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Merge pull request Azure#1951 from Azure/release_update_stablev2/Release-200
update samples from Release-200 as a part of 1.54.0 SDK stable release
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‎README.md

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# Azure Machine Learning Python SDK notebooks
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** **With the introduction of AzureML SDK v2, this samples repository for the v1 SDK is now deprecated and will not be monitored or updated. Users are encouraged to visit the [v2 SDK samples repository](https://github.com/Azure/azureml-examples) instead for up-to-date and enhanced examples of how to build, train, and deploy machine learning models with AzureML's newest features.** **
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### **With the introduction of AzureML SDK v2, this samples repository for the v1 SDK is now deprecated and will not be monitored or updated. Users are encouraged to visit the [v2 SDK samples repository](https://github.com/Azure/azureml-examples) instead for up-to-date and enhanced examples of how to build, train, and deploy machine learning models with AzureML's newest features.**
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Welcome to the Azure Machine Learning Python SDK notebooks repository!
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‎configuration.ipynb

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"source": [
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"import azureml.core\n",
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"\n",
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"print(\"This notebook was created using version 1.53.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.54.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

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

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# The python interpreter version.
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# Azure ML only supports 3.7.0 and later.
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- pip==22.3.1
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- python>=3.8,<3.9
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- holidays==0.10.3
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- pandas==1.3.5
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- python>=3.9,<3.10
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- holidays==0.29
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- scipy==1.10.1
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- Cython==0.29.14
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- tqdm==4.66.1
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# TODO: Remove this requirement when azureml-responsibleai will
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# upgrade responsibleai to version to >=0.30.0
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- scikit-learn<1.1
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- pip:
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# Required packages for AzureML execution, history, and data preparation.
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- azureml-widgets~=1.53.0
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- azureml-defaults~=1.53.0
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.53.2/validated_win32_requirements.txt [--no-deps]
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- azureml-widgets~=1.54.0
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- azureml-defaults~=1.54.0
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.54.0/validated_win32_requirements.txt [--no-deps]
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- matplotlib==3.7.1
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- xgboost==1.3.3
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- prophet==1.1.4
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- pandas==1.3.5
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- cmdstanpy==1.1.0
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- setuptools-git==1.2

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

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# The python interpreter version.
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# Azure ML only supports 3.7 and later.
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- pip==22.3.1
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- python>=3.8,<3.9
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- python>=3.9,<3.10
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- matplotlib==3.7.1
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- numpy==1.22.3
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- cython==0.29.14
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- numpy>=1.21.6,<=1.23.5
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- urllib3==1.26.7
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- scipy==1.10.1
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- scikit-learn==1.1.3
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# TODO: Upgrade this requirement to 1.1.3 when azureml-responsibleai will
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# upgrade responsibleai to version to >=0.30.0
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- scikit-learn<1.1
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- py-xgboost<=1.3.3
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- holidays==0.10.3
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- conda-forge::prophet==1.1.4
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- holidays==0.29
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- pytorch::pytorch=1.11.0
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- cudatoolkit=10.1.243
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- notebook
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- scikit-learn<1.1
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- pip:
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# Required packages for AzureML execution, history, and data preparation.
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- azureml-widgets~=1.53.0
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- azureml-defaults~=1.53.0
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- azureml-widgets~=1.54.0
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- azureml-defaults~=1.54.0
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- pytorch-transformers==1.0.0
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- spacy==2.2.4
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- pystan==2.19.1.1
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- prophet==1.1.4
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- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.53.2/validated_linux_requirements.txt [--no-deps]
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.54.0/validated_linux_requirements.txt [--no-deps]

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

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# The python interpreter version.
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# Currently Azure ML only supports 3.7 and later.
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- pip==22.3.1
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- python>=3.8,<3.9
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- numpy==1.22.3
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- cython==0.29.14
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- python>=3.9,<3.10
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- numpy>=1.21.6,<=1.23.5
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- scipy==1.10.1
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- scikit-learn==1.1.3
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- holidays==0.10.3
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# TODO: Upgrade this requirement to 1.1.3 when azureml-responsibleai will
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# upgrade responsibleai to version to >=0.30.0
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- scikit-learn<1.1
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- holidays==0.29
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- pip:
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# Required packages for AzureML execution, history, and data preparation.
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- azureml-widgets~=1.53.0
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- azureml-defaults~=1.53.0
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- azureml-widgets~=1.54.0
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- azureml-defaults~=1.54.0
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- pytorch-transformers==1.0.0
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- spacy==2.2.4
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- pystan==2.19.1.1
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- prophet==1.1.4
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- xgboost==1.3.3
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- matplotlib==3.7.1
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- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.53.2/validated_darwin_requirements.txt [--no-deps]
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.54.0/validated_darwin_requirements.txt [--no-deps]

‎how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-codegen/codegen-for-autofeaturization.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.53.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.54.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

‎how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-custom-model-training/custom-model-training-from-autofeaturization-run.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.53.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.54.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

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

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.53.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.54.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

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

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.53.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.54.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

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

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{
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"cells": [
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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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"Copyright (c) Microsoft Corporation. All rights reserved.\n",
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"\n",
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"Licensed under the MIT License."
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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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"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.png)"
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]
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},
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{
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"import logging\n",
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"import os\n",
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"\n",
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"from matplotlib import pyplot as plt\n",
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"import pandas as pd\n",
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"\n",
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"import azureml.core\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"from azureml.pipeline.core import PipelineData\n",
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"\n",
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"# The model name with which to register the trained model in the workspace.\n",
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"model_name_str = \"ojmodel\"\n",
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"model_name = PipelineParameter(\"model_name\", default_value=model_name_str)\n",

