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update samples from Release-130 as a part of SDK release
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‎CODE_OF_CONDUCT.md

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# Microsoft Open Source Code of Conduct
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
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Resources:
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- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
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- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
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- Contact [[email protected]](mailto:[email protected]) with questions or concerns

‎LICENSE

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MIT License
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Copyright (c) Microsoft Corporation. All rights reserved.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE

‎Licenses/sdk-license/LICENSE

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This software is made available to you on the condition that you agree to
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[your agreement][1] governing your use of Azure.
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If you do not have an existing agreement governing your use of Azure, you agree that
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your agreement governing use of Azure is the [Microsoft Online Subscription Agreement][2]
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(which incorporates the [Online Services Terms][3]).
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By using the software you agree to these terms. This software may collect data
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that is transmitted to Microsoft. Please see the [Microsoft Privacy Statement][4]
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to learn more about how Microsoft processes personal data.
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[1]: https://azure.microsoft.com/en-us/support/legal/
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[2]: https://azure.microsoft.com/en-us/support/legal/subscription-agreement/
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[3]: http://www.microsoftvolumelicensing.com/DocumentSearch.aspx?Mode=3&DocumentTypeId=46
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[4]: http://go.microsoft.com/fwlink/?LinkId=248681

‎Licenses/sdk-preview-license/LICENSE

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This Preview is made available to you on the condition that you agree to the
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[Supplemental Terms of Use for Microsoft Azure Previews][1], which supplement
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[your agreement][2] governing your use of Azure.
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If you do not have an existing agreement governing your use of Azure, you agree that
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your agreement governing use of Azure is the [Microsoft Online Subscription Agreement][3]
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(which incorporates the [Online Services Terms][4]).
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By using the Preview you agree to these terms. This Preview may collect data
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that is transmitted to Microsoft. Please see the [Microsoft Privacy Statement][5]
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to learn more about how Microsoft processes personal data.
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[1]: https://azure.microsoft.com/en-us/support/legal/preview-supplemental-terms/
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[2]: https://azure.microsoft.com/en-us/support/legal/
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[3]: https://azure.microsoft.com/en-us/support/legal/subscription-agreement/
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[4]: http://www.microsoftvolumelicensing.com/DocumentSearch.aspx?Mode=3&DocumentTypeId=46
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[5]: http://go.microsoft.com/fwlink/?LinkId=248681

‎NBSETUP.md

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# Set up your notebook environment for Azure Machine Learning
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To run the notebooks in this repository use one of following options.
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## **Option 1: Use Azure Notebooks**
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Azure Notebooks is a hosted Jupyter-based notebook service in the Azure cloud. Azure Machine Learning Python SDK is already pre-installed in the Azure Notebooks `Python 3.6` kernel.
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1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
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[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks
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1. Follow the instructions in the [Configuration](configuration.ipynb) notebook to create and connect to a workspace
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1. Open one of the sample notebooks
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**Make sure the Azure Notebook kernel is set to `Python 3.6`** when you open a notebook by choosing Kernel > Change Kernel > Python 3.6 from the menus.
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## **Option 2: Use your own notebook server**
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### Quick installation
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We recommend you create a Python virtual environment ([Miniconda](https://conda.io/miniconda.html) preferred but [virtualenv](https://virtualenv.pypa.io/en/latest/) works too) and install the SDK in it.
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```sh
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# install just the base SDK
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pip install azureml-sdk
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# clone the sample repoistory
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git clone https://github.com/Azure/MachineLearningNotebooks.git
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# below steps are optional
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# install the base SDK, Jupyter notebook server and tensorboard
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pip install azureml-sdk[notebooks,tensorboard]
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# install model explainability component
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pip install azureml-sdk[interpret]
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# install automated ml components
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pip install azureml-sdk[automl]
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# install experimental features (not ready for production use)
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pip install azureml-sdk[contrib]
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```
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Note the _extras_ (the keywords inside the square brackets) can be combined. For example:
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```sh
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# install base SDK, Jupyter notebook and automated ml components
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pip install azureml-sdk[notebooks,automl]
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```
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### Full instructions
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[Install the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python)
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Please make sure you start with the [Configuration](configuration.ipynb) notebook to create and connect to a workspace.
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### Video walkthrough:
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[!VIDEO https://youtu.be/VIsXeTuW3FU]
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## **Option 3: Use Docker**
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You need to have Docker engine installed locally and running. Open a command line window and type the following command.
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__Note:__ We use version `1.0.10` below as an exmaple, but you can replace that with any available version number you like.
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```sh
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# clone the sample repoistory
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git clone https://github.com/Azure/MachineLearningNotebooks.git
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# change current directory to the folder
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# where Dockerfile of the specific SDK version is located.
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cd MachineLearningNotebooks/Dockerfiles/1.0.10
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# build a Docker image with the a name (azuremlsdk for example)
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# and a version number tag (1.0.10 for example).
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# this can take several minutes depending on your computer speed and network bandwidth.
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docker build . -t azuremlsdk:1.0.10
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# launch the built Docker container which also automatically starts
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# a Jupyter server instance listening on port 8887 of the host machine
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docker run -it -p 8887:8887 azuremlsdk:1.0.10
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```
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Now you can point your browser to http://localhost:8887. We recommend that you start from the `configuration.ipynb` notebook at the root directory.
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If you need additional Azure ML SDK components, you can either modify the Docker files before you build the Docker images to add additional steps, or install them through command line in the live container after you build the Docker image. For example:
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```sh
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# install the core SDK and automated ml components
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pip install azureml-sdk[automl]
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# install the core SDK and model explainability component
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pip install azureml-sdk[interpret]
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# install the core SDK and experimental components
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pip install azureml-sdk[contrib]
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```
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Drag and Drop
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The image will be downloaded by Fatkun

‎README.md

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# Azure Machine Learning Python SDK notebooks
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> a community-driven repository of examples using mlflow for tracking can be found at https://github.com/Azure/azureml-examples
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Welcome to the Azure Machine Learning Python SDK notebooks repository!
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## Getting started
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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.
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However, the notebooks can be run in any development environment with the correct `azureml` packages installed.
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Install the `azureml.core` Python package:
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```sh
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pip install azureml-core
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```
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Install additional packages as needed:
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```sh
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pip install azureml-mlflow
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pip install azureml-dataset-runtime
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pip install azureml-automl-runtime
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pip install azureml-pipeline
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pip install azureml-pipeline-steps
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...
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```
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We recommend starting with one of the [quickstarts](tutorials/compute-instance-quickstarts).
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## Contributing
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This repository is a push-only mirror. Pull requests are ignored.
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## Code of Conduct
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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.
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## Reference
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- [Documentation](https://docs.microsoft.com/azure/machine-learning)
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