARE FOR YOUR OR OTHERS PERSONAL USE ONLY, AND (2) ARE NOT
-DISTRIBUTED OR USED COMMERCIALLY.  PROHIBITED COMMERCIAL
-DISTRIBUTION INCLUDES BY ANY SERVICE THAT CHARGES FOR DOWNLOAD
-TIME OR FOR MEMBERSHIP.
-
-  B) This license is subject to the conditions that you honor
-the refund and replacement provisions of this "small print!"
-statement; and that you distribute exact copies of this etext,
-including this Small Print statement.  Such copies can be
-compressed or any proprietary form (including any form resulting
-from word processing or hypertext software), so long as
-*EITHER*:
-
-    (1) The etext, when displayed, is clearly readable, and does
-  *not* contain characters other than those intended by the
-  author of the work, although tilde (~), asterisk (*) and
-  underline (_) characters may be used to convey punctuation
-  intended by the author, and additional characters may be used
-  to indicate hypertext links; OR
-
-    (2) The etext is readily convertible by the reader at no
-  expense into plain ASCII, EBCDIC or equivalent form by the
-  program that displays the etext (as is the case, for instance,
-  with most word processors); OR
-
-    (3) You provide or agree to provide on request at no
-  additional cost, fee or expense, a copy of the etext in plain
-  ASCII.
-
-2.  LIMITED WARRANTY; DISCLAIMER OF DAMAGES
-
-This etext may contain a "Defect" in the form of incomplete,
-inaccurate or corrupt data, transcription errors, a copyright or
-other infringement, a defective or damaged disk, computer virus,
-or codes that damage or cannot be read by your equipment.  But
-for the "Right of Replacement or Refund" described below, the
-Project (and any other party you may receive this etext from as
-a PROJECT GUTENBERG-tm etext) disclaims all liability to you for
-damages, costs and expenses, including legal fees, and YOU HAVE
-NO REMEDIES FOR NEGLIGENCE OR UNDER STRICT LIABILITY, OR FOR
-BREACH OF WARRANTY OR CONTRACT, INCLUDING BUT NOT LIMITED TO
-INDIRECT, CONSEQUENTIAL, PUNITIVE OR INCIDENTAL DAMAGES, EVEN IF
-YOU GIVE NOTICE OF THE POSSIBILITY OF SUCH DAMAGES.
-
-If you discover a Defect in this etext within 90 days of receiv-
-ing it, you can receive a refund of the money (if any) you paid
-for it by sending an explanatory note within that time to the
-person you received it from.  If you received it on a physical
-medium, you must return it with your note, and such person may
-choose to alternatively give you a replacement copy.  If you
-received it electronically, such person may choose to
-alternatively give you a second opportunity to receive it
-electronically.
-
-THIS ETEXT IS OTHERWISE PROVIDED TO YOU "AS-IS".  NO OTHER
-WARRANTIES OF ANY KIND, EXPRESS OR IMPLIED, ARE MADE TO YOU AS
-TO THE ETEXT OR ANY MEDIUM IT MAY BE ON, INCLUDING BUT NOT
-LIMITED TO WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A
-PARTICULAR PURPOSE.  Some states do not allow disclaimers of
-implied warranties or the exclusion or limitation of consequen-
-tial damages, so the above disclaimers and exclusions may not
-apply to you, and you may have other legal rights.
-
-3.  INDEMNITY: You will indemnify and hold the Project, its
-directors, officers, members and agents harmless from all lia-
-bility, cost and expense, including legal fees, that arise
-directly or indirectly from any of the following that you do or
-cause: [A] distribution of this etext, [B] alteration,
-modification, or addition to the etext, or [C] any Defect.
-
-4.  WHAT IF YOU *WANT* TO SEND MONEY EVEN IF YOU DON'T HAVE TO?
-Project Gutenberg is dedicated to increasing the number of
-public domain and licensed works that can be freely distributed
-in machine readable form.  The Project gratefully accepts
-contributions in money, time, scanning machines, OCR software,
-public domain etexts, royalty free copyright licenses, and
-whatever else you can think of.  Money should be paid to "Pro-
-ject Gutenberg Association / Illinois Benedictine College".
-
-WRITE TO US! We can be reached at:
-     Internet: hart@vmd.cso.uiuc.edu
-       Bitnet: hart@uiucvmd
-   CompuServe: >internet:hart@.vmd.cso.uiuc.edu
-      Attmail: internet!vmd.cso.uiuc.edu!Hart
-        Mail:  Prof. Michael Hart
-               P.O. Box 2782
-               Champaign, IL 61825
-
-This "Small Print!" by Charles B. Kramer, Attorney
-Internet (72600.2026@compuserve.com); TEL: (212-254-5093)
-****   SMALL PRINT! FOR __ COMPLETE SHAKESPEARE ****
-["Small Print" V.12.08.93]
-
-<<THIS ELECTRONIC VERSION OF THE COMPLETE WORKS OF WILLIAM         
-SHAKESPEARE IS COPYRIGHT 1990-1993 BY WORLD LIBRARY, INC., AND IS  
-PROVIDED BY PROJECT GUTENBERG ETEXT OF ILLINOIS BENEDICTINE COLLEGE
-WITH PERMISSION.  ELECTRONIC AND MACHINE READABLE COPIES MAY BE    
-DISTRIBUTED SO LONG AS SUCH COPIES (1) ARE FOR YOUR OR OTHERS      
-PERSONAL USE ONLY, AND (2) ARE NOT DISTRIBUTED OR USED             
-COMMERCIALLY.  PROHIBITED COMMERCIAL DISTRIBUTION INCLUDES BY ANY  
-SERVICE THAT CHARGES FOR DOWNLOAD TIME OR FOR MEMBERSHIP.>> 
-
-
-1609
-
-THE SONNETS
-
-by William Shakespeare
-
-
-THE END
-
-
-
-<<THIS ELECTRONIC VERSION OF THE COMPLETE WORKS OF WILLIAM
-SHAKESPEARE IS COPYRIGHT 1990-1993 BY WORLD LIBRARY, INC., AND IS
-PROVIDED BY PROJECT GUTENBERG ETEXT OF ILLINOIS BENEDICTINE COLLEGE
-WITH PERMISSION.  ELECTRONIC AND MACHINE READABLE COPIES MAY BE
-DISTRIBUTED SO LONG AS SUCH COPIES (1) ARE FOR YOUR OR OTHERS
-PERSONAL USE ONLY, AND (2) ARE NOT DISTRIBUTED OR USED
-COMMERCIALLY.  PROHIBITED COMMERCIAL DISTRIBUTION INCLUDES BY ANY
-SERVICE THAT CHARGES FOR DOWNLOAD TIME OR FOR MEMBERSHIP.>>
-
-
-
-End of this Etext of The Complete Works of William Shakespeare
-
-
-

how-to-use-azureml/azure-synapse/spark_job_on_synapse_spark_pool.ipynb

@@ -1,507 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved. \n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-arcadia/spark_job_on_synapse_spark_pool.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Using Synapse Spark Pool as a Compute Target from Azure Machine Learning Remote Run\n",
-        "1. To use Synapse Spark Pool as a compute target from Experiment Run, [ScriptRunConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.script_run_config.scriptrunconfig?view=azure-ml-py) is used, the same as other Experiment Runs. This notebook demonstrates how to leverage ScriptRunConfig to submit an experiment run to an attached Synapse Spark cluster.\n",
-        "2. To use Synapse Spark Pool as a compute target from [Azure Machine Learning Pipeline](https://aka.ms/pl-concept), a [SynapseSparkStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.synapse_spark_step.synapsesparkstep?view=azure-ml-py) is used. This notebook demonstrates how to leverage SynapseSparkStep in Azure Machine Learning Pipeline.\n",
-        "\n",
-        "## Before you begin:\n",
-        "1. **Create an Azure Synapse workspace**, check [this] (https://docs.microsoft.com/en-us/azure/synapse-analytics/quickstart-create-workspace) for more information.\n",
-        "2. **Create Spark Pool in Synapse workspace**: check [this] (https://docs.microsoft.com/en-us/azure/synapse-analytics/quickstart-create-apache-spark-pool-portal) for more information."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Azure Machine Learning and Pipeline SDK-specific imports"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import azureml.core\n",
-        "from azureml.core import Workspace, Experiment\n",
-        "from azureml.core import LinkedService, SynapseWorkspaceLinkedServiceConfiguration\n",
-        "from azureml.core.compute import ComputeTarget, AmlCompute, SynapseCompute\n",
-        "from azureml.exceptions import ComputeTargetException\n",
-        "from azureml.data import HDFSOutputDatasetConfig\n",
-        "from azureml.core.datastore import Datastore\n",
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "from azureml.pipeline.core import Pipeline\n",
-        "from azureml.pipeline.steps import PythonScriptStep, SynapseSparkStep\n",
-        "\n",
-        "# Check core SDK version number\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Link Synapse workspace to AML \n",
-        "You have to be an \"Owner\" of Synapse workspace resource to perform linking. You can check your role in the Azure resource management portal, if you don't have an \"Owner\" role, you can contact an \"Owner\" to link the workspaces for you."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "\n",
-        "# Replace with your resource info before running.\n",
-        "\n",
-        "synapse_subscription_id=os.getenv(\"SYNAPSE_SUBSCRIPTION_ID\", \"<my-synapse-subscription-id>\")\n",
-        "synapse_resource_group=os.getenv(\"SYNAPSE_RESOURCE_GROUP\", \"<my-synapse-resource-group>\")\n",
-        "synapse_workspace_name=os.getenv(\"SYNAPSE_WORKSPACE_NAME\", \"<my-synapse-workspace-name>\")\n",
-        "synapse_linked_service_name=os.getenv(\"SYNAPSE_LINKED_SERVICE_NAME\", \"<my-synapse-linked-service-name>\")\n",
-        "\n",
-        "synapse_link_config = SynapseWorkspaceLinkedServiceConfiguration(\n",
-        "    subscription_id=synapse_subscription_id,\n",
-        "    resource_group=synapse_resource_group,\n",
-        "    name=synapse_workspace_name\n",
-        ")\n",
-        "\n",
-        "linked_service = LinkedService.register(\n",
-        "    workspace=ws,\n",
-        "    name=synapse_linked_service_name,\n",
-        "    linked_service_config=synapse_link_config)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Linked service property\n",
-        "\n",
-        "A MSI (system_assigned_identity_principal_id) will be generated for each linked service, for example:\n",
-        "\n",
-        "name=synapselink,</p>\n",
-        "type=Synapse, </p>\n",
-        "linked_service_resource_id=/subscriptions/4faaaf21-663f-4391-96fd-47197c630979/resourceGroups/static_resources_synapse_test/providers/Microsoft.Synapse/workspaces/synapsetest2, </p>\n",
-        "system_assigned_identity_principal_id=eb355d52-3806-4c5a-aec9-91447e8cfc2e </p>\n",
-        "\n",
-        "#### Make sure you grant \"Synapse Apache Spark Administrator\" role of the synapse workspace to the generated workspace linking MSI in Synapse studio portal before you submit job."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "linked_service"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "LinkedService.list(ws)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Attach Synapse spark pool as AML compute target"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "synapse_spark_pool_name=os.getenv(\"SYNAPSE_SPARK_POOL_NAME\", \"<my-synapse-spark-pool-name>\")\n",
-        "synapse_compute_name=os.getenv(\"SYNAPSE_COMPUTE_NAME\", \"<my-synapse-compute-name>\")\n",
-        "\n",
-        "attach_config = SynapseCompute.attach_configuration(\n",
-        "        linked_service,\n",
-        "        type=\"SynapseSpark\",\n",
-        "        pool_name=synapse_spark_pool_name)\n",
-        "\n",
-        "synapse_compute=ComputeTarget.attach(\n",
-        "        workspace=ws,\n",
-        "        name=synapse_compute_name,\n",
-        "        attach_configuration=attach_config)\n",
-        "\n",
-        "synapse_compute.wait_for_completion()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Start an experiment run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prepare data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Use the default blob storage\n",
-        "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
-        "print('Datastore {} will be used'.format(def_blob_store.name))\n",
-        "\n",
-        "# We are uploading a sample file in the local directory to be used as a datasource\n",
-        "file_name = \"Titanic.csv\"\n",
-        "def_blob_store.upload_files(files=[\"./{}\".format(file_name)], overwrite=False)\n",
-        " "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Tabular dataset as input"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Dataset\n",
-        "titanic_tabular_dataset = Dataset.Tabular.from_delimited_files(path=[(def_blob_store, file_name)])\n",
-        "input1 = titanic_tabular_dataset.as_named_input(\"tabular_input\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## File dataset as input"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Dataset\n",
-        "titanic_file_dataset = Dataset.File.from_files(path=[(def_blob_store, file_name)])\n",
-        "input2 = titanic_file_dataset.as_named_input(\"file_input\").as_hdfs()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Output config: the output will be registered as a File dataset\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.data import HDFSOutputDatasetConfig\n",
-        "output = HDFSOutputDatasetConfig(destination=(def_blob_store,\"test\")).register_on_complete(name=\"registered_dataset\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Dataprep script"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "os.makedirs(\"code\", exist_ok=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile code/dataprep.py\n",
-        "import os\n",
-        "import sys\n",
-        "import azureml.core\n",
-        "from pyspark.sql import SparkSession\n",
-        "from azureml.core import Run, Dataset\n",
-        "\n",
-        "print(azureml.core.VERSION)\n",
-        "print(os.environ)\n",
-        "\n",
-        "import argparse\n",
-        "parser = argparse.ArgumentParser()\n",
-        "parser.add_argument(\"--tabular_input\")\n",
-        "parser.add_argument(\"--file_input\")\n",
-        "parser.add_argument(\"--output_dir\")\n",
-        "args = parser.parse_args()\n",
-        "\n",
-        "# use dataset sdk to read tabular dataset\n",
-        "run_context = Run.get_context()\n",
-        "dataset = Dataset.get_by_id(run_context.experiment.workspace,id=args.tabular_input)\n",
-        "sdf = dataset.to_spark_dataframe()\n",
-        "sdf.show()\n",
-        "\n",
-        "# use hdfs path to read file dataset\n",
-        "spark= SparkSession.builder.getOrCreate()\n",
-        "sdf = spark.read.option(\"header\", \"true\").csv(args.file_input)\n",
-        "sdf.show()\n",
-        "\n",
-        "sdf.coalesce(1).write\\\n",
-        ".option(\"header\", \"true\")\\\n",
-        ".mode(\"append\")\\\n",
-        ".csv(args.output_dir)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Set up Conda dependency for the following Script Run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.environment import CondaDependencies\n",
-        "conda_dep = CondaDependencies()\n",
-        "conda_dep.add_pip_package(\"azureml-core==1.20.0\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## How to leverage ScriptRunConfig to submit an experiment run to an attached Synapse Spark cluster"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import RunConfiguration\n",
-        "from azureml.core import ScriptRunConfig \n",
-        "from azureml.core import Experiment\n",
-        "\n",
-        "run_config = RunConfiguration(framework=\"pyspark\")\n",
-        "run_config.target = synapse_compute_name\n",
-        "\n",
-        "run_config.spark.configuration[\"spark.driver.memory\"] = \"1g\" \n",
-        "run_config.spark.configuration[\"spark.driver.cores\"] = 2 \n",
-        "run_config.spark.configuration[\"spark.executor.memory\"] = \"1g\" \n",
-        "run_config.spark.configuration[\"spark.executor.cores\"] = 1 \n",
-        "run_config.spark.configuration[\"spark.executor.instances\"] = 1 \n",
-        "\n",
-        "run_config.environment.python.conda_dependencies = conda_dep\n",
-        "\n",
-        "script_run_config = ScriptRunConfig(source_directory = './code',\n",
-        "                                    script= 'dataprep.py',\n",
-        "                                    arguments = [\"--tabular_input\", input1, \n",
-        "                                                 \"--file_input\", input2,\n",
-        "                                                 \"--output_dir\", output],\n",
-        "                                    run_config = run_config) "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Experiment \n",
-        "exp = Experiment(workspace=ws, name=\"synapse-spark\") \n",
-        "run = exp.submit(config=script_run_config) \n",
-        "run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## How to leverage SynapseSparkStep in an AML pipeline to orchestrate data prep step on Synapse Spark and training step on AzureML compute."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# 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=1)\n",
-        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
-        "\n",
-        "cpu_cluster.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile code/train.py\n",
-        "import glob\n",
-        "import os\n",
-        "import sys\n",
-        "from os import listdir\n",
-        "from os.path import isfile, join\n",
-        "\n",
-        "mypath = os.environ[\"step2_input\"]\n",
-        "files = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n",
-        "for file in files:\n",
-        "    with open(join(mypath,file)) as f:\n",
-        "        print(f.read())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "titanic_tabular_dataset = Dataset.Tabular.from_delimited_files(path=[(def_blob_store, file_name)])\n",
-        "titanic_file_dataset = Dataset.File.from_files(path=[(def_blob_store, file_name)])\n",
-        "\n",
-        "step1_input1 = titanic_tabular_dataset.as_named_input(\"tabular_input\")\n",
-        "step1_input2 = titanic_file_dataset.as_named_input(\"file_input\").as_hdfs()\n",
-        "step1_output = HDFSOutputDatasetConfig(destination=(def_blob_store,\"test\")).register_on_complete(name=\"registered_dataset\")\n",
-        "\n",
-        "step2_input = step1_output.as_input(\"step2_input\").as_download()\n",
-        "\n",
-        "\n",
-        "from azureml.core.environment import Environment\n",
-        "env = Environment(name=\"myenv\")\n",
-        "env.python.conda_dependencies.add_pip_package(\"azureml-core==1.20.0\")\n",
-        "\n",
-        "step_1 = SynapseSparkStep(name = 'synapse-spark',\n",
-        "                          file = 'dataprep.py',\n",
-        "                          source_directory=\"./code\", \n",
-        "                          inputs=[step1_input1, step1_input2],\n",
-        "                          outputs=[step1_output],\n",
-        "                          arguments = [\"--tabular_input\", step1_input1, \n",
-        "                                       \"--file_input\", step1_input2,\n",
-        "                                       \"--output_dir\", step1_output],\n",
-        "                          compute_target = synapse_compute_name,\n",
-        "                          driver_memory = \"7g\",\n",
-        "                          driver_cores = 4,\n",
-        "                          executor_memory = \"7g\",\n",
-        "                          executor_cores = 2,\n",
-        "                          num_executors = 1,\n",
-        "                          environment = env)\n",
-        "\n",
-        "step_2 = PythonScriptStep(script_name=\"train.py\",\n",
-        "                          arguments=[step2_input],\n",
-        "                          inputs=[step2_input],\n",
-        "                          compute_target=cpu_cluster_name,\n",
-        "                          source_directory=\"./code\",\n",
-        "                          allow_reuse=False)\n",
-        "\n",
-        "pipeline = Pipeline(workspace=ws, steps=[step_1, step_2])\n",
-        "pipeline_run = pipeline.submit('synapse-pipeline', regenerate_outputs=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "yunzhan"
-      }
-    ],
-    "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": "3.6.7"
-    },
-    "nteract": {
-      "version": "0.28.0"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/azure-synapse/spark_session_on_synapse_spark_pool.ipynb

@@ -1,327 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-arcadia/spark_session_on_synapse_spark_pool.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Interactive Spark Session on Synapse Spark Pool"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Install package"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "!pip install -U \"azureml-synapse\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "For JupyterLab, please additionally run:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "!jupyter lab build --minimize=False"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## PLEASE restart kernel and then refresh web page before starting spark session."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 0. How to leverage Spark Magic for interactive Spark experience"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2020-06-05T03:22:14.965395Z",
-          "iopub.status.busy": "2020-06-05T03:22:14.965395Z",
-          "iopub.status.idle": "2020-06-05T03:22:14.970398Z",
-          "shell.execute_reply": "2020-06-05T03:22:14.969397Z",
-          "shell.execute_reply.started": "2020-06-05T03:22:14.965395Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "# show help\n",
-        "%synapse ?"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 1. Start Synapse Session"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "synapse_compute_name=os.getenv(\"SYNAPSE_COMPUTE_NAME\", \"<my-synapse-compute-name>\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# use Synapse compute linked to the Compute Instance's workspace with an aml envrionment.\n",
-        "# conda dependencies specified in the environment will be installed before the spark session started.\n",
-        "\n",
-        "%synapse start -c $synapse_compute_name -e AzureML-Minimal"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# use Synapse compute from anther workspace via its config file\n",
-        "\n",
-        "# %synapse start -c <compute-name> -f config.json"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# use Synapse compute from anther workspace via subscription_id, resource_group and workspace_name\n",
-        "\n",
-        "# %synapse start -c <compute-name> -s <subscription-id> -r <resource group> -w <workspace-name>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# start a spark session with an AML environment, \n",
-        "# %synapse start -c <compute-name> -s <subscription-id> -r <resource group> -w <workspace-name> -e AzureML-Minimal"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 2. Data prepration\n",
-        "\n",
-        "Three types of datastore are supported in synapse spark, and you have two ways to load the data.\n",
-        "\n",
-        "\n",
-        "| Datastore Type     | Data Acess                    |\n",
-        "|--------------------|-------------------------------|\n",
-        "| Blob               | Credential                    |\n",
-        "| Adlsgen1           | Credential & Credential-less  |\n",
-        "| Adlsgen2           | Credential & Credential-less  |"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Example 1: Data loading by HDFS path"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "**Read data from Blob**\n",
-        "\n",
-        "```python\n",
-        "# setup access key or sas token\n",
-        "\n",
-        "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.key.<storage account name>.blob.core.windows.net\", \"<acess key>\")\n",
-        "sc._jsc.hadoopConfiguration().set(\"fs.azure.sas.<container name>.<storage account name>.blob.core.windows.net\", \"sas token\")\n",
-        "\n",
-        "df = spark.read.parquet(\"wasbs://<container name>@<storage account name>.blob.core.windows.net/<path>\")\n",
-        "```\n",
-        "\n",
-        "**Read data from Adlsgen1**\n",
-        "\n",
-        "```python\n",
-        "# setup service pricinpal which has access of the data\n",
-        "# If no data Credential is setup, the user identity will be used to do access control\n",
-        "\n",
-        "sc._jsc.hadoopConfiguration().set(\"fs.adl.account.<storage account name>.oauth2.access.token.provider.type\",\"ClientCredential\")\n",
-        "sc._jsc.hadoopConfiguration().set(\"fs.adl.account.<storage account name>.oauth2.client.id\", \"<client id>\")\n",
-        "sc._jsc.hadoopConfiguration().set(\"fs.adl.account.<storage account name>.oauth2.credential\", \"<client secret>\")\n",
-        "sc._jsc.hadoopConfiguration().set(\"fs.adl.account.<storage account name>.oauth2.refresh.url\", \"https://login.microsoftonline.com/<tenant id>/oauth2/token\")\n",
-        "\n",
-        "df = spark.read.csv(\"adl://<storage account name>.azuredatalakestore.net/<path>\")\n",
-        "```\n",
-        "\n",
-        "**Read data from Adlsgen2**\n",
-        "\n",
-        "```python\n",
-        "# setup service pricinpal which has access of the data\n",
-        "# If no data Credential is setup, the user identity will be used to do access control\n",
-        "\n",
-        "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.auth.type.<storage account name>.dfs.core.windows.net\",\"OAuth\")\n",
-        "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.oauth.provider.type.<storage account name>.dfs.core.windows.net\", \"org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider\")\n",
-        "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.oauth2.client.id.<storage account name>.dfs.core.windows.net\", \"<client id>\")\n",
-        "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.oauth2.client.secret.<storage account name>.dfs.core.windows.net\", \"<client secret>\")\n",
-        "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.oauth2.client.endpoint.<storage account name>.dfs.core.windows.net\", \"https://login.microsoftonline.com/<tenant id>/oauth2/token\")\n",
-        "\n",
-        "df = spark.read.csv(\"abfss://<container name>@<storage account>.dfs.core.windows.net/<path>\")\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2020-06-04T08:11:18.812276Z",
-          "iopub.status.busy": "2020-06-04T08:11:18.812276Z",
-          "iopub.status.idle": "2020-06-04T08:11:23.854526Z",
-          "shell.execute_reply": "2020-06-04T08:11:23.853525Z",
-          "shell.execute_reply.started": "2020-06-04T08:11:18.812276Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "%%synapse\n",
-        "\n",
-        "from pyspark.sql.functions import col, desc\n",
-        "\n",
-        "df = spark.read.option(\"header\", \"true\").csv(\"wasbs://demo@dprepdata.blob.core.windows.net/Titanic.csv\")\n",
-        "df.filter(col('Survived') == 1).groupBy('Age').count().orderBy(desc('count')).show(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Example 2: Data loading by AML Dataset\n",
-        "\n",
-        "You can create tabular data by following the [guidance](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets) and use to_spark_dataframe() to load the data.\n",
-        "\n",
-        "```text\n",
-        "%%synapse\n",
-        "\n",
-        "import azureml.core\n",
-        "print(azureml.core.VERSION)\n",
-        "\n",
-        "from azureml.core import Workspace, Dataset\n",
-        "ws = Workspace.get(name='<workspace name>', subscription_id='<subscription id>', resource_group='<resource group>')\n",
-        "ds = Dataset.get_by_name(ws, \"<tabular dataset name>\")\n",
-        "df = ds.to_spark_dataframe()\n",
-        "\n",
-        "# You can do more data transformation on spark dataframe\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 3. Session Metadata\n",
-        "After session started, you can check the session's metadata, find the links to Synapse portal."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%synapse meta"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 4. Stop Session\n",
-        "When current session reach the status timeout, dead or any failure, you must explicitly stop it before start new one. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%synapse stop"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "yunzhan"
-      }
-    ],
-    "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": "3.6.7"
-    },
-    "nteract": {
-      "version": "0.28.0"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 4
-}

how-to-use-azureml/azure-synapse/start_script.py

@@ -1,18 +0,0 @@
-from pyspark.sql import SparkSession
-
-import argparse
-parser = argparse.ArgumentParser()
-parser.add_argument("--input", default="")
-parser.add_argument("--output", default="")
-
-args, unparsed = parser.parse_known_args()
-
-spark = SparkSession.builder.getOrCreate()
-sc = spark.sparkContext
-
-arr = sc._gateway.new_array(sc._jvm.java.lang.String, 2)
-arr[0] = args.input
-arr[1] = args.output
-
-obj = sc._jvm.WordCount
-obj.main(arr)

how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb

@@ -77,7 +77,7 @@
       "source": [
         "## Create trained model\n",
         "\n",
-        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset). "
+        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
       ]
     },
     {
@@ -382,111 +382,13 @@
       "source": [
         "## Update Service\n",
         "\n",
-        "If you want to change your model(s), Conda dependencies or deployment configuration, call `update()` to rebuild the Docker image.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_service.update(models=[model],\n",
+        "If you want to change your model(s), Conda dependencies, or deployment configuration, call `update()` to rebuild the Docker image.\n",
+        "\n",
+        "```python\n",
+        "local_service.update(models=[SomeOtherModelObject],\n",
         "                     inference_config=inference_config,\n",
-        "                     deployment_config=deployment_config)\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deploy model to AKS cluster based on the LocalWebservice's configuration."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# This is a one time setup for AKS Cluster. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n",
-        "from azureml.core.compute import AksCompute, ComputeTarget\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# Choose a name for your AKS cluster\n",
-        "aks_name = 'my-aks-9' \n",
-        "\n",
-        "# Verify the cluster does not exist already\n",
-        "try:\n",
-        "    aks_target = ComputeTarget(workspace=ws, name=aks_name)\n",
-        "    print('Found existing cluster, use it.')\n",
-        "except ComputeTargetException:\n",
-        "    # Use the default configuration (can also provide parameters to customize)\n",
-        "    prov_config = AksCompute.provisioning_configuration()\n",
-        "\n",
-        "    # Create the cluster\n",
-        "    aks_target = ComputeTarget.create(workspace = ws, \n",
-        "                                      name = aks_name, \n",
-        "                                      provisioning_configuration = prov_config)\n",
-        "\n",
-        "if aks_target.get_status() != \"Succeeded\":\n",
-        "    aks_target.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import AksWebservice\n",
-        "# Set the web service configuration (using default here)\n",
-        "aks_config = AksWebservice.deploy_configuration()\n",
-        "\n",
-        "# # Enable token auth and disable (key) auth on the webservice\n",
-        "# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "aks_service_name ='aks-service-1'\n",
-        "\n",
-        "aks_service = local_service.deploy_to_cloud(name=aks_service_name,\n",
-        "                                            deployment_config=aks_config,\n",
-        "                                            deployment_target=aks_target)\n",
-        "\n",
-        "aks_service.wait_for_deployment(show_output = True)\n",
-        "print(aks_service.state)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Test aks service\n",
-        "\n",
-        "sample_input = json.dumps({\n",
-        "    'data': dataset_x[0:2].tolist()\n",
-        "})\n",
-        "\n",
-        "aks_service.run(sample_input)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Delete the service if not needed.\n",
-        "aks_service.delete()"
+        "                     deployment_config=local_config)\n",
+        "```"
       ]
     },
     {

