Merge pull request Azure#396 from rastala/master

version 1.0.41

Author: rastala

README.md

@@ -55,62 +55,18 @@ Visit following repos to see projects contributed by Azure ML users:
  - [Fine tune natural language processing 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)
 
- ## Azure Machine Learning Resources & Links
-## Product Documentation
-- [Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/)
-- [Azure Machine Learning Studio](https://docs.microsoft.com/en-us/azure/machine-learning/studio/)
-
-## Product Team Blogs
-- [What’s new in Azure Machine Learning service](https://aka.ms/aml-blog-whats-new)
-- [Announcing automated ML capability in Azure Machine Learning](https://aka.ms/aml-blog-automl)
-- [Experimentation using Azure Machine Learning](https://aka.ms/aml-blog-experimentation)
-- [Azure AI – Making AI real for business](https://aka.ms/aml-blog-overview)
-
-## Community Blogs
-- [Power Bat – How Spektacom is Powering the Game of Cricket with Microsoft AI](https://blogs.technet.microsoft.com/machinelearning/2018/10/11/power-bat-how-spektacom-is-powering-the-game-of-cricket-with-microsoft-ai/)
-
-## Ignite 2018 Public Preview Launch Sessions
-- [AI with Azure Machine Learning services: Simplifying the data science process](https://myignite.techcommunity.microsoft.com/sessions/66248)
-- [AI TechTalk: Azure Machine Learning SDK - a walkthrough](https://myignite.techcommunity.microsoft.com/sessions/66265) 
-- [AI for an intelligent cloud and intelligent edge: Discover, deploy, and manage with Azure ML services](https://myignite.techcommunity.microsoft.com/sessions/65389)
-- [Generating high quality models efficiently using Automated ML and Hyperparameter Tuning](https://myignite.techcommunity.microsoft.com/sessions/66245)
-- [AI for pros: Deep learning with PyTorch using the Azure Data Science Virtual Machine and scaling training with Azure ML](https://myignite.techcommunity.microsoft.com/sessions/66244)
-
-## Get-started Videos on YouTube
-- [Get started with Python SDK](https://youtu.be/VIsXeTuW3FU)
-- [Get started from Azure Portal](https://youtu.be/lCkYUHV86Mk)
-
-
-## Third Party Articles
-- [Azure’s new machine learning features embrace Python](https://www.infoworld.com/article/3306840/azure/azures-new-machine-learning-features-embrace-python.html) (InfoWorld)
-- [How to use Azure ML in Windows 10](https://www.infoworld.com/article/3308381/azure/how-to-use-azure-ml-in-windows-10.html) (InfoWorld)
-- [How Azure ML Streamlines Cloud-based Machine Learning](https://thenewstack.io/how-the-azure-ml-streamlines-cloud-based-machine-learning/) (The New Stack)
-- [Facebook launches PyTorch 1.0 with integrations for Google Cloud, AWS, and Azure Machine Learning](https://venturebeat.com/2018/10/02/facebook-launches-pytorch-1-0-integrations-for-google-cloud-aws-and-azure-machine-learning/) (VentureBeat)
-- [How Microsoft Uses Machine Learning to Help You Build Machine Learning Pipelines](https://towardsdatascience.com/how-microsoft-uses-machine-learning-to-help-you-build-machine-learning-pipelines-be75f710613b) (Towards Data Science)
-- [Microsoft's Machine Learning Tools for Developers Get Smarter](https://techcrunch.com/2018/09/24/microsofts-machine-learning-tools-for-developers-get-smarter/) (TechCrunch)
-- [Microsoft introduces Azure service to automatically build AI models](https://venturebeat.com/2018/09/24/microsoft-introduces-azure-service-to-automatically-build-ai-models/) (VentureBeat)
-
-## Community Projects
-- [Use Papermill with Azure ML](https://github.com/jreynolds01/papermill_execution_azureml/)
-- [Fashion MNIST](https://github.com/amynic/azureml-sdk-fashion)
-- Keras on Databricks
-- [Samples from CSS](https://github.com/Azure/AMLSamples)
-
-
-## Azure Machine Learning Studio Resources
-- [A-Z Machine Learning using Azure Machine Learning (AzureML)](https://www.udemy.com/machine-learning-using-azureml/)
-- [Machine Learning In The Cloud With Azure Machine Learning](https://www.udemy.com/machine-learning-in-the-cloud-with-azure-machine-learning/)
-- [How to Become A Data Scientist Using Azure Machine Learning](https://www.udemy.com/azure-machine-learning-introduction/)
-- [Learn Azure Machine Learning from scratch](https://www.udemy.com/learn-azure-machine-learning-from-scratch/)
-- [Azure Machine Learning Studio PowerShell Module](https://aka.ms/amlps)
-
-## Forum Help
-- [Azure Machine Learning service](https://social.msdn.microsoft.com/Forums/en-US/home?forum=AzureMachineLearningService)
-- [Azure Machine Learning Studio](https://social.msdn.microsoft.com/forums/azure/en-US/home?forum=MachineLearning)
+## Data/Telemetry 
+This repository collects usage data and sends it to Mircosoft 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
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/README.png)"
+```
+This URL will be slightly different depending on the file. 
 
 ## 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)
+This repository collects usage data and sends it to Mircosoft 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:
 
@@ -119,5 +75,4 @@ To opt out of tracking, please go to the raw markdown or .ipynb files and remove
 ```
 This URL will be slightly different depending on the file. 
 
-
  ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/README.png)

how-to-use-azureml/README.md

@@ -2,7 +2,7 @@
 
 Learn how to use Azure Machine Learning services for experimentation and model management.
 
-If you are using an Azure Machine Learning Notebook VM, you are all set.  Otherwise, go through the [configuration Notebook](../configuration.ipynb) first if you haven't already to establish your connection to the AzureML Workspace. Then, run the notebooks in following recommended order.
+As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
 
 * [train-within-notebook](./training/train-within-notebook): Train a model hile tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
 * [train-on-local](./training/train-on-local): Learn how to submit a run to local computer and use Azure ML managed run configuration.
@@ -15,6 +15,3 @@ If you are using an Azure Machine Learning Notebook VM, you are all set.  Otherw
 * [enable-app-insights-in-production-service](./deployment/enable-app-insights-in-production-service) Learn how to use App Insights with production web service.
 
 Find 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/).
-
-
- ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/README.png)

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

@@ -10,7 +10,7 @@ dependencies:
 - urllib3<1.24
 - scipy>=1.0.0,<=1.1.0
 - scikit-learn>=0.19.0,<=0.20.3
-- pandas>=0.22.0,<0.23.0
+- pandas>=0.22.0,<=0.23.4
 - py-xgboost<=0.80
 
 - pip:

how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb

@@ -9,6 +9,13 @@
         "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/classification-with-deployment/auto-ml-classification-with-deployment.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -18,7 +25,7 @@
         "\n",
         "## Contents\n",
         "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#setup)\n",
+        "1. [Setup](#Setup)\n",
         "1. [Train](#Train)\n",
         "1. [Deploy](#Deploy)\n",
         "1. [Test](#Test)"
@@ -49,7 +56,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Setup \n",
+        "## Setup\n",
         "\n",
         "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
       ]
@@ -500,4 +507,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.ipynb

@@ -9,6 +9,13 @@
         "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/classification-with-onnx/auto-ml-classification-with-onnx.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -66,11 +73,12 @@
         "import numpy as np\n",
         "import pandas as pd\n",
         "from sklearn import datasets\n",
+        "from sklearn.model_selection import train_test_split\n",
         "\n",
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig"
+        "from azureml.train.automl import AutoMLConfig, constants"
       ]
     },
     {
@@ -106,7 +114,7 @@
       "source": [
         "## Data\n",
         "\n",
-        "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
+        "This uses scikit-learn's [load_iris](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) method."
       ]
     },
     {
@@ -115,11 +123,17 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "digits = datasets.load_digits()\n",
+        "iris = datasets.load_iris()\n",
+        "X_train, X_test, y_train, y_test = train_test_split(iris.data, \n",
+        "                                                    iris.target, \n",
+        "                                                    test_size=0.2, \n",
+        "                                                    random_state=0)\n",
         "\n",
-        "# Exclude the first 100 rows from training so that they can be used for test.\n",
-        "X_train = digits.data[100:,:]\n",
-        "y_train = digits.target[100:]"
+        "# Convert the X_train and X_test to pandas DataFrame and set column names,\n",
