Merge pull request Azure#962 from Azure/release_update_stablev2/Release-11

update samples from Release-11 as a part of 1.5.0 SDK stable release

Author: harneetvirk

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.5.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/automated-machine-learning/README.md

@@ -1,8 +1,8 @@
 # Table of Contents
 1. [Automated ML Introduction](#introduction)
-1. [Setup using Azure Notebooks](#jupyter)
-1. [Setup using Azure Databricks](#databricks)
+1. [Setup using Compute Instances](#jupyter)
 1. [Setup using a Local Conda environment](#localconda)
+1. [Setup using Azure Databricks](#databricks)
 1. [Automated ML SDK Sample Notebooks](#samples)
 1. [Documentation](#documentation)
 1. [Running using python command](#pythoncommand)
@@ -21,13 +21,13 @@ Below are the three execution environments supported by automated ML.
 
 
  <a name="jupyter"></a>
-## Setup using Notebook VMs - Jupyter based notebooks from a Azure VM
+## Setup using Compute Instances - Jupyter based notebooks from a Azure Virtual Machine
 
 1. Open the [ML Azure portal](https://ml.azure.com)
 1. Select Compute
-1. Select Notebook VMs
+1. Select Compute Instances
 1. Click New
-1. Type a name for the Vm and select a VM type
+1. Type a Compute Name, select a Virtual Machine type and select a Virtual Machine size
 1. Click Create
 
 <a name="localconda"></a>

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

@@ -5,19 +5,18 @@ dependencies:
 - pip<=19.3.1
 - nomkl
 - python>=3.5.2,<3.6.8
-- wheel==0.30.0
 - nb_conda
 - matplotlib==2.1.0
 - numpy>=1.16.0,<=1.16.2
 - cython
 - urllib3<1.24
-- scipy>=1.0.0,<=1.1.0
+- scipy==1.4.1
 - scikit-learn>=0.19.0,<=0.20.3
 - pandas>=0.22.0,<0.23.0
 - py-xgboost<=0.80
-- fbprophet==0.5
-- pytorch=1.1.0
-- cudatoolkit=9.0
+- conda-forge::fbprophet==0.5
+- pytorch::pytorch=1.4.0
+- cudatoolkit=10.1.243
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
@@ -30,8 +29,3 @@ dependencies:
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz  
-
-channels:
-- anaconda
-- conda-forge
-- pytorch

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

@@ -1,36 +1,33 @@
-name: azure_automl
+name: automl_env_master
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
 - pip<=19.3.1
 - python>=3.5.2,<3.6.8
-- wheel==0.30.0
 - nb_conda
 - matplotlib==2.1.0
 - numpy>=1.16.0,<=1.16.2
 - cython
 - urllib3<1.24
-- scipy>=1.0.0,<=1.1.0
+- scipy==1.4.1
 - scikit-learn>=0.19.0,<=0.20.3
 - pandas>=0.22.0,<=0.23.4
+- testpath=0.3.1
 - py-xgboost<=0.90
-- fbprophet==0.5
-- pytorch=1.1.0
-- cudatoolkit=9.0
+- conda-forge::fbprophet==0.5
+- pytorch::pytorch=1.4.0
+- cudatoolkit=10.1.243
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-defaults
+  - --extra-index-url https://azuremlsdktestpypi.azureedge.net/sdk-release/master/588E708E0DF342C4A80BD954289657CF
+  - --extra-index-url https://dataprepdownloads.azureedge.net/pypi/weekly-rc-932B96D048E011E8B56608/latest/
+  - azureml-defaults<0.1.50
   - azureml-dataprep[pandas]
-  - azureml-train-automl
-  - azureml-train
-  - azureml-widgets
-  - azureml-pipeline
+  - azureml-train-automl<0.1.50
+  - azureml-train<0.1.50
+  - azureml-widgets<0.1.50
+  - azureml-pipeline<0.1.50
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-
-channels:
-- anaconda
-- conda-forge
-- pytorch

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

@@ -41,7 +41,7 @@
         "\n",
         "In this example we use the UCI Bank Marketing dataset to showcase how you can use AutoML for a  classification problem and deploy it to an Azure Container Instance (ACI). The classification goal is to predict if the client will subscribe to a term deposit with the bank.\n",
         "\n",
-        "If you are using an Azure Machine Learning Notebook VM, 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",
+        "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",
         "Please find the ONNX related documentations [here](https://github.com/onnx/onnx).\n",
         "\n",
@@ -105,7 +105,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.5.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -643,7 +643,7 @@
         "\n",
         "### Retrieve the Best Model\n",
         "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last 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 best pipeline from our iterations.  The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
       ]
     },
     {

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

@@ -42,7 +42,7 @@
         "\n",
         "This notebook is using remote compute to train the model.\n",
         "\n",
-        "If you are using an Azure Machine Learning [Notebook VM](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-1st-experiment-sdk-setup), 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",
+        "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",
@@ -93,7 +93,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.5.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -322,7 +322,7 @@
         "\n",
         "### Retrieve the Best Model\n",
         "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last 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 best pipeline from our iterations. The `get_output` method returns the best run and the fitted model.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
       ]
     },
     {

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

@@ -97,7 +97,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.5.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

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

@@ -2,8 +2,7 @@
 import argparse
 from azureml.core import Run
 from sklearn.externals import joblib
-from azureml.automl.core._vendor.automl.client.core.common import metrics
-from automl.client.core.common import constants
+from azureml.automl.core.shared import constants, metrics
 from azureml.core.model import Model
 
 

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

@@ -88,7 +88,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.5.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

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

@@ -114,7 +114,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.5.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },