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‎README.md

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@@ -55,62 +55,18 @@ Visit following repos to see projects contributed by Azure ML users:
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- [Fine tune natural language processing models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
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- [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)
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## Azure Machine Learning Resources & Links
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## Product Documentation
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- [Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/)
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- [Azure Machine Learning Studio](https://docs.microsoft.com/en-us/azure/machine-learning/studio/)
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## Product Team Blogs
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- [What’s new in Azure Machine Learning service](https://aka.ms/aml-blog-whats-new)
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- [Announcing automated ML capability in Azure Machine Learning](https://aka.ms/aml-blog-automl)
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- [Experimentation using Azure Machine Learning](https://aka.ms/aml-blog-experimentation)
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- [Azure AI – Making AI real for business](https://aka.ms/aml-blog-overview)
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## Community Blogs
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- [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/)
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## Ignite 2018 Public Preview Launch Sessions
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- [AI with Azure Machine Learning services: Simplifying the data science process](https://myignite.techcommunity.microsoft.com/sessions/66248)
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- [AI TechTalk: Azure Machine Learning SDK - a walkthrough](https://myignite.techcommunity.microsoft.com/sessions/66265)
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- [AI for an intelligent cloud and intelligent edge: Discover, deploy, and manage with Azure ML services](https://myignite.techcommunity.microsoft.com/sessions/65389)
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- [Generating high quality models efficiently using Automated ML and Hyperparameter Tuning](https://myignite.techcommunity.microsoft.com/sessions/66245)
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- [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)
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## Get-started Videos on YouTube
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- [Get started with Python SDK](https://youtu.be/VIsXeTuW3FU)
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- [Get started from Azure Portal](https://youtu.be/lCkYUHV86Mk)
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## Third Party Articles
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- [Azure’s new machine learning features embrace Python](https://www.infoworld.com/article/3306840/azure/azures-new-machine-learning-features-embrace-python.html) (InfoWorld)
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- [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)
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- [How Azure ML Streamlines Cloud-based Machine Learning](https://thenewstack.io/how-the-azure-ml-streamlines-cloud-based-machine-learning/) (The New Stack)
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- [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)
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- [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)
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- [Microsoft's Machine Learning Tools for Developers Get Smarter](https://techcrunch.com/2018/09/24/microsofts-machine-learning-tools-for-developers-get-smarter/) (TechCrunch)
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- [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)
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## Community Projects
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- [Use Papermill with Azure ML](https://github.com/jreynolds01/papermill_execution_azureml/)
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- [Fashion MNIST](https://github.com/amynic/azureml-sdk-fashion)
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- Keras on Databricks
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- [Samples from CSS](https://github.com/Azure/AMLSamples)
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## Azure Machine Learning Studio Resources
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- [A-Z Machine Learning using Azure Machine Learning (AzureML)](https://www.udemy.com/machine-learning-using-azureml/)
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- [Machine Learning In The Cloud With Azure Machine Learning](https://www.udemy.com/machine-learning-in-the-cloud-with-azure-machine-learning/)
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- [How to Become A Data Scientist Using Azure Machine Learning](https://www.udemy.com/azure-machine-learning-introduction/)
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- [Learn Azure Machine Learning from scratch](https://www.udemy.com/learn-azure-machine-learning-from-scratch/)
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- [Azure Machine Learning Studio PowerShell Module](https://aka.ms/amlps)
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## Forum Help
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- [Azure Machine Learning service](https://social.msdn.microsoft.com/Forums/en-US/home?forum=AzureMachineLearningService)
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- [Azure Machine Learning Studio](https://social.msdn.microsoft.com/forums/azure/en-US/home?forum=MachineLearning)
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## Data/Telemetry
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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)
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To opt out of tracking, please go to the raw markdown or .ipynb files and remove the following line of code:
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```sh
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"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/README.png)"
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```
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This URL will be slightly different depending on the file.
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## Data/Telemetry
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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)
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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)
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To opt out of tracking, please go to the raw markdown or .ipynb files and remove the following line of code:
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```
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This URL will be slightly different depending on the file.
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![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/README.png)