+        "# This is needed for initializing the input variable names of ONNX model, \n",
+        "# and the prediction with the ONNX model using the inference helper.\n",
+        "X_train = pd.DataFrame(X_train, columns=['c1', 'c2', 'c3', 'c4'])\n",
+        "X_test = pd.DataFrame(X_test, columns=['c1', 'c2', 'c3', 'c4'])"
       ]
     },
     {
@@ -155,9 +169,10 @@
         "                             primary_metric = 'AUC_weighted',\n",
         "                             iteration_timeout_minutes = 60,\n",
         "                             iterations = 10,\n",
-        "                             verbosity = logging.INFO,\n",
+        "                             verbosity = logging.INFO,                             \n",
         "                             X = X_train, \n",
         "                             y = y_train,\n",
+        "                             preprocess=True,\n",
         "                             enable_onnx_compatible_models=True,\n",
         "                             path = project_folder)"
       ]
@@ -253,6 +268,65 @@
         "onnx_fl_path = \"./best_model.onnx\"\n",
         "OnnxConverter.save_onnx_model(onnx_mdl, onnx_fl_path)"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Predict with the ONNX model, using onnxruntime package"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "import json\n",
+        "from azureml.automl.core.onnx_convert import OnnxConvertConstants\n",
+        "\n",
+        "if sys.version_info < OnnxConvertConstants.OnnxIncompatiblePythonVersion:\n",
+        "    python_version_compatible = True\n",
+        "else:\n",
+        "    python_version_compatible = False\n",
+        "\n",
+        "try:\n",
+        "    import onnxruntime\n",
+        "    from azureml.automl.core.onnx_convert import OnnxInferenceHelper    \n",
+        "    onnxrt_present = True\n",
+        "except ImportError:\n",
+        "    onnxrt_present = False\n",
+        "\n",
+        "def get_onnx_res(run):\n",
+        "    res_path = '_debug_y_trans_converter.json'\n",
+        "    run.download_file(name=constants.MODEL_RESOURCE_PATH_ONNX, output_file_path=res_path)\n",
+        "    with open(res_path) as f:\n",
+        "        onnx_res = json.load(f)\n",
+        "    return onnx_res\n",
+        "\n",
+        "if onnxrt_present and python_version_compatible:    \n",
+        "    mdl_bytes = onnx_mdl.SerializeToString()\n",
+        "    onnx_res = get_onnx_res(best_run)\n",
+        "\n",
+        "    onnxrt_helper = OnnxInferenceHelper(mdl_bytes, onnx_res)\n",
+        "    pred_onnx, pred_prob_onnx = onnxrt_helper.predict(X_test)\n",
+        "\n",
+        "    print(pred_onnx)\n",
+        "    print(pred_prob_onnx)\n",
+        "else:\n",
+        "    if not python_version_compatible:\n",
+        "        print('Please use Python version 3.6 to run the inference helper.')    \n",
+        "    if not onnxrt_present:\n",
+        "        print('Please install the onnxruntime package to do the prediction with ONNX model.')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
     }
   ],
   "metadata": {

how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb

@@ -9,6 +9,13 @@
         "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/classification-with-whitelisting/auto-ml-classification-with-whitelisting.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

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

@@ -9,6 +9,13 @@
         "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/classification/auto-ml-classification.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb

@@ -9,6 +9,13 @@
         "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/dataprep-remote-execution/auto-ml-dataprep-remote-execution.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

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

@@ -9,6 +9,13 @@
         "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/dataprep/auto-ml-dataprep.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb

@@ -9,6 +9,13 @@
         "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/exploring-previous-runs/auto-ml-exploring-previous-runs.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},