how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb

@@ -157,9 +157,7 @@
       "metadata": {},
       "source": [
         "## Provision the AKS Cluster\n",
-        "If you already have an AKS cluster attached to this workspace, skip the step below and provide the name of the cluster.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+        "If you already have an AKS cluster attached to this workspace, skip the step below and provide the name of the cluster."
       ]
     },
     {

how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb

@@ -267,9 +267,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Create AKS compute if you haven't done so.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+        "### Create AKS compute if you haven't done so."
       ]
     },
     {

how-to-use-azureml/deployment/onnx/mnist.py

@@ -94,17 +94,6 @@ def main():
     os.makedirs(output_dir, exist_ok=True)
 
     kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
-    # Use Azure Open Datasets for MNIST dataset
-    datasets.MNIST.resources = [
-        ("https://azureopendatastorage.azurefd.net/mnist/train-images-idx3-ubyte.gz",
-         "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
-        ("https://azureopendatastorage.azurefd.net/mnist/train-labels-idx1-ubyte.gz",
-         "d53e105ee54ea40749a09fcbcd1e9432"),
-        ("https://azureopendatastorage.azurefd.net/mnist/t10k-images-idx3-ubyte.gz",
-         "9fb629c4189551a2d022fa330f9573f3"),
-        ("https://azureopendatastorage.azurefd.net/mnist/t10k-labels-idx1-ubyte.gz",
-         "ec29112dd5afa0611ce80d1b7f02629c")
-    ]
     train_loader = torch.utils.data.DataLoader(
         datasets.MNIST('data', train=True, download=True,
                        transform=transforms.Compose([transforms.ToTensor(),

how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb

@@ -70,16 +70,16 @@
         "\n",
         "import urllib.request\n",
         "\n",
-        "onnx_model_url = \"https://github.com/onnx/models/blob/master/vision/body_analysis/emotion_ferplus/model/emotion-ferplus-7.tar.gz?raw=true\"\n",
+        "onnx_model_url = \"https://www.cntk.ai/OnnxModels/emotion_ferplus/opset_7/emotion_ferplus.tar.gz\"\n",
         "\n",
-        "urllib.request.urlretrieve(onnx_model_url, filename=\"emotion-ferplus-7.tar.gz\")\n",
+        "urllib.request.urlretrieve(onnx_model_url, filename=\"emotion_ferplus.tar.gz\")\n",
         "\n",
         "# the ! magic command tells our jupyter notebook kernel to run the following line of \n",
         "# code from the command line instead of the notebook kernel\n",
         "\n",
         "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n",
         "\n",
-        "!tar xvzf emotion-ferplus-7.tar.gz"
+        "!tar xvzf emotion_ferplus.tar.gz"
       ]
     },
     {
@@ -570,7 +570,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "plt.figure(figsize = (16, 6))\n",
+        "plt.figure(figsize = (16, 6), frameon=False)\n",
         "plt.subplot(1, 8, 1)\n",
         "\n",
         "plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n",

how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb

@@ -70,9 +70,9 @@
         "\n",
         "import urllib.request\n",
         "\n",
-        "onnx_model_url = \"https://github.com/onnx/models/blob/master/vision/classification/mnist/model/mnist-7.tar.gz?raw=true\"\n",
+        "onnx_model_url = \"https://www.cntk.ai/OnnxModels/mnist/opset_7/mnist.tar.gz\"\n",
         "\n",
-        "urllib.request.urlretrieve(onnx_model_url, filename=\"mnist-7.tar.gz\")"
+        "urllib.request.urlretrieve(onnx_model_url, filename=\"mnist.tar.gz\")"
       ]
     },
     {
@@ -86,7 +86,7 @@
         "\n",
         "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n",
         "\n",
-        "!tar xvzf mnist-7.tar.gz"
+        "!tar xvzf mnist.tar.gz"
       ]
     },
     {
@@ -521,7 +521,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "plt.figure(figsize = (16, 6))\n",
+        "plt.figure(figsize = (16, 6), frameon=False)\n",
         "plt.subplot(1, 8, 1)\n",
         "\n",
         "plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n",
@@ -684,7 +684,18 @@
         "\n",
         "A convolution layer is a set of filters. Each filter is defined by a weight (**W**) matrix, and  bias ($b$).\n",
         "\n",
-        "These filters are scanned across the image performing the dot product between the weights and corresponding input value ($x$). The bias value is added to the output of the dot product and the resulting sum is optionally mapped through an activation function."
+        "![](https://www.cntk.ai/jup/cntk103d_filterset_v2.png)\n",
+        "\n",
+        "These filters are scanned across the image performing the dot product between the weights and corresponding input value ($x$). The bias value is added to the output of the dot product and the resulting sum is optionally mapped through an activation function. This process is illustrated in the following animation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "Image(url=\"https://www.cntk.ai/jup/cntk103d_conv2d_final.gif\", width= 200)"
       ]
     },
     {
@@ -696,6 +707,24 @@
         "The MNIST model from the ONNX Model Zoo uses maxpooling to update the weights in its convolutions, summarized by the graphic below. You can see the entire workflow of our pre-trained model in the following image, with our input images and our output probabilities of each of our 10 labels. If you're interested in exploring the logic behind creating a Deep Learning model further, please look at the [training tutorial for our ONNX MNIST Convolutional Neural Network](https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_103D_MNIST_ConvolutionalNeuralNetwork.ipynb). "
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Max-Pooling for Convolutional Neural Nets\n",
+        "\n",
+        "![](http://www.cntk.ai/jup/c103d_max_pooling.gif)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Pre-Trained Model Architecture\n",
+        "\n",
+        "![](http://www.cntk.ai/jup/conv103d_mnist-conv-mp.png)"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,

how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb

@@ -211,8 +211,6 @@
         "# Provision the AKS Cluster with SSL\n",
         "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n",
         "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
-        "\n",
         "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details"
       ]
     },

how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb

@@ -325,9 +325,7 @@
       "metadata": {},
       "source": [
         "# Provision the AKS Cluster\n",
-        "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+        "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
       ]
     },
     {

how-to-use-azureml/deployment/spark/model-register-and-deploy-spark.ipynb

@@ -2,22 +2,23 @@
   "cells": [
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "Copyright (c) Microsoft Corporation. All rights reserved.\n",
         "\n",
         "Licensed under the MIT License."
-      ],
-      "metadata": {}
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.png)"
-      ],
-      "metadata": {}
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "# Register Spark Model and deploy as Webservice\n",
         "\n",
@@ -25,128 +26,120 @@
         "\n",
         " 1. Register Spark Model\n",
         " 2. Deploy Spark Model as Webservice"
-      ],
-      "metadata": {}
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "## Prerequisites\n",
         "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
-      ],
-      "metadata": {}
+      ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "source": [
-        "# Check core SDK version number\r\n",
-        "import azureml.core\r\n",
-        "\r\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ],
+      "metadata": {},
       "outputs": [],
-      "metadata": {}
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "## Initialize Workspace\n",
         "\n",
         "Initialize a workspace object from persisted configuration."
-      ],
-      "metadata": {}
+      ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "source": [
-        "from azureml.core import Workspace\r\n",
-        "\r\n",
-        "ws = Workspace.from_config()\r\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
-      ],
-      "outputs": [],
       "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": [
         "### Register Model"
-      ],
-      "metadata": {}
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "You can add tags and descriptions to your Models. Note you need to have a `iris.model` file in the current directory. This model file is generated using [train in spark](../training/train-in-spark/train-in-spark.ipynb) notebook. The below call registers that file as a Model with the same name `iris.model` in the workspace.\n",
         "\n",
         "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
-      ],
-      "metadata": {}
+      ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "source": [
-        "from azureml.core.model import Model\r\n",
-        "\r\n",
-        "model = Model.register(model_path=\"iris.model\",\r\n",
-        "                       model_name=\"iris.model\",\r\n",
-        "                       tags={'type': \"regression\"},\r\n",
-        "                       description=\"Logistic regression model to predict iris species\",\r\n",
-        "                       workspace=ws)"
-      ],
-      "outputs": [],
       "metadata": {
         "tags": [
           "register model from file"
         ]
-      }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import Model\n",
+        "\n",
+        "model = Model.register(model_path=\"iris.model\",\n",
+        "                       model_name=\"iris.model\",\n",
+        "                       tags={'type': \"regression\"},\n",
+        "                       description=\"Logistic regression model to predict iris species\",\n",
+        "                       workspace=ws)"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "### Fetch Environment"
-      ],
-      "metadata": {}
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "You can now create and/or use an Environment object when deploying a Webservice. The Environment can have been previously registered with your Workspace, or it will be registered with it as a part of the Webservice deployment.\n",
         "\n",
         "In this notebook, we will be using 'AzureML-PySpark-MmlSpark-0.15', a curated environment.\n",
         "\n",
         "More information can be found in our [using environments notebook](../training/using-environments/using-environments.ipynb)."
-      ],
-      "metadata": {}
+      ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "source": [
-        "from azureml.core import Environment\r\n",
-        "from azureml.core.environment import SparkPackage\r\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\r\n",
-        "\r\n",
-        "myenv = Environment('my-pyspark-environment')\r\n",
-        "myenv.docker.base_image = \"mcr.microsoft.com/mmlspark/release:0.15\"\r\n",
-        "myenv.inferencing_stack_version = \"latest\"\r\n",
-        "myenv.python.conda_dependencies = CondaDependencies.create(pip_packages=[\"azureml-core\",\"azureml-defaults\",\"azureml-telemetry\",\"azureml-train-restclients-hyperdrive\",\"azureml-train-core\"], python_version=\"3.6.2\")\r\n",
-        "myenv.python.conda_dependencies.add_channel(\"conda-forge\")\r\n",
-        "myenv.spark.packages = [SparkPackage(\"com.microsoft.ml.spark\", \"mmlspark_2.11\", \"0.15\"), SparkPackage(\"com.microsoft.azure\", \"azure-storage\", \"2.0.0\"), SparkPackage(\"org.apache.hadoop\", \"hadoop-azure\", \"2.7.0\")]\r\n",
-        "myenv.spark.repositories = [\"https://mmlspark.azureedge.net/maven\"]\r\n"
-      ],
+      "metadata": {},
       "outputs": [],
-      "metadata": {}
+      "source": [
+        "from azureml.core import Environment\n",
+        "\n",
+        "env = Environment.get(ws, name='AzureML-PySpark-MmlSpark-0.15')\n"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "## Create Inference Configuration\n",
         "\n",
@@ -164,109 +157,109 @@
         " - source_directory = holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n",
         " - entry_script = contains logic specific to initializing your model and running predictions\n",
         " - environment = An environment object to use for the deployment. Doesn't have to be registered"
-      ],
-      "metadata": {}
+      ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "source": [
-        "from azureml.core.model import InferenceConfig\r\n",
-        "\r\n",
-        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
-      ],
-      "outputs": [],
       "metadata": {
         "tags": [
           "create image"
         ]
-      }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import InferenceConfig\n",
+        "\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "### Deploy Model as Webservice on Azure Container Instance\n",
         "\n",
         "Note that the service creation can take few minutes."
-      ],
-      "metadata": {}
+      ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "source": [
-        "from azureml.core.webservice import AciWebservice, Webservice\r\n",
-        "from azureml.exceptions import WebserviceException\r\n",
-        "\r\n",
-        "deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\r\n",
-        "aci_service_name = 'aciservice1'\r\n",
-        "\r\n",
-        "try:\r\n",
-        "    # if you want to get existing service below is the command\r\n",
-        "    # since aci name needs to be unique in subscription deleting existing aci if any\r\n",
-        "    # we use aci_service_name to create azure aci\r\n",
-        "    service = Webservice(ws, name=aci_service_name)\r\n",
-        "    if service:\r\n",
-        "        service.delete()\r\n",
-        "except WebserviceException as e:\r\n",
-        "    print()\r\n",
-        "\r\n",
-        "service = Model.deploy(ws, aci_service_name, [model], inference_config, deployment_config)\r\n",
-        "\r\n",
-        "service.wait_for_deployment(True)\r\n",
-        "print(service.state)"
-      ],
-      "outputs": [],
       "metadata": {
         "tags": [
           "azuremlexception-remarks-sample"
         ]
-      }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice, Webservice\n",
+        "from azureml.exceptions import WebserviceException\n",
+        "\n",
+        "deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
+        "aci_service_name = 'aciservice1'\n",
+        "\n",
+        "try:\n",
+        "    # if you want to get existing service below is the command\n",
+        "    # since aci name needs to be unique in subscription deleting existing aci if any\n",
+        "    # we use aci_service_name to create azure aci\n",
+        "    service = Webservice(ws, name=aci_service_name)\n",
+        "    if service:\n",
+        "        service.delete()\n",
+        "except WebserviceException as e:\n",
+        "    print()\n",
+        "\n",
+        "service = Model.deploy(ws, aci_service_name, [model], inference_config, deployment_config)\n",
+        "\n",
+        "service.wait_for_deployment(True)\n",
+        "print(service.state)"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "#### Test web service"
-      ],
-      "metadata": {}
+      ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "source": [
-        "import json\r\n",
-        "test_sample = json.dumps({'features':{'type':1,'values':[4.3,3.0,1.1,0.1]},'label':2.0})\r\n",
-        "\r\n",
-        "test_sample_encoded = bytes(test_sample, encoding='utf8')\r\n",
-        "prediction = service.run(input_data=test_sample_encoded)\r\n",
-        "print(prediction)"
-      ],
+      "metadata": {},
       "outputs": [],
-      "metadata": {}
+      "source": [
+        "import json\n",
+        "test_sample = json.dumps({'features':{'type':1,'values':[4.3,3.0,1.1,0.1]},'label':2.0})\n",
+        "\n",
+        "test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
+        "prediction = service.run(input_data=test_sample_encoded)\n",
+        "print(prediction)"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "#### Delete ACI to clean up"
-      ],
-      "metadata": {}
+      ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "source": [
-        "service.delete()"
-      ],
-      "outputs": [],
       "metadata": {
         "tags": [
           "deploy service",
           "aci"
         ]
-      }
+      },
+      "outputs": [],
+      "source": [
+        "service.delete()"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "### Model Profiling\n",
         "\n",
@@ -278,11 +271,11 @@
         "profiling_results = profile.get_results()\n",
         "print(profiling_results)\n",
         "```"
-      ],
-      "metadata": {}
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "### Model Packaging\n",
         "\n",
@@ -303,8 +296,7 @@
         "package.wait_for_creation(show_output=True)\n",
         "package.save(\"./local_context_dir\")\n",
         "```"
-      ],
-      "metadata": {}
+      ]
     }
   ],
   "metadata": {