‎how-to-use-azureml/README.md

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Learn how to use Azure Machine Learning services for experimentation and model management.
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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.
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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.
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* [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.
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* [train-on-local](./training/train-on-local): Learn how to submit a run to local computer and use Azure ML managed run configuration.
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* [enable-app-insights-in-production-service](./deployment/enable-app-insights-in-production-service) Learn how to use App Insights with production web service.
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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/).
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![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/README.png)

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

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

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

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"Licensed under the MIT License."
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]
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.png)"
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},
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"\n",
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"## Contents\n",
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"1. [Introduction](#Introduction)\n",
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"1. [Setup](#setup)\n",
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"1. [Setup](#Setup)\n",
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"1. [Train](#Train)\n",
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"1. [Deploy](#Deploy)\n",
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"1. [Test](#Test)"
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup \n",
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"## Setup\n",
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"\n",
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"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."
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]
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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}

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

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"Licensed under the MIT License."
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"metadata": {},
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"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.png)"
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn import datasets\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"import azureml.core\n",
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"from azureml.core.experiment import Experiment\n",
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"from azureml.core.workspace import Workspace\n",
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"from azureml.train.automl import AutoMLConfig"
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"from azureml.train.automl import AutoMLConfig, constants"
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"source": [
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"## Data\n",
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"\n",
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"This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
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"This uses scikit-learn's [load_iris](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) method."
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"metadata": {},
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"outputs": [],
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"source": [
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"digits = datasets.load_digits()\n",
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"iris = datasets.load_iris()\n",
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"X_train, X_test, y_train, y_test = train_test_split(iris.data, \n",
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" iris.target, \n",
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" test_size=0.2, \n",
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" random_state=0)\n",
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"\n",
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"# Exclude the first 100 rows from training so that they can be used for test.\n",
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"X_train = digits.data[100:,:]\n",
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"y_train = digits.target[100:]"
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"# Convert the X_train and X_test to pandas DataFrame and set column names,\n",
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"# This is needed for initializing the input variable names of ONNX model, \n",
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"# and the prediction with the ONNX model using the inference helper.\n",
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"X_train = pd.DataFrame(X_train, columns=['c1', 'c2', 'c3', 'c4'])\n",
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"X_test = pd.DataFrame(X_test, columns=['c1', 'c2', 'c3', 'c4'])"
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" verbosity = logging.INFO,\n",
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" verbosity = logging.INFO, \n",
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" X = X_train, \n",
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" preprocess=True,\n",
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Predict with the ONNX model, using onnxruntime package"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"import json\n",
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"from azureml.automl.core.onnx_convert import OnnxConvertConstants\n",
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"\n",
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"if sys.version_info < OnnxConvertConstants.OnnxIncompatiblePythonVersion:\n",
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" python_version_compatible = True\n",
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"else:\n",
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" python_version_compatible = False\n",
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"\n",
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"try:\n",
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" import onnxruntime\n",
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" from azureml.automl.core.onnx_convert import OnnxInferenceHelper \n",
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" onnxrt_present = True\n",
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"except ImportError:\n",
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" onnxrt_present = False\n",
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"\n",
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"def get_onnx_res(run):\n",
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" res_path = '_debug_y_trans_converter.json'\n",
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" run.download_file(name=constants.MODEL_RESOURCE_PATH_ONNX, output_file_path=res_path)\n",
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" with open(res_path) as f:\n",
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" onnx_res = json.load(f)\n",
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" return onnx_res\n",
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"\n",
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"if onnxrt_present and python_version_compatible: \n",
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" mdl_bytes = onnx_mdl.SerializeToString()\n",
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" onnx_res = get_onnx_res(best_run)\n",
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"\n",
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" onnxrt_helper = OnnxInferenceHelper(mdl_bytes, onnx_res)\n",
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" pred_onnx, pred_prob_onnx = onnxrt_helper.predict(X_test)\n",
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"\n",
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" print(pred_onnx)\n",
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" print(pred_prob_onnx)\n",
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"else:\n",
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" if not python_version_compatible:\n",
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" print('Please use Python version 3.6 to run the inference helper.') \n",
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" if not onnxrt_present:\n",
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" print('Please install the onnxruntime package to do the prediction with ONNX model.')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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‎how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb

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"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.png)"
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‎how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb

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

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"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.png)"
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‎how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.ipynb

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"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.png)"
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‎how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb

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"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.png)"
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‎how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb

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"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.png)"
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‎how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb

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"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.png)"
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‎how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb

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"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.png)"
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