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

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{
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"cells": [
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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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"Copyright (c) Microsoft Corporation. All rights reserved.\n",
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"\n",
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"Licensed under the MIT License."
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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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"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.png)"
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]
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},
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"cell_type": "markdown",
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‎how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb

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{
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"cells": [
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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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"Copyright (c) Microsoft Corporation. All rights reserved.\n",
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"\n",
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"Licensed under the MIT License."
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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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"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.png)"
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]
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},
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‎how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb

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"from azureml.core.conda_dependencies import CondaDependencies \n",
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"\n",
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"myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
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"myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime==1.15.1\", \"azureml-core\", \"azureml-defaults\"])\n",
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"\n",
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"with open(\"myenv.yml\",\"w\") as f:\n",
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" f.write(myenv.serialize_to_string())"

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

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"from azureml.core.conda_dependencies import CondaDependencies \n",
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"\n",
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"\n",
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"myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
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"myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime==1.15.1\", \"azureml-core\", \"azureml-defaults\"])\n",
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"\n",
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"with open(\"myenv.yml\",\"w\") as f:\n",
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" f.write(myenv.serialize_to_string())"

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

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"from azureml.core.conda_dependencies import CondaDependencies \n",
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"\n",
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"myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
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"myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime==1.15.1\", \"azureml-core\", \"azureml-defaults\"])\n",
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"\n",
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"with open(\"myenv.yml\",\"w\") as f:\n",
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" f.write(myenv.serialize_to_string())"

‎how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb

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"from azureml.core.conda_dependencies import CondaDependencies \n",
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"\n",
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"\n",
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"myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
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"myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime==1.15.1\", \"azureml-core\", \"azureml-defaults\"])\n",
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"\n",
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"with open(\"myenv.yml\",\"w\") as f:\n",
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" f.write(myenv.serialize_to_string())"

‎how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb

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"from azureml.core.conda_dependencies import CondaDependencies \n",
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"\n",
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"myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime\",\"azureml-core\", \"azureml-defaults\"])\n",
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"myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime==1.15.1\",\"azureml-core\", \"azureml-defaults\"])\n",
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"\n",
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"with open(\"myenv.yml\",\"w\") as f:\n",
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" f.write(myenv.serialize_to_string())"

‎how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/distributed-pytorch-with-distributeddataparallel.ipynb

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"### Create an environment\n",
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"In this tutorial, we will use one of Azure ML's curated PyTorch environments for training. [Curated environments](https://docs.microsoft.com/azure/machine-learning/how-to-use-environments#use-a-curated-environment) are available in your workspace by default. Specifically, we will use the PyTorch 1.6 GPU curated environment."
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"In this tutorial, we will use one of Azure ML's curated PyTorch environments for training. [Curated environments](https://docs.microsoft.com/azure/machine-learning/how-to-use-environments#use-a-curated-environment) are available in your workspace by default. Specifically, we will use the PyTorch 2.0 GPU curated environment."
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{
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"from azureml.core import Environment\n",
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"pytorch_env = Environment.get(ws, name='azureml-acpt-pytorch-1.11-cuda11.3')"
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"pytorch_env = Environment.get(ws, name='azureml-acpt-pytorch-1.13-cuda11.7')"
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]
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},
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{

‎how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb

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"# Check core SDK version number\n",
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"print(\"This notebook was created using SDK version 1.53.0, you are currently running version\", azureml.core.VERSION)"
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"print(\"This notebook was created using SDK version 1.54.0, you are currently running version\", azureml.core.VERSION)"
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]
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},
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{

‎how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/prepare.py

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convert(os.path.join(mounted_input_path, 'mnist-fashion/train-images-idx3-ubyte'),
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os.path.join(mounted_input_path, 'mnist-fashion/train-labels-idx1-ubyte'),
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convert(os.path.join(mounted_input_path, 'train-images-idx3-ubyte'),
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os.path.join(mounted_input_path, 'train-labels-idx1-ubyte'),
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os.path.join(mounted_output_path, 'mnist_train.csv'), 60000)
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convert(os.path.join(mounted_input_path, 'mnist-fashion/t10k-images-idx3-ubyte'),
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os.path.join(mounted_input_path, 'mnist-fashion/t10k-labels-idx1-ubyte'),
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convert(os.path.join(mounted_input_path, 't10k-images-idx3-ubyte'),
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os.path.join(mounted_input_path, 't10k-labels-idx1-ubyte'),
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os.path.join(mounted_output_path, 'mnist_test.csv'), 10000)

‎index.md

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|:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
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| [Deploy MNIST digit recognition with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb) | Image Classification | MNIST | Local | Azure Container Instance | ONNX | ONNX Model Zoo |
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| [Deploy Facial Expression Recognition (FER+) with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb) | Facial Expression Recognition | Emotion FER | Local | Azure Container Instance | ONNX | ONNX Model Zoo |
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| :star:[Register model and deploy as webservice](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb) | Deploy a model with Azure Machine Learning | Diabetes | None | Azure Container Instance | Scikit-learn | None |
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| [Train MNIST in PyTorch, convert, and deploy with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb) | Image Classification | MNIST | AML Compute | Azure Container Instance | ONNX | ONNX Converter |
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| [Deploy ResNet50 with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb) | Image Classification | ImageNet | Local | Azure Container Instance | ONNX | ONNX Model Zoo |
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| :star:[Convert and deploy TinyYolo with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb) | Object Detection | PASCAL VOC | local | Azure Container Instance | ONNX | ONNX Converter |

‎setup-environment/configuration.ipynb

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"source": [
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"import azureml.core\n",
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"\n",
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"print(\"This notebook was created using version 1.53.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.54.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

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