how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb

@@ -203,8 +203,6 @@
       "source": [
         "### Provision a compute target\n",
         "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
-        "\n",
         "You can provision an 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",
@@ -217,6 +215,7 @@
       "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",
@@ -256,8 +255,11 @@
         "# 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",
         "azureml_pip_packages = [\n",
-        "    'azureml-defaults', 'azureml-telemetry', 'azureml-interpret'\n",
+        "    'azureml-defaults', 'azureml-contrib-interpret', 'azureml-telemetry', 'azureml-interpret'\n",
         "]\n",
         "\n",
         "# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n",
@@ -266,7 +268,7 @@
         "available_packages = pkg_resources.working_set\n",
         "sklearn_ver = None\n",
         "pandas_ver = None\n",
-        "for dist in list(available_packages):\n",
+        "for dist in available_packages:\n",
         "    if dist.key == 'scikit-learn':\n",
         "        sklearn_ver = dist.version\n",
         "    elif dist.key == 'pandas':\n",
@@ -285,6 +287,7 @@
         "azureml_pip_packages.extend([sklearn_dep, pandas_dep])\n",
         "run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
         "\n",
+        "from azureml.core import Run\n",
         "from azureml.core import ScriptRunConfig\n",
         "\n",
         "src = ScriptRunConfig(source_directory=project_folder, \n",
@@ -414,6 +417,7 @@
       "outputs": [],
       "source": [
         "# Retrieve x_test for visualization\n",
+        "import joblib\n",
         "x_test_path = './x_test_boston_housing.pkl'\n",
         "run.download_file('x_test_boston_housing.pkl', output_file_path=x_test_path)"
       ]
@@ -441,7 +445,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from raiwidgets import ExplanationDashboard"
+        "from interpret_community.widget import ExplanationDashboard"
       ]
     },
     {
@@ -450,7 +454,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(global_explanation, original_model, dataset=x_test)"
+        "ExplanationDashboard(global_explanation, original_model, datasetX=x_test)"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml

@@ -3,12 +3,9 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-interpret
-  - flask
-  - flask-cors
-  - gevent>=1.3.6
-  - jinja2
-  - ipython
+  - interpret-community[visualization]
   - matplotlib
+  - azureml-contrib-interpret
+  - sklearn-pandas<2.0.0
   - azureml-dataset-runtime
   - ipywidgets
-  - raiwidgets~=0.7.0

how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb

@@ -57,7 +57,7 @@
         "Problem: IBM employee attrition classification with scikit-learn (run model explainer locally and upload explanation to the Azure Machine Learning Run History)\n",
         "\n",
         "1. Train a SVM classification model using Scikit-learn\n",
-        "2. Run 'explain-model-sample' with AML Run History, which leverages run history service to store and manage the explanation data\n",
+        "2. Run 'explain_model' with AML Run History, which leverages run history service to store and manage the explanation data\n",
         "---\n",
         "\n",
         "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
@@ -87,6 +87,7 @@
         "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
         "from sklearn.svm import SVC\n",
         "import pandas as pd\n",
+        "import numpy as np\n",
         "\n",
         "# Explainers:\n",
         "# 1. SHAP Tabular Explainer\n",
@@ -225,6 +226,36 @@
         "                      ('classifier', SVC(C=1.0, probability=True))])"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "'''\n",
+        "# Uncomment below if sklearn-pandas is not installed\n",
+        "#!pip install sklearn-pandas\n",
+        "from sklearn_pandas import DataFrameMapper\n",
+        "\n",
+        "# Impute, standardize the numeric features and one-hot encode the categorical features.    \n",
+        "\n",
+        "\n",
+        "numeric_transformations = [([f], Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])) for f in numerical]\n",
+        "\n",
+        "categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]\n",
+        "\n",
+        "transformations = numeric_transformations + categorical_transformations\n",
+        "\n",
+        "# Append classifier to preprocessing pipeline.\n",
+        "# Now we have a full prediction pipeline.\n",
+        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
+        "                      ('classifier', SVC(C=1.0, probability=True))]) \n",
+        "\n",
+        "\n",
+        "\n",
+        "'''"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -444,7 +475,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "experiment_name = 'explain-model-sample'\n",
+        "experiment_name = 'explain_model'\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "run = experiment.start_logging()\n",
         "client = ExplanationClient.from_run(run)"
@@ -532,7 +563,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from raiwidgets import ExplanationDashboard"
+        "from interpret_community.widget import ExplanationDashboard"
       ]
     },
     {
@@ -541,7 +572,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(downloaded_global_explanation, model, dataset=x_test)"
+        "ExplanationDashboard(downloaded_global_explanation, model, datasetX=x_test)"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml

@@ -3,11 +3,7 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-interpret
-  - flask
-  - flask-cors
-  - gevent>=1.3.6
-  - jinja2
-  - ipython
+  - interpret-community[visualization]
   - matplotlib
+  - azureml-contrib-interpret
   - ipywidgets
-  - raiwidgets~=0.7.0

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb

@@ -166,11 +166,12 @@
       "source": [
         "from sklearn.model_selection import train_test_split\n",
         "import joblib\n",
-        "from sklearn.compose import ColumnTransformer\n",
         "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
         "from sklearn.impute import SimpleImputer\n",
         "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.linear_model import LogisticRegression\n",
         "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn_pandas import DataFrameMapper\n",
         "\n",
         "from interpret.ext.blackbox import TabularExplainer\n",
         "\n",
@@ -200,26 +201,21 @@
         "# Store the numerical columns in a list numerical\n",
         "numerical = attritionXData.columns.difference(categorical)\n",
         "\n",
-        "# We create the preprocessing pipelines for both numeric and categorical data.\n",
-        "numeric_transformer = Pipeline(steps=[\n",
+        "numeric_transformations = [([f], Pipeline(steps=[\n",
         "    ('imputer', SimpleImputer(strategy='median')),\n",
-        "    ('scaler', StandardScaler())])\n",
+        "    ('scaler', StandardScaler())])) for f in numerical]\n",
         "\n",
-        "categorical_transformer = Pipeline(steps=[\n",
-        "    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
-        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
+        "categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]\n",
         "\n",
-        "transformations = ColumnTransformer(\n",
-        "    transformers=[\n",
-        "        ('num', numeric_transformer, numerical),\n",
-        "        ('cat', categorical_transformer, categorical)])\n",
+        "transformations = numeric_transformations + categorical_transformations\n",
         "\n",
         "# Append classifier to preprocessing pipeline.\n",
         "# Now we have a full prediction pipeline.\n",
-        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
+        "clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),\n",
         "                      ('classifier', RandomForestClassifier())])\n",
         "\n",
         "# Split data into train and test\n",
+        "from sklearn.model_selection import train_test_split\n",
         "x_train, x_test, y_train, y_test = train_test_split(attritionXData,\n",
         "                                                    target,\n",
         "                                                    test_size=0.2,\n",
@@ -294,7 +290,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from raiwidgets import ExplanationDashboard"
+        "from interpret_community.widget import ExplanationDashboard"
       ]
     },
     {
@@ -303,7 +299,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(global_explanation, clf, dataset=x_test)"
+        "ExplanationDashboard(global_explanation, clf, datasetX=x_test)"
       ]
     },
     {
@@ -327,7 +323,7 @@
         "\n",
         "# azureml-defaults is required to host the model as a web service.\n",
         "azureml_pip_packages = [\n",
-        "    'azureml-defaults', 'azureml-core', 'azureml-telemetry',\n",
+        "    'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n",
         "    'azureml-interpret'\n",
         "]\n",
         " \n",
@@ -354,7 +350,8 @@
         "# the submitted job is run in.  Note the remote environment(s) needs to be similar to the local\n",
         "# environment, otherwise if a model is trained or deployed in a different environment this can\n",
         "# cause errors.  Please take extra care when specifying your dependencies in a production environment.\n",
-        "myenv = CondaDependencies.create(pip_packages=['pyyaml', sklearn_dep, pandas_dep] + azureml_pip_packages)\n",
+        "myenv = CondaDependencies.create(pip_packages=['sklearn-pandas', 'pyyaml', sklearn_dep, pandas_dep] + azureml_pip_packages,\n",
+        "                                 pin_sdk_version=False)\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())\n",
@@ -380,10 +377,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.core.webservice import Webservice\n",
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.webservice import AciWebservice\n",
+        "from azureml.core.model import Model\n",
         "from azureml.core.environment import Environment\n",
-        "from azureml.exceptions import WebserviceException\n",
         "\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
@@ -397,12 +395,7 @@
         "\n",
         "# Use configs and models generated above\n",
         "service = Model.deploy(ws, 'model-scoring-deploy-local', [scoring_explainer_model, original_model], inference_config, aciconfig)\n",
-        "try:\n",
-        "    service.wait_for_deployment(show_output=True)\n",
-        "except WebserviceException as e:\n",
-        "    print(e.message)\n",
-        "    print(service.get_logs())\n",
-        "    raise"
+        "service.wait_for_deployment(show_output=True)"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml

@@ -3,11 +3,8 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-interpret
-  - flask
-  - flask-cors
-  - gevent>=1.3.6
-  - jinja2
-  - ipython
+  - interpret-community[visualization]
   - matplotlib
+  - azureml-contrib-interpret
+  - sklearn-pandas<2.0.0
   - ipywidgets
-  - raiwidgets~=0.7.0

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb

@@ -204,8 +204,6 @@
       "source": [
         "### Provision a compute target\n",
         "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
-        "\n",
         "You can provision an 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",
@@ -218,6 +216,7 @@
       "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",
@@ -258,6 +257,9 @@
         "# 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",
         "# Set Docker base image to the default CPU-based image\n",
         "run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n",
         "\n",
@@ -265,7 +267,7 @@
         "run_config.environment.python.user_managed_dependencies = False\n",
         "\n",
         "azureml_pip_packages = [\n",
-        "    'azureml-defaults', 'azureml-telemetry', 'azureml-interpret'\n",
+        "    'azureml-defaults', 'azureml-contrib-interpret', 'azureml-telemetry', 'azureml-interpret'\n",
         "]\n",
         " \n",
         "\n",
@@ -292,7 +294,7 @@
         "# the submitted job is run in.  Note the remote environment(s) needs to be similar to the local\n",
         "# environment, otherwise if a model is trained or deployed in a different environment this can\n",
         "# cause errors.  Please take extra care when specifying your dependencies in a production environment.\n",
-        "azureml_pip_packages.extend(['pyyaml', sklearn_dep, pandas_dep])\n",
+        "azureml_pip_packages.extend(['sklearn-pandas', 'pyyaml', sklearn_dep, pandas_dep])\n",
         "run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
         "# Now submit a run on AmlCompute\n",
         "from azureml.core.script_run_config import ScriptRunConfig\n",
@@ -379,6 +381,7 @@
       "outputs": [],
       "source": [
         "# Retrieve x_test for visualization\n",
+        "import joblib\n",
         "x_test_path = './x_test.pkl'\n",
         "run.download_file('x_test_ibm.pkl', output_file_path=x_test_path)\n",
         "x_test = joblib.load(x_test_path)"
@@ -398,7 +401,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from raiwidgets import ExplanationDashboard"
+        "from interpret_community.widget import ExplanationDashboard"
       ]
     },
     {
@@ -407,7 +410,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(global_explanation, original_svm_model, dataset=x_test)"
+        "ExplanationDashboard(global_explanation, original_svm_model, datasetX=x_test)"
       ]
     },
     {
@@ -424,15 +427,18 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.core.conda_dependencies import CondaDependencies \n",
+        "\n",
         "# WARNING: to install this, g++ needs to be available on the Docker image and is not by default (look at the next cell)\n",
         "azureml_pip_packages = [\n",
-        "    'azureml-defaults', 'azureml-core', 'azureml-telemetry',\n",
+        "    'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n",
         "    'azureml-interpret'\n",
         "]\n",
         " \n",
         "\n",
         "# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n",
         "# In production scenario user would choose their dependencies\n",
+        "import pkg_resources\n",
         "available_packages = pkg_resources.working_set\n",
         "sklearn_ver = None\n",
         "pandas_ver = None\n",
@@ -452,7 +458,7 @@
         "# the submitted job is run in.  Note the remote environment(s) needs to be similar to the local\n",
         "# environment, otherwise if a model is trained or deployed in a different environment this can\n",
         "# cause errors.  Please take extra care when specifying your dependencies in a production environment.\n",
-        "azureml_pip_packages.extend(['pyyaml', sklearn_dep, pandas_dep])\n",
+        "azureml_pip_packages.extend(['sklearn-pandas', 'pyyaml', sklearn_dep, pandas_dep])\n",
         "myenv = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
@@ -478,10 +484,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.core.webservice import Webservice\n",
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.webservice import AciWebservice\n",
+        "from azureml.core.model import Model\n",
         "from azureml.core.environment import Environment\n",
-        "from azureml.exceptions import WebserviceException\n",
         "\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
@@ -495,12 +502,7 @@
         "\n",
         "# Use configs and models generated above\n",
         "service = Model.deploy(ws, 'model-scoring-service', [scoring_explainer_model, original_model], inference_config, aciconfig)\n",
-        "try:\n",
-        "    service.wait_for_deployment(show_output=True)\n",
-        "except WebserviceException as e:\n",
-        "    print(e.message)\n",
-        "    print(service.get_logs())\n",
-        "    raise"
+        "service.wait_for_deployment(show_output=True)"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml

@@ -3,13 +3,10 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-interpret
-  - flask
-  - flask-cors
-  - gevent>=1.3.6
-  - jinja2
-  - ipython
+  - interpret-community[visualization]
   - matplotlib
+  - azureml-contrib-interpret
+  - sklearn-pandas<2.0.0
   - azureml-dataset-runtime
   - azureml-core
   - ipywidgets
-  - raiwidgets~=0.7.0

how-to-use-azureml/explain-model/azure-integration/scoring-time/train_explain.py

@@ -5,13 +5,13 @@
 import os
 import pandas as pd
 import zipfile
-import joblib
-from sklearn.compose import ColumnTransformer
 from sklearn.model_selection import train_test_split
+import joblib
 from sklearn.preprocessing import StandardScaler, OneHotEncoder
 from sklearn.impute import SimpleImputer
 from sklearn.pipeline import Pipeline
 from sklearn.linear_model import LogisticRegression
+from sklearn_pandas import DataFrameMapper
 
 from azureml.core.run import Run
 from interpret.ext.blackbox import TabularExplainer
@@ -57,22 +57,16 @@ for col, value in attritionXData.iteritems():
 # store the numerical columns
 numerical = attritionXData.columns.difference(categorical)
 
-# We create the preprocessing pipelines for both numeric and categorical data.
-numeric_transformer = Pipeline(steps=[
+numeric_transformations = [([f], Pipeline(steps=[
     ('imputer', SimpleImputer(strategy='median')),
-    ('scaler', StandardScaler())])
+    ('scaler', StandardScaler())])) for f in numerical]
 
-categorical_transformer = Pipeline(steps=[
-    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
-    ('onehot', OneHotEncoder(handle_unknown='ignore'))])
+categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]
 
-transformations = ColumnTransformer(
-    transformers=[
-        ('num', numeric_transformer, numerical),
-        ('cat', categorical_transformer, categorical)])
+transformations = numeric_transformations + categorical_transformations
 
 # append classifier to preprocessing pipeline
-clf = Pipeline(steps=[('preprocessor', transformations),
+clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),
                       ('classifier', LogisticRegression(solver='lbfgs'))])
 
 # get the run this was submitted from to interact with run history

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/README.md

@@ -9,7 +9,7 @@ These notebooks below are designed to go in sequence.
 4. [aml-pipelines-data-transfer.ipynb](https://aka.ms/pl-data-trans): This notebook shows how you transfer data between supported datastores.
 5. [aml-pipelines-use-databricks-as-compute-target.ipynb](https://aka.ms/pl-databricks): This notebooks shows how you can use Pipelines to send your compute payload to Azure Databricks.
 6. [aml-pipelines-use-adla-as-compute-target.ipynb](https://aka.ms/pl-adla): This notebook shows how you can use Azure Data Lake Analytics (ADLA) as a compute target.
-7. [aml-pipelines-with-commandstep.ipynb](aml-pipelines-with-commandstep.ipynb): This notebook shows how to use the CommandStep.
+7. [aml-pipelines-how-to-use-estimatorstep.ipynb](https://aka.ms/pl-estimator): This notebook shows how to use the EstimatorStep.
 8. [aml-pipelines-parameter-tuning-with-hyperdrive.ipynb](https://aka.ms/pl-hyperdrive): HyperDriveStep in Pipelines shows how you can do hyper parameter tuning using Pipelines.
 9. [aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb](https://aka.ms/pl-azbatch): AzureBatchStep can be used to run your custom code in AzureBatch cluster.
 10. [aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb](https://aka.ms/pl-schedule): Once you publish a Pipeline, you can schedule it to trigger based on an interval or on data change in a defined datastore.
@@ -19,6 +19,5 @@ These notebooks below are designed to go in sequence.
 14. [aml-pipelines-how-to-use-pipeline-drafts.ipynb](http://aka.ms/pl-pl-draft): This notebook shows how to use Pipeline Drafts. Pipeline Drafts are mutable pipelines which can be used to submit runs and create Published Pipelines.
 15. [aml-pipelines-hot-to-use-modulestep.ipynb](https://aka.ms/pl-modulestep): This notebook shows how to define Module, ModuleVersion and how to use them in an AML Pipeline using ModuleStep.
 16. [aml-pipelines-with-notebook-runner-step.ipynb](https://aka.ms/pl-nbrstep): This notebook shows how you can run another notebook as a step in Azure Machine Learning Pipeline.
-17. [aml-pipelines-with-commandstep-r.ipynb](aml-pipelines-with-commandstep-r.ipynb): This notebook shows how to use CommandStep to run R scripts.
 
  ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/README.png)

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb

@@ -22,8 +22,6 @@
         "# Azure Machine Learning Pipeline with DataTransferStep\n",
         "This notebook is used to demonstrate the use of DataTransferStep in an Azure Machine Learning Pipeline.\n",
         "\n",
-        "> **Note:** In Azure Machine Learning, you can write output data directly to Azure Blob Storage, Azure Data Lake Storage Gen 1, Azure Data Lake Storage Gen 2, Azure FileShare without going through extra DataTransferStep. Learn how to use [OutputFileDatasetConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.data.output_dataset_config.outputfiledatasetconfig?view=azure-ml-py) to achieve that with sample notebooks [here](https://aka.ms/pipeline-with-dataset).**\n",
-        "\n",
         "In certain cases, you will need to transfer data from one data location to another. For example, your data may be in Azure SQL Database and you may want to move it to Azure Data Lake storage. Or, your data is in an ADLS account and you want to make it available in the Blob storage. The built-in **DataTransferStep** class helps you transfer data in these situations.\n",
         "\n",
         "The below examples show how to move data between different storage types supported in Azure Machine Learning.\n",

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb

@@ -209,8 +209,6 @@
         "#### Retrieve or create a Azure Machine Learning compute\n",
         "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Azure Machine Learning Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target.\n",
         "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
-        "\n",
         "If we could not find the compute with the given name in the previous cell, then we will create a new compute here. We will create an Azure Machine Learning Compute containing **STANDARD_D2_V2 CPU VMs**. This process is broken down into the following steps:\n",
         "\n",
         "1. Create the configuration\n",

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb

@@ -341,7 +341,7 @@
       "outputs": [],
       "source": [
         "pipeline = Pipeline(workspace=ws, steps=[step])\n",
-        "pipeline_run = Experiment(ws, 'azurebatch_sample').submit(pipeline)"
+        "pipeline_run = Experiment(ws, 'azurebatch_experiment').submit(pipeline)"
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-estimatorstep.ipynb

@@ -20,15 +20,15 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# How to use CommandStep in Azure ML Pipelines\n",
+        "# How to use EstimatorStep in AML Pipeline\n",
         "\n",
-        "This notebook shows how to use the CommandStep with Azure Machine Learning Pipelines for running commands in steps. The example shows running distributed TensorFlow training from within a pipeline.\n",
+        "This notebook shows how to use the EstimatorStep with Azure Machine Learning Pipelines. Estimator is a convenient object in Azure Machine Learning that wraps run configuration information to help simplify the tasks of specifying how a script is executed.\n",
         "\n",
         "\n",
         "## Prerequisite:\n",
         "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
         "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](https://aka.ms/pl-config) to:\n",
-        "    * install the Azure ML SDK\n",
+        "    * install the AML SDK\n",
         "    * create a workspace and its configuration file (`config.json`)"
       ]
     },
@@ -77,9 +77,7 @@
       "metadata": {},
       "source": [
         "## Create or Attach existing AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource."
       ]
     },
     {
@@ -102,57 +100,75 @@
         "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# choose a name for your cluster\n",
-        "cluster_name = \"gpu-cluster\"\n",
+        "cluster_name = \"amlcomp\"\n",
         "\n",
         "try:\n",
-        "    gpu_cluster = ComputeTarget(workspace=ws, name=cluster_name)\n",
+        "    cpu_cluster = ComputeTarget(workspace=ws, name=cluster_name)\n",
         "    print('Found existing compute target')\n",
         "except ComputeTargetException:\n",
         "    print('Creating a new compute target...')\n",
         "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', max_nodes=4)\n",
         "\n",
         "    # create the cluster\n",
-        "    gpu_cluster = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+        "    cpu_cluster = ComputeTarget.create(ws, cluster_name, compute_config)\n",
         "\n",
         "    # can poll for a minimum number of nodes and for a specific timeout. \n",
         "    # if no min node count is provided it uses the scale settings for the cluster\n",
-        "    gpu_cluster.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
+        "    cpu_cluster.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
         "\n",
         "# use get_status() to get a detailed status for the current cluster. \n",
-        "print(gpu_cluster.get_status().serialize())"
+        "print(cpu_cluster.get_status().serialize())"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'gpu-cluster' of type `AmlCompute`."
+        "Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'cpu-cluster' of type `AmlCompute`."
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create a CommandStep\n",
-        "CommandStep adds a step to run a command in a Pipeline. For the full set of configurable options see the CommandStep [reference docs](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.commandstep?view=azure-ml-py).\n",
+        "## Use a simple script\n",
+        "We have already created a simple \"hello world\" script. This is the script that we will submit through the estimator pattern. It prints a hello-world message, and if Azure ML SDK is installed, it will also logs an array of values ([Fibonacci numbers](https://en.wikipedia.org/wiki/Fibonacci_number))."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Build an Estimator object\n",
+        "Estimator by default will attempt to use Docker-based execution. You can also enable Docker and let estimator pick the default CPU image supplied by Azure ML for execution. You can target an AmlCompute cluster (or any other supported compute target types). You can also customize the conda environment by adding conda and/or pip packages.\n",
         "\n",
-        "- **name:** Name of the step\n",
-        "- **runconfig:** ScriptRunConfig object. You can configure a ScriptRunConfig object as you would for a standalone non-pipeline run and pass it in to this parameter. If using this option, you do not have to specify the `command`, `source_directory`, `compute_target` parameters of the CommandStep constructor as they are already defined in your ScriptRunConfig.\n",
-        "- **runconfig_pipeline_params:** Override runconfig properties at runtime using key-value pairs each with name of the runconfig property and PipelineParameter for that property\n",
-        "- **command:** The command to run or path of the executable/script relative to `source_directory`. It is required unless the `runconfig` parameter is specified. It can be specified with string arguments in a single string or with input/output/PipelineParameter in a list.\n",
-        "- **source_directory:** A folder containing the script and other resources used in the step.\n",
-        "- **compute_target:** Compute target to use \n",
-        "- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs. If this is false, a new run will always be generated for this step during pipeline execution.\n",
-        "- **version:** Optional version tag to denote a change in functionality for the step\n",
+        "> Note: The arguments to the entry script used in the Estimator object should be specified as *list* using\n",
+        "    'estimator_entry_script_arguments' parameter when instantiating EstimatorStep. Estimator object's parameter\n",
+        "    'script_params' accepts a dictionary. However 'estimator_entry_script_arguments' parameter expects arguments as\n",
+        "    a list.\n",
         "\n",
+        "> Estimator object initialization involves specifying a list of data input and output.\n",
+        "    In Pipelines, a step can take another step's output as input. So when creating an EstimatorStep.\n",
+        "   \n",
         "> The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
       ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {},
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "datareference-remarks-sample"
+        ]
+      },
+      "outputs": [],
       "source": [
-        "First define the environment that you want to step to run in. This example users a curated TensorFlow environment, but in practice you can configure any environment you want."
+        "from azureml.core import Datastore\n",
+        "\n",
+        "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
+        "\n",
+        "#upload input data to workspaceblobstore\n",
+        "def_blob_store.upload_files(files=['20news.pkl'], target_path='20newsgroups', overwrite=True)"
       ]
     },
     {
@@ -161,20 +177,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core import Environment\n",
+        "from azureml.core import Dataset\n",
+        "from azureml.data import OutputFileDatasetConfig\n",
         "\n",
-        "tf_env = Environment.get(ws, name='AzureML-TensorFlow-2.3-GPU')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This example will first create a ScriptRunConfig object that configures the training job. Since we are running a distributed job, specify the `distributed_job_config` parameter. If you are just running a single-node job, omit that parameter.\n",
+        "# create dataset to be used as the input to estimator step\n",
+        "input_data = Dataset.File.from_files(def_blob_store.path('20newsgroups/20news.pkl'))\n",
         "\n",
-        "> If you have an input dataset you want to use in this step, you can specify that as part of the command. For example, if you have a FileDataset object called `dataset` and a `--data-dir` script argument, you can do the following: `command=['python train.py --epochs 30 --data-dir', dataset.as_mount()]`.\n",
+        "# OutputFileDatasetConfig by default write output to the default workspaceblobstore\n",
+        "output = OutputFileDatasetConfig()\n",
         "\n",
-        "> For detailed guidance on how to move data in pipelines for input and output data, see the documentation [Moving data into and between ML pipelines](https://docs.microsoft.com/azure/machine-learning/how-to-move-data-in-out-of-pipelines)."
+        "source_directory = 'estimator_train'"
       ]
     },
     {
@@ -183,24 +195,28 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core import ScriptRunConfig\n",
-        "from azureml.core.runconfig import MpiConfiguration\n",
+        "from azureml.train.estimator import Estimator\n",
         "\n",
-        "src_dir = 'commandstep_train'\n",
-        "distr_config = MpiConfiguration(node_count=2) # you can also specify the process_count_per_node parameter for multi-process-per-node training\n",
-        "\n",
-        "src = ScriptRunConfig(source_directory=src_dir,\n",
-        "                      command=['python train.py --epochs 30'],\n",
-        "                      compute_target=gpu_cluster,\n",
-        "                      environment=tf_env,\n",
-        "                      distributed_job_config=distr_config)"
+        "est = Estimator(source_directory=source_directory, \n",
+        "                compute_target=cpu_cluster, \n",
+        "                entry_script='dummy_train.py', \n",
+        "                conda_packages=['scikit-learn'])"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Now create a CommandStep and pass in the ScriptRunConfig object to the `runconfig` parameter."
+        "## Create an EstimatorStep\n",
+        "[EstimatorStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.estimator_step.estimatorstep?view=azure-ml-py) adds a step to run Estimator in a Pipeline.\n",
+        "\n",
+        "- **name:** Name of the step\n",
+        "- **estimator:** Estimator object\n",
+        "- **estimator_entry_script_arguments:** A list of command-line arguments\n",
+        "- **runconfig_pipeline_params:** Override runconfig properties at runtime using key-value pairs each with name of the runconfig property and PipelineParameter for that property\n",
+        "- **compute_target:** Compute target to use \n",
+        "- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs. If this is false, a new run will always be generated for this step during pipeline execution.\n",
+        "- **version:** Optional version tag to denote a change in functionality for the step"
       ]
     },
     {
@@ -213,16 +229,20 @@
       },
       "outputs": [],
       "source": [
-        "from azureml.pipeline.steps import CommandStep\n",
+        "from azureml.pipeline.steps import EstimatorStep\n",
         "\n",
-        "train = CommandStep(name='train-mnist', runconfig=src)"
+        "est_step = EstimatorStep(name=\"Estimator_Train\", \n",
+        "                         estimator=est, \n",
+        "                         estimator_entry_script_arguments=[\"--datadir\", input_data.as_mount(), \"--output\", output],\n",
+        "                         runconfig_pipeline_params=None, \n",
+        "                         compute_target=cpu_cluster)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Build and Submit the Pipeline"
+        "## Build and Submit the Experiment"
       ]
     },
     {
@@ -233,9 +253,8 @@
       "source": [
         "from azureml.pipeline.core import Pipeline\n",
         "from azureml.core import Experiment\n",
-        "\n",
-        "pipeline = Pipeline(workspace=ws, steps=[train])\n",
-        "pipeline_run = Experiment(ws, 'train-commandstep-pipeline').submit(pipeline)"
+        "pipeline = Pipeline(workspace=ws, steps=[est_step])\n",
+        "pipeline_run = Experiment(ws, 'Estimator_sample').submit(pipeline)"
       ]
     },
     {
@@ -276,7 +295,7 @@
     "framework": [
       "Azure ML"
     ],
-    "friendly_name": "Azure Machine Learning Pipeline with CommandStep",
+    "friendly_name": "Azure Machine Learning Pipeline with EstimatorStep",
     "kernelspec": {
       "display_name": "Python 3.6",
       "language": "python",
@@ -292,7 +311,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.7"
+      "version": "3.6.7"
     },
     "order_index": 7,
     "star_tag": [
@@ -301,7 +320,7 @@
     "tags": [
       "None"
     ],
-    "task": "Demonstrates the use of CommandStep"
+    "task": "Demonstrates the use of EstimatorStep"
   },
   "nbformat": 4,
   "nbformat_minor": 2

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-estimatorstep.yml

@@ -1,4 +1,4 @@
-name: distributed-pytorch-with-distributeddataparallel
+name: aml-pipelines-how-to-use-estimatorstep
 dependencies:
 - pip:
   - azureml-sdk

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb

@@ -55,9 +55,7 @@
       "metadata": {},
       "source": [
         "### Compute Target\n",
-        "Retrieve an already attached Azure Machine Learning Compute to use in the Pipeline.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+        "Retrieve an already attached Azure Machine Learning Compute to use in the Pipeline."
       ]
     },
     {
@@ -132,7 +130,7 @@
         "\n",
         "pipeline_draft = PipelineDraft.create(ws, name=\"TestPipelineDraft\",\n",
         "                                      description=\"draft description\",\n",
-        "                                      experiment_name=\"pipeline_draft_sample\",\n",
+        "                                      experiment_name=\"helloworld\",\n",
         "                                      pipeline=pipeline,\n",
         "                                      continue_on_step_failure=True,\n",
         "                                      tags={'dev': 'true'},\n",

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb

@@ -42,13 +42,15 @@
       "outputs": [],
       "source": [
         "import azureml.core\n",
-        "from azureml.core import Workspace, Environment, Experiment, Datastore, Dataset, ScriptRunConfig\n",
+        "from azureml.core import Workspace, Experiment, Datastore, Dataset\n",
         "from azureml.core.compute import ComputeTarget, AmlCompute\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.exceptions import ComputeTargetException\n",
         "from azureml.pipeline.steps import HyperDriveStep, HyperDriveStepRun, PythonScriptStep\n",
         "from azureml.pipeline.core import Pipeline, PipelineData, TrainingOutput\n",
+        "from azureml.train.dnn import TensorFlow\n",
+        "# from azureml.train.hyperdrive import *\n",
         "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveConfig, PrimaryMetricGoal\n",
         "from azureml.train.hyperdrive import choice, loguniform\n",
         "\n",
@@ -119,17 +121,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "data_folder = os.path.join(os.getcwd(), 'data/mnist')\n",
-        "os.makedirs(data_folder, exist_ok=True)\n",
+        "os.makedirs('./data/mnist', exist_ok=True)\n",
         "\n",
-        "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/train-images-idx3-ubyte.gz',\n",
-        "                           filename=os.path.join(data_folder, 'train-images.gz'))\n",
-        "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/train-labels-idx1-ubyte.gz',\n",
-        "                           filename=os.path.join(data_folder, 'train-labels.gz'))\n",
-        "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/t10k-images-idx3-ubyte.gz',\n",
-        "                           filename=os.path.join(data_folder, 'test-images.gz'))\n",
-        "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/t10k-labels-idx1-ubyte.gz',\n",
-        "                           filename=os.path.join(data_folder, 'test-labels.gz'))"
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz', filename = './data/mnist/train-images.gz')\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz', filename = './data/mnist/train-labels.gz')\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename = './data/mnist/test-images.gz')\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename = './data/mnist/test-labels.gz')"
       ]
     },
     {
@@ -149,11 +146,11 @@
         "from utils import load_data\n",
         "\n",
         "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the neural network converge faster.\n",
-        "X_train = load_data(os.path.join(data_folder, 'train-images.gz'), False) / np.float32(255.0)\n",
-        "X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / np.float32(255.0)\n",
-        "y_train = load_data(os.path.join(data_folder, 'train-labels.gz'), True).reshape(-1)\n",
-        "y_test = load_data(os.path.join(data_folder, 'test-labels.gz'), True).reshape(-1)\n",
+        "X_train = load_data('./data/mnist/train-images.gz', False) / 255.0\n",
+        "y_train = load_data('./data/mnist/train-labels.gz', True).reshape(-1)\n",
         "\n",
+        "X_test = load_data('./data/mnist/test-images.gz', False) / 255.0\n",
+        "y_test = load_data('./data/mnist/test-labels.gz', True).reshape(-1)\n",
         "\n",
         "count = 0\n",
         "sample_size = 30\n",
@@ -210,8 +207,6 @@
         "## Retrieve or create a Azure Machine Learning compute\n",
         "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Azure Machine Learning Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target.\n",
         "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
-        "\n",
         "If we could not find the compute with the given name in the previous cell, then we will create a new compute here. This process is broken down into the following steps:\n",
         "\n",
         "1. Create the configuration\n",
@@ -282,8 +277,13 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Retrieve an Environment\n",
-        "In this tutorial, we will use one of Azure ML's curated TensorFlow environments for training. Curated environments are available in your workspace by default. Specifically, we will use the TensorFlow 2.0 GPU curated environment."
+        "## Create TensorFlow estimator\n",
+        "Next, we construct an [TensorFlow](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.dnn.tensorflow?view=azure-ml-py) estimator object.\n",
+        "The TensorFlow estimator is providing a simple way of launching a TensorFlow training job on a compute target. It will automatically provide a docker image that has TensorFlow installed -- if additional pip or conda packages are required, their names can be passed in via the `pip_packages` and `conda_packages` arguments and they will be included in the resulting docker.\n",
+        "\n",
+        "The TensorFlow estimator also takes a `framework_version` parameter -- if no version is provided, the estimator will default to the latest version supported by AzureML. Use `TensorFlow.get_supported_versions()` to get a list of all versions supported by your current SDK version or see the [SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn?view=azure-ml-py) for the versions supported in the most current release.\n",
+        "\n",
+        "The TensorFlow estimator also takes a `framework_version` parameter -- if no version is provided, the estimator will default to the latest version supported by AzureML. Use `TensorFlow.get_supported_versions()` to get a list of all versions supported by your current SDK version or see the [SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn?view=azure-ml-py) for the versions supported in the most current release."
       ]
     },
     {
@@ -292,45 +292,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "tf_env = Environment.get(ws, name='AzureML-TensorFlow-2.0-GPU')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Setup an input for the ScriptRunConfig step\n",
-        "You can mount dataset to remote compute."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "data_folder = dataset.as_mount()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure the training job\n",
-        "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "src = ScriptRunConfig(source_directory=script_folder,\n",
-        "                      script='tf_mnist.py',\n",
-        "                      arguments=['--data-folder', data_folder],\n",
-        "                      compute_target=compute_target,\n",
-        "                      environment=tf_env)"
+        "est = TensorFlow(source_directory=script_folder,                 \n",
+        "                 compute_target=compute_target,\n",
+        "                 entry_script='tf_mnist.py', \n",
+        "                 use_gpu=True,\n",
+        "                 framework_version='2.0',\n",
+        "                 pip_packages=['azureml-dataset-runtime[pandas,fuse]'])"
       ]
     },
     {
@@ -394,7 +361,7 @@
       },
       "outputs": [],
       "source": [
-        "hd_config = HyperDriveConfig(run_config=src, \n",
+        "hd_config = HyperDriveConfig(estimator=est, \n",
         "                             hyperparameter_sampling=ps,\n",
         "                             policy=early_termination_policy,\n",
         "                             primary_metric_name='validation_acc', \n",
@@ -403,6 +370,25 @@
         "                             max_concurrent_runs=4)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Add HyperDrive as a step of pipeline\n",
+        "\n",
+        "### Setup an input for the hypderdrive step\n",
+        "You can mount dataset to remote compute."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "data_folder = dataset.as_mount()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -411,6 +397,7 @@
         "HyperDriveStep can be used to run HyperDrive job as a step in pipeline.\n",
         "- **name:** Name of the step\n",
         "- **hyperdrive_config:** A HyperDriveConfig that defines the configuration for this HyperDrive run\n",
+        "- **estimator_entry_script_arguments:** List of command-line arguments for estimator entry script\n",
         "- **inputs:** List of input port bindings\n",
         "- **outputs:** List of output port bindings\n",
         "- **metrics_output:** Optional value specifying the location to store HyperDrive run metrics as a JSON file\n",
@@ -445,6 +432,7 @@
         "hd_step = HyperDriveStep(\n",
         "    name=hd_step_name,\n",
         "    hyperdrive_config=hd_config,\n",
+        "    estimator_entry_script_arguments=['--data-folder', data_folder],\n",
         "    inputs=[data_folder],\n",
         "    outputs=[metrics_data, saved_model])"
       ]

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb

@@ -41,14 +41,14 @@
       "source": [
         "import azureml.core\n",
         "from azureml.core import Workspace, Datastore, Experiment, Dataset\n",
-        "from azureml.data import OutputFileDatasetConfig\n",
         "from azureml.core.compute import AmlCompute\n",
         "from azureml.core.compute import ComputeTarget\n",
         "\n",
         "# Check core SDK version number\n",
         "print(\"SDK version:\", azureml.core.VERSION)\n",
         "\n",
-        "from azureml.pipeline.core import Pipeline\n",
+        "from azureml.data.data_reference import DataReference\n",
+        "from azureml.pipeline.core import Pipeline, PipelineData\n",
         "from azureml.pipeline.steps import PythonScriptStep\n",
         "from azureml.pipeline.core.graph import PipelineParameter\n",
         "\n",
@@ -68,9 +68,7 @@
       "metadata": {},
       "source": [
         "### Compute Targets\n",
-        "#### Retrieve an already attached  Azure Machine Learning Compute\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+        "#### Retrieve an already attached  Azure Machine Learning Compute"
       ]
     },
     {
@@ -142,9 +140,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Define intermediate data using OutputFileDatasetConfig\n",
-        "processed_data1 = OutputFileDatasetConfig(name=\"processed_data1\")\n",
-        "print(\"Output dataset object created\")"
+        "# Define intermediate data using PipelineData\n",
+        "processed_data1 = PipelineData(\"processed_data1\",datastore=def_blob_store)\n",
+        "print(\"PipelineData object created\")"
       ]
     },
     {
@@ -172,7 +170,9 @@
         "\n",
         "trainStep = PythonScriptStep(\n",
         "    script_name=\"train.py\", \n",
-        "        arguments=[\"--input_data\", blob_input_data.as_mount(), \"--output_train\", processed_data1],\n",
+        "        arguments=[\"--input_data\", blob_input_data, \"--output_train\", processed_data1],\n",
+        "    inputs=[blob_input_data],\n",
+        "    outputs=[processed_data1],\n",
         "    compute_target=aml_compute, \n",
         "    source_directory=source_directory\n",
         ")\n",
@@ -195,14 +195,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# extractStep to use the intermediate data produced by trainStep\n",
+        "# extractStep to use the intermediate data produced by step4\n",
         "# This step also produces an output processed_data2\n",
-        "processed_data2 = OutputFileDatasetConfig(name=\"processed_data2\")\n",
+        "processed_data2 = PipelineData(\"processed_data2\", datastore=def_blob_store)\n",
         "source_directory = \"publish_run_extract\"\n",
         "\n",
         "extractStep = PythonScriptStep(\n",
         "    script_name=\"extract.py\",\n",
-        "    arguments=[\"--input_extract\", processed_data1.as_input(), \"--output_extract\", processed_data2],\n",
+        "    arguments=[\"--input_extract\", processed_data1, \"--output_extract\", processed_data2],\n",
+        "    inputs=[processed_data1],\n",
+        "    outputs=[processed_data2],\n",
         "    compute_target=aml_compute, \n",
         "    source_directory=source_directory)\n",
         "print(\"extractStep created\")"
@@ -254,17 +256,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Now define compareStep that takes two inputs (both intermediate data), and produce an output\n",
-        "processed_data3 = OutputFileDatasetConfig(name=\"processed_data3\")\n",
-        "\n",
-        "# You can register the output as dataset after job completion\n",
-        "processed_data3 = processed_data3.register_on_complete(\"compare_result\")\n",
-        "\n",
+        "# Now define step6 that takes two inputs (both intermediate data), and produce an output\n",
+        "processed_data3 = PipelineData(\"processed_data3\", datastore=def_blob_store)\n",
         "source_directory = \"publish_run_compare\"\n",
         "\n",
         "compareStep = PythonScriptStep(\n",
         "    script_name=\"compare.py\",\n",
-        "    arguments=[\"--compare_data1\", processed_data1.as_input(), \"--compare_data2\", processed_data2.as_input(), \"--output_compare\", processed_data3, \"--pipeline_param\", pipeline_param],  \n",
+        "    arguments=[\"--compare_data1\", processed_data1, \"--compare_data2\", processed_data2, \"--output_compare\", processed_data3, \"--pipeline_param\", pipeline_param],\n",
+        "    inputs=[processed_data1, processed_data2],\n",
+        "    outputs=[processed_data3],    \n",
         "    compute_target=aml_compute, \n",
         "    source_directory=source_directory)\n",
         "print(\"compareStep created\")"
@@ -327,7 +327,7 @@
       "outputs": [],
       "source": [
         "# submit a pipeline run\n",
-        "pipeline_run1 = Experiment(ws, 'Pipeline_experiment_sample').submit(pipeline1)\n",
+        "pipeline_run1 = Experiment(ws, 'Pipeline_experiment').submit(pipeline1)\n",
         "# publish a pipeline from the submitted pipeline run\n",
         "published_pipeline2 = pipeline_run1.publish_pipeline(name=\"My_New_Pipeline2\", description=\"My Published Pipeline Description\", version=\"0.1\", continue_on_step_failure=True)\n",
         "published_pipeline2"

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb

@@ -54,9 +54,7 @@
       "metadata": {},
       "source": [
         "### Compute Targets\n",
-        "#### Retrieve an already attached Azure Machine Learning Compute\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+        "#### Retrieve an already attached Azure Machine Learning Compute"
       ]
     },
     {
@@ -261,7 +259,7 @@
         "\n",
         "schedule = Schedule.create(workspace=ws, name=\"My_Schedule\",\n",
         "                           pipeline_id=pub_pipeline_id, \n",
-        "                           experiment_name='Schedule-run-sample',\n",
+        "                           experiment_name='Schedule_Run',\n",
         "                           recurrence=recurrence,\n",
         "                           wait_for_provisioning=True,\n",
         "                           description=\"Schedule Run\")\n",
@@ -447,7 +445,7 @@
         "\n",
         "schedule = Schedule.create(workspace=ws, name=\"My_Schedule\",\n",
         "                           pipeline_id=pub_pipeline_id, \n",
-        "                           experiment_name='Schedule-run-sample',\n",
+        "                           experiment_name='Schedule_Run',\n",
         "                           datastore=datastore,\n",
         "                           wait_for_provisioning=True,\n",
         "                           description=\"Schedule Run\")\n",
@@ -518,7 +516,7 @@
         "\n",
         "schedule = Schedule.create_for_pipeline_endpoint(workspace=ws, name=\"My_Endpoint_Schedule\",\n",
         "                                                 pipeline_endpoint_id=published_pipeline_endpoint_id,\n",
-        "                                                 experiment_name='Schedule-run-sample',\n",
+        "                                                 experiment_name='Schedule_Run',\n",
         "                                                 recurrence=recurrence, description=\"Schedule_Run\",\n",
         "                                                 wait_for_provisioning=True)\n",
         "\n",

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb

@@ -78,9 +78,7 @@
       "source": [
         "####  Initialization, Steps to create a Pipeline\n",
         "\n",
-        "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+        "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
       ]
     },
     {
@@ -555,7 +553,7 @@
       "outputs": [],
       "source": [
         "from azureml.core import Experiment\n",
-        "pipeline_run = Experiment(ws, name=\"submit_endpoint_sample\").submit(pipeline_endpoint_by_name, tags={'endpoint_tag': \"1\"}, pipeline_version=\"0\")"
+        "pipeline_run = Experiment(ws, name=\"submit_from_endpoint\").submit(pipeline_endpoint_by_name, tags={'endpoint_tag': \"1\"}, pipeline_version=\"0\")"
       ]
     }
   ],

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb

@@ -109,9 +109,7 @@
       "metadata": {},
       "source": [
         "## Create or Attach an AmlCompute cluster\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-dataset-and-pipelineparameter.ipynb

@@ -111,9 +111,7 @@
       "metadata": {},
       "source": [
         "## Create or Attach an AmlCompute cluster\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb

@@ -699,162 +699,12 @@
       ]
     },
     {
-      "source": [
-        "### 5. Running demo notebook already added to the Databricks workspace using existing cluster\n",
-        "First you need register DBFS datastore and make sure path_on_datastore does exist in databricks file system, you can browser the files by refering [this](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html).\n",
-        "\n",
-        "Find existing_cluster_id by opeing Azure Databricks UI with Clusters page and in url you will find a string connected with '-' right after \"clusters/\"."
-      ],
       "cell_type": "markdown",
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
       "metadata": {},
-      "outputs": [],
-      "source": [
-        "try:\n",
-        "    dbfs_ds = Datastore.get(workspace=ws, datastore_name='dbfs_datastore')\n",
-        "    print('DBFS Datastore already exists')\n",
-        "except Exception as ex:\n",
-        "    dbfs_ds = Datastore.register_dbfs(ws, datastore_name='dbfs_datastore')\n",
-        "\n",
-        "step_1_input = DataReference(datastore=dbfs_ds, path_on_datastore=\"FileStore\", data_reference_name=\"input\")\n",
-        "step_1_output = PipelineData(\"output\", datastore=dbfs_ds)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dbNbWithExistingClusterStep = DatabricksStep(\n",
-        "    name=\"DBFSReferenceWithExisting\",\n",
-        "    inputs=[step_1_input],\n",
-        "    outputs=[step_1_output],\n",
-        "    notebook_path=notebook_path,\n",
-        "    notebook_params={'myparam': 'testparam', \n",
-        "        'myparam2': pipeline_param},\n",
-        "    run_name='DBFS_Reference_With_Existing',\n",
-        "    compute_target=databricks_compute,\n",
-        "    existing_cluster_id=\"your existing cluster id\",\n",
-        "    allow_reuse=True\n",
-        ")"
-      ]
-    },
-    {
-      "source": [
-        "#### Build and submit the Experiment"
-      ],
-      "cell_type": "markdown",
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "steps = [dbNbWithExistingClusterStep]\n",
-        "pipeline = Pipeline(workspace=ws, steps=steps)\n",
-        "pipeline_run = Experiment(ws, 'DBFS_Reference_With_Existing').submit(pipeline)\n",
-        "pipeline_run.wait_for_completion()"
-      ]
-    },
-    {
-      "source": [
-        "#### View Run Details"
-      ],
-      "cell_type": "markdown",
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(pipeline_run).show()"
-      ]
-    },
-    {
-      "source": [
-        "### 6. Running a Python script in Databricks that currenlty is in local computer with existing cluster\n",
-        "When you access azure blob or data lake storage from an existing (interactive) cluster, you need to ensure the Spark configuration is set up correctly to access this storage and this set up may require the cluster to be restarted.\n",
-        "\n",
-        "If you set permit_cluster_restart to True, AML will check if the spark configuration needs to be updated and restart the cluster for you if required. This will ensure that the storage can be correctly accessed from the Databricks cluster."
-      ],
-      "cell_type": "markdown",
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "step_1_input = DataReference(datastore=def_blob_store, path_on_datastore=\"dbtest\",\n",
-        "                                     data_reference_name=\"input\")\n",
-        "\n",
-        "dbPythonInLocalWithExistingStep = DatabricksStep(\n",
-        "    name=\"DBPythonInLocalMachineWithExisting\",\n",
-        "    inputs=[step_1_input],\n",
-        "    python_script_name=python_script_name,\n",
-        "    source_directory=source_directory,\n",
-        "    run_name='DB_Python_Local_existing_demo',\n",
-        "    compute_target=databricks_compute,\n",
-        "    existing_cluster_id=\"your existing cluster id\",\n",
-        "    allow_reuse=False,\n",
-        "    permit_cluster_restart=True\n",
-        ")"
-      ]
-    },
-    {
-      "source": [
-        "#### Build and submit the Experiment"
-      ],
-      "cell_type": "markdown",
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "steps = [dbPythonInLocalWithExistingStep]\n",
-        "pipeline = Pipeline(workspace=ws, steps=steps)\n",
-        "pipeline_run = Experiment(ws, 'DB_Python_Local_existing_demo').submit(pipeline)\n",
-        "pipeline_run.wait_for_completion()"
-      ]
-    },
-    {
-      "source": [
-        "#### View Run Details"
-      ],
-      "cell_type": "markdown",
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(pipeline_run).show()"
-      ]
-    },
-    {
       "source": [
         "# Next: ADLA as a Compute Target\n",
         "To use ADLA as a compute target from Azure Machine Learning Pipeline, a AdlaStep is used. This [notebook](https://aka.ms/pl-adla) demonstrates the use of AdlaStep in Azure Machine Learning Pipeline."
-      ],
-      "cell_type": "markdown",
-      "metadata": {}
+      ]
     }
   ],
   "metadata": {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb

@@ -101,7 +101,7 @@
       "metadata": {},
       "source": [
         "## Create an Azure ML experiment\n",
-        "Let's create an experiment named \"automlstep-sample\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure.\n",
+        "Let's create an experiment named \"automlstep-classification\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure.\n",
         "\n",
         "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
       ]
@@ -113,7 +113,7 @@
       "outputs": [],
       "source": [
         "# Choose a name for the run history container in the workspace.\n",
-        "experiment_name = 'automlstep-sample'\n",
+        "experiment_name = 'automlstep-classification'\n",
         "project_folder = './project'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
@@ -125,9 +125,7 @@
       "metadata": {},
       "source": [
         "### Create or Attach an AmlCompute cluster\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
       ]
     },
     {
@@ -148,7 +146,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',# for GPU, use \"STANDARD_NC6\"\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',# for GPU, use \"STANDARD_NC6\"\n",
         "                                                           #vm_priority = 'lowpriority', # optional\n",
         "                                                           max_nodes=4)\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep-r.ipynb

@@ -1,345 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-estimatorstep.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# How to use CommandStep in Azure ML Pipelines\n",
-        "\n",
-        "This notebook shows how to use the CommandStep with Azure Machine Learning Pipelines for running R scripts in a pipeline.\n",
-        "\n",
-        "The example shows training a model in R to predict probability of fatality for vehicle crashes.\n",
-        "\n",
-        "\n",
-        "## Prerequisite:\n",
-        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](https://aka.ms/pl-config) to:\n",
-        "    * install the Azure ML SDK\n",
-        "    * create a workspace and its configuration file (`config.json`)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's get started. First let's import some Python libraries."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "# check core SDK version number\n",
-        "print(\"Azure ML SDK Version: \", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize workspace\n",
-        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\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": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create or Attach existing AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_D2_V2` CPU VMs. This process is broken down into 3 steps:\n",
-        "1. create the configuration (this step is local and only takes a second)\n",
-        "2. create the cluster (this step will take about **20 seconds**)\n",
-        "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell"
-      ]
-    },
-    {
-      "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 cluster\n",
-        "cluster_name = \"cpu-cluster\"\n",
-        "\n",
-        "try:\n",
-        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2', max_nodes=4)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "\n",
-        "    # can poll for a minimum number of nodes and for a specific timeout. \n",
-        "    # if no min node count is provided it uses the scale settings for the cluster\n",
-        "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
-        "\n",
-        "# use get_status() to get a detailed status for the current cluster. \n",
-        "print(compute_target.get_status().serialize())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'cpu-cluster' of type `AmlCompute`."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create a CommandStep\n",
-        "CommandStep adds a step to run a command in a Pipeline. For the full set of configurable options see the CommandStep [reference docs](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.commandstep?view=azure-ml-py).\n",
-        "\n",
-        "- **name:** Name of the step\n",
-        "- **runconfig:** ScriptRunConfig object. You can configure a ScriptRunConfig object as you would for a standalone non-pipeline run and pass it in to this parameter. If using this option, you do not have to specify the `command`, `source_directory`, `compute_target` parameters of the CommandStep constructor as they are already defined in your ScriptRunConfig.\n",
-        "- **runconfig_pipeline_params:** Override runconfig properties at runtime using key-value pairs each with name of the runconfig property and PipelineParameter for that property\n",
-        "- **command:** The command to run or path of the executable/script relative to `source_directory`. It is required unless the `runconfig` parameter is specified. It can be specified with string arguments in a single string or with input/output/PipelineParameter in a list.\n",
-        "- **source_directory:** A folder containing the script and other resources used in the step.\n",
-        "- **compute_target:** Compute target to use \n",
-        "- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs. If this is false, a new run will always be generated for this step during pipeline execution.\n",
-        "- **version:** Optional version tag to denote a change in functionality for the step\n",
-        "\n",
-        "> The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Configure environment\n",
-        "\n",
-        "Configure the environment for the train step. In this example we will create an environment from the Dockerfile we have included.\n",
-        "\n",
-        "> Azure ML currently requires Python as an implicit dependency, so Python must installed in your image even if your training script does not have this dependency."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Environment\n",
-        "import os\n",
-        "\n",
-        "src_dir = 'commandstep_r'\n",
-        "\n",
-        "env = Environment.from_dockerfile(name='r_env', dockerfile=os.path.join(src_dir, 'Dockerfile'))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Configure input training dataset\n",
-        "\n",
-        "This tutorial uses data from the US National Highway Traffic Safety Administration. This dataset includes data from over 25,000 car crashes in the US, with variables you can use to predict the likelihood of a fatality. We have included an Rdata file that includes the accidents data for analysis.\n",
-        "\n",
-        "Here we use the workspace's default datastore to upload the training data file (**accidents.Rd**); in practice you can use any datastore you want."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "datastore = ws.get_default_datastore()\n",
-        "data_ref = datastore.upload_files(files=[os.path.join(src_dir, 'accidents.Rd')], target_path='accidentdata')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now create a FileDataset from the data, which will be used as an input to the train step."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Dataset\n",
-        "dataset = Dataset.File.from_files(datastore.path('accidentdata'))\n",
-        "dataset"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now create a ScriptRunConfig that configures the training run. Note that in the `command` we include the input dataset for the training data.\n",
-        "\n",
-        "> For detailed guidance on how to move data in pipelines for input and output data, see the documentation [Moving data into and between ML pipelines](https://docs.microsoft.com/azure/machine-learning/how-to-move-data-in-out-of-pipelines)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import ScriptRunConfig\n",
-        "\n",
-        "train_config = ScriptRunConfig(source_directory=src_dir,\n",
-        "                            command=['Rscript accidents.R --data_folder', dataset.as_mount(), '--output_folder outputs'],\n",
-        "                            compute_target=compute_target,\n",
-        "                            environment=env)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now create a CommandStep and pass in the ScriptRunConfig object to the `runconfig` parameter."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.pipeline.steps import CommandStep\n",
-        "\n",
-        "train = CommandStep(name='train', runconfig=train_config)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Build and Submit the Pipeline"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.pipeline.core import Pipeline\n",
-        "from azureml.core import Experiment\n",
-        "\n",
-        "pipeline = Pipeline(workspace=ws, steps=[train])\n",
-        "pipeline_run = Experiment(ws, 'r-commandstep-pipeline').submit(pipeline)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## View Run Details"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(pipeline_run).show()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "minxia"
-      }
-    ],
-    "category": "tutorial",
-    "compute": [
-      "AML Compute"
-    ],
-    "datasets": [
-      "Custom"
-    ],
-    "deployment": [
-      "None"
-    ],
-    "exclude_from_index": false,
-    "framework": [
-      "Azure ML"
-    ],
-    "friendly_name": "Azure Machine Learning Pipeline with CommandStep for R",
-    "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": "3.7.7"
-    },
-    "order_index": 7,
-    "star_tag": [
-      "None"
-    ],
-    "tags": [
-      "None"
-    ],
-    "task": "Demonstrates the use of CommandStep for running R scripts"
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb

@@ -134,9 +134,7 @@
       "metadata": {},
       "source": [
         "#### Retrieve or create an Aml compute\n",
-        "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's get the default Aml Compute in the current workspace. We will then run the training script on this compute target.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+        "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's get the default Aml Compute in the current workspace. We will then run the training script on this compute target."
       ]
     },
     {
@@ -430,7 +428,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "pipeline_run1 = Experiment(ws, 'Data_dependency_sample').submit(pipeline1)\n",
+        "pipeline_run1 = Experiment(ws, 'Data_dependency').submit(pipeline1)\n",
         "print(\"Pipeline is submitted for execution\")"
       ]
     },

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-notebook-runner-step.ipynb

@@ -147,9 +147,7 @@
       "metadata": {},
       "source": [
         "### Create or Attach an AmlCompute cluster\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.computetarget?view=azure-ml-py) for your remote run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+        "You will need to create a [compute target](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.computetarget?view=azure-ml-py) for your remote run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/commandstep_r/Dockerfile

@@ -1,11 +0,0 @@
-FROM rocker/tidyverse:4.0.0-ubuntu18.04
- 
-# Install python
-RUN apt-get update -qq && \
- apt-get install -y python3
- 
-# Create link for python
-RUN ln -f /usr/bin/python3 /usr/bin/python
-
-# Install additional R packages
-RUN R -e "install.packages(c('optparse'), repos = 'https://cloud.r-project.org/')"

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/commandstep_r/accidents.R

@@ -1,34 +0,0 @@
-#' Copyright(c) Microsoft Corporation.
-#' Licensed under the MIT license.
-
-library(optparse)
-
-options <- list(
-  make_option(c("-d", "--data_folder")),
-  make_option(c("--output_folder"))
-  
-)
-
-opt_parser <- OptionParser(option_list = options)
-opt <- parse_args(opt_parser)
-
-paste(opt$data_folder)
-
-accidents <- readRDS(file.path(opt$data_folder, "accidents.Rd"))
-summary(accidents)
-
-mod <- glm(dead ~ dvcat + seatbelt + frontal + sex + ageOFocc + yearVeh + airbag  + occRole, family=binomial, data=accidents)
-summary(mod)
-predictions <- factor(ifelse(predict(mod)>0.1, "dead","alive"))
-accuracy <- mean(predictions == accidents$dead)
-
-# make directory for output dir
-output_dir = opt$output_folder
-if (!dir.exists(output_dir)){
-  dir.create(output_dir)
-}
-
-# save model
-model_path = file.path(output_dir, "model.rds")
-saveRDS(mod, file = model_path)
-message("Model saved")

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/commandstep_train/conda_dependencies.yml

@@ -1,8 +0,0 @@
-channels:
-  - conda-forge
-dependencies:
-  - python=3.7
-  - pip:
-    - azureml-defaults
-    - tensorflow-gpu==2.3.0
-    - horovod==0.19.5

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/commandstep_train/train.py

@@ -1,120 +0,0 @@
-# Copyright 2019 Uber Technologies, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Script adapted from: https://github.com/horovod/horovod/blob/master/examples/tensorflow2_keras_mnist.py
-# ==============================================================================
-
-import tensorflow as tf
-import horovod.tensorflow.keras as hvd
-
-import os
-import argparse
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--learning-rate", "-lr", type=float, default=0.001)
-parser.add_argument("--epochs", type=int, default=24)
-parser.add_argument("--steps-per-epoch", type=int, default=500)
-args = parser.parse_args()
-
-# Horovod: initialize Horovod.
-hvd.init()
-
-# Horovod: pin GPU to be used to process local rank (one GPU per process)
-gpus = tf.config.experimental.list_physical_devices("GPU")
-for gpu in gpus:
-    tf.config.experimental.set_memory_growth(gpu, True)
-if gpus:
-    tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], "GPU")
-
-(mnist_images, mnist_labels), _ = tf.keras.datasets.mnist.load_data(
-    path="mnist-%d.npz" % hvd.rank()
-)
-
-dataset = tf.data.Dataset.from_tensor_slices(
-    (
-        tf.cast(mnist_images[..., tf.newaxis] / 255.0, tf.float32),
-        tf.cast(mnist_labels, tf.int64),
-    )
-)
-dataset = dataset.repeat().shuffle(10000).batch(128)
-
-mnist_model = tf.keras.Sequential(
-    [
-        tf.keras.layers.Conv2D(32, [3, 3], activation="relu"),
-        tf.keras.layers.Conv2D(64, [3, 3], activation="relu"),
-        tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
-        tf.keras.layers.Dropout(0.25),
-        tf.keras.layers.Flatten(),
-        tf.keras.layers.Dense(128, activation="relu"),
-        tf.keras.layers.Dropout(0.5),
-        tf.keras.layers.Dense(10, activation="softmax"),
-    ]
-)
-
-# Horovod: adjust learning rate based on number of GPUs.
-scaled_lr = args.learning_rate * hvd.size()
-opt = tf.optimizers.Adam(scaled_lr)
-
-# Horovod: add Horovod DistributedOptimizer.
-opt = hvd.DistributedOptimizer(opt)
-
-# Horovod: Specify `experimental_run_tf_function=False` to ensure TensorFlow
-# uses hvd.DistributedOptimizer() to compute gradients.
-mnist_model.compile(
-    loss=tf.losses.SparseCategoricalCrossentropy(),
-    optimizer=opt,
-    metrics=["accuracy"],
-    experimental_run_tf_function=False,
-)
-
-callbacks = [
-    # Horovod: broadcast initial variable states from rank 0 to all other processes.
-    # This is necessary to ensure consistent initialization of all workers when
-    # training is started with random weights or restored from a checkpoint.
-    hvd.callbacks.BroadcastGlobalVariablesCallback(0),
-    # Horovod: average metrics among workers at the end of every epoch.
-    #
-    # Note: This callback must be in the list before the ReduceLROnPlateau,
-    # TensorBoard or other metrics-based callbacks.
-    hvd.callbacks.MetricAverageCallback(),
-    # Horovod: using `lr = 1.0 * hvd.size()` from the very beginning leads to worse final
-    # accuracy. Scale the learning rate `lr = 1.0` ---> `lr = 1.0 * hvd.size()` during
-    # the first three epochs. See https://arxiv.org/abs/1706.02677 for details.
-    hvd.callbacks.LearningRateWarmupCallback(
-        warmup_epochs=3, initial_lr=scaled_lr, verbose=1
-    ),
-]
-
-# Horovod: save checkpoints only on worker 0 to prevent other workers from corrupting them.
-if hvd.rank() == 0:
-    output_dir = "./outputs"
-    os.makedirs(output_dir, exist_ok=True)
-    callbacks.append(
-        tf.keras.callbacks.ModelCheckpoint(
-            os.path.join(output_dir, "checkpoint-{epoch}.h5")
-        )
-    )
-
-# Horovod: write logs on worker 0.
-verbose = 1 if hvd.rank() == 0 else 0
-
-# Train the model.
-# Horovod: adjust number of steps based on number of GPUs.
-mnist_model.fit(
-    dataset,
-    steps_per_epoch=args.steps_per_epoch // hvd.size(),
-    callbacks=callbacks,
-    epochs=args.epochs,
-    verbose=verbose,
-)

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/estimator_train/dummy_train.py

@@ -0,0 +1,30 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+import argparse
+import os
+
+print("*********************************************************")
+print("Hello Azure ML!")
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--datadir', type=str, help="data directory")
+parser.add_argument('--output', type=str, help="output")
+args = parser.parse_args()
+
+print("Argument 1: %s" % args.datadir)
+print("Argument 2: %s" % args.output)
+
+if not (args.output is None):
+    os.makedirs(args.output, exist_ok=True)
+    print("%s created" % args.output)
+
+try:
+    from azureml.core import Run
+    run = Run.get_context()
+    print("Log Fibonacci numbers.")
+    run.log_list('Fibonacci numbers', [0, 1, 1, 2, 3, 5, 8, 13, 21, 34])
+    run.complete()
+except:
+    print("Warning: you need to install Azure ML SDK in order to log metrics.")
+
+print("*********************************************************")

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/publish_run_compare/compare.py

@@ -22,6 +22,3 @@ print("Argument 4: %s" % args.pipeline_param)
 if not (args.output_compare is None):
     os.makedirs(args.output_compare, exist_ok=True)
     print("%s created" % args.output_compare)
-
-with open(os.path.join(args.output_compare, 'compare.txt'), 'w') as fw:
-    fw.write('here is the compare result')

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/publish_run_extract/extract.py

@@ -19,8 +19,3 @@ print("Argument 2: %s" % args.output_extract)
 if not (args.output_extract is None):
     os.makedirs(args.output_extract, exist_ok=True)
     print("%s created" % args.output_extract)
-
-with open(os.path.join(args.input_extract, '20news.pkl'), 'rb') as f:
-    content = f.read()
-    with open(os.path.join(args.output_extract, '20news.pkl'), 'wb') as fw:
-        fw.write(content)

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/publish_run_train/train.py

@@ -20,8 +20,3 @@ print("Argument 2: %s" % args.output_train)
 if not (args.output_train is None):
     os.makedirs(args.output_train, exist_ok=True)
     print("%s created" % args.output_train)
-
-with open(os.path.join(args.input_data, '20news.pkl'), 'rb') as f:
-    content = f.read()
-    with open(os.path.join(args.output_train, '20news.pkl'), 'wb') as fw:
-        fw.write(content)

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb

@@ -225,9 +225,7 @@
       "metadata": {},
       "source": [
         "### Setup Compute\n",
-        "#### Create new or use an existing compute\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+        "#### Create new or use an existing compute"
       ]
     },
     {
@@ -247,7 +245,7 @@
         "    aml_compute = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=4)\n",
         "    aml_compute = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
@@ -681,6 +679,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "import logging\n",
         "from azureml.train.automl import AutoMLConfig\n",
         "\n",
         "# Change iterations to a reasonable number (50) to get better accuracy\n",
@@ -783,8 +782,8 @@
         "    path =  download_path + '/azureml/' + output_folder + '/' + output_name\n",
         "    return path\n",
         "\n",
-        "def fetch_df(current_step, output_name):\n",
-        "    output_data = current_step.get_output_data(output_name)    \n",
+        "def fetch_df(step, output_name):\n",
+        "    output_data = step.get_output_data(output_name)    \n",
         "    download_path = './outputs/' + output_name\n",
         "    output_data.download(download_path, overwrite=True)\n",
         "    df_path = get_download_path(download_path, output_name) + '/processed.parquet'\n",
@@ -940,6 +939,32 @@
         "#RunDetails(automl_run).show()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Retrieve all Child runs\n",
+        "\n",
+        "We 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(automl_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": {},

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.yml

@@ -2,7 +2,6 @@ name: nyc-taxi-data-regression-model-building
 dependencies:
 - pip:
   - azureml-sdk
-  - certifi
   - azureml-widgets
   - azureml-opendatasets
   - azureml-train-automl

how-to-use-azureml/machine-learning-pipelines/parallel-run/README.md

@@ -122,8 +122,4 @@ pipeline_run.wait_for_completion(show_output=True)
 -  [tabular-dataset-inference-iris.ipynb](./tabular-dataset-inference-iris.ipynb) demonstrates how to run batch inference on an IRIS dataset using TabularDataset.
 -  [pipeline-style-transfer.ipynb](../pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb) demonstrates using ParallelRunStep in multi-step pipeline and using output from one step as input to ParallelRunStep.
 
-# Troubleshooting guide
-
-- [Troubleshooting the ParallelRunStep](https://aka.ms/prstsg) includes answers to frequently asked questions. You can find more references there.
-
 ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/parallel-run/README.png)

how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb

@@ -24,9 +24,9 @@
         "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n",
         "\n",
         "> **Tip**\n",
-        "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",
+        "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",
         "\n",
-        "In this example will be take a digit identification model already-trained on MNIST dataset using the [AzureML training with deep learning example notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb), and run that trained model on some of the MNIST test images in batch.  \n",
+        "In this example will be take a digit identification model already-trained on MNIST dataset using the [AzureML training with deep learning example notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb), and run that trained model on some of the MNIST test images in batch.  \n",
         "\n",
         "The input dataset used for this notebook differs from a standard MNIST dataset in that it has been converted to PNG images to demonstrate use of files as inputs to Batch Inference. A sample of PNG-converted images of the MNIST dataset were take from [this repository](https://github.com/myleott/mnist_png). \n",
         "\n",
@@ -86,8 +86,6 @@
         "### Create or Attach existing compute resource\n",
         "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n",
         "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
-        "\n",
         "**Creation of compute takes approximately 5 minutes. If the AmlCompute with that name is already in your workspace the code will skip the creation process.**"
       ]
     },
@@ -182,7 +180,8 @@
       "metadata": {},
       "source": [
         "### Create a FileDataset\n",
-        "A [FileDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) references single or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred.\n",
+        "A [FileDataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) references single or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred.",
+        "\n",
         "You can use dataset objects as inputs. Register the datasets to the workspace if you want to reuse them later."
       ]
     },
@@ -225,7 +224,7 @@
       "metadata": {},
       "source": [
         "### Intermediate/Output Data\n",
-        "Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps."
+        "Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps."
       ]
     },
     {
@@ -277,7 +276,7 @@
         "### Register the model with Workspace\n",
         "A registered model is a logical container for one or more files that make up your model. For example, if you have a model that's stored in multiple files, you can register them as a single model in the workspace. After you register the files, you can then download or deploy the registered model and receive all the files that you registered.\n",
         "\n",
-        "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric. Learn more about registering models [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-and-where#registermodel) "
+        "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric. Learn more about registering models [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where#registermodel) "
       ]
     },
     {
@@ -363,6 +362,7 @@
         "                                                          \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n",
         "batch_env = Environment(name=\"batch_environment\")\n",
         "batch_env.python.conda_dependencies = batch_conda_deps\n",
+        "batch_env.docker.enabled = True\n",
         "batch_env.docker.base_image = DEFAULT_CPU_IMAGE"
       ]
     },
@@ -379,6 +379,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.pipeline.core import PipelineParameter\n",
         "from azureml.pipeline.steps import ParallelRunStep, ParallelRunConfig\n",
         "\n",
         "parallel_run_config = ParallelRunConfig(\n",