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Copy file name to clipboardExpand all lines: articles/machine-learning/data-science-virtual-machine/dsvm-samples-and-walkthroughs.md
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author: timoklimmer
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ms.author: tklimmer
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ms.topic: conceptual
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ms.date: 05/12/2021
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ms.reviewer: franksolomon
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ms.date: 04/16/2024
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---
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# Samples on Azure Data Science Virtual Machines
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Azure Data Science Virtual Machines (DSVMs) include a comprehensive set of sample code. These samples include Jupyter notebooks and scripts in languages like Python and R.
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An Azure Data Science Virtual Machines (DSVM) includes a comprehensive set of sample code. These samples include Jupyter notebooks and scripts in languages like Python and R.
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> [!NOTE]
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> For more information about how to run Jupyter notebooks on your data science virtual machines, see the [Access Jupyter](#access-jupyter) section.
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> For more information about how to run Jupyter notebooks on your data science virtual machines, visit the [Access Jupyter](#access-jupyter) section.
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## Prerequisites
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In order to run these samples, you must have provisioned an[Ubuntu Data Science Virtual Machine](./dsvm-ubuntu-intro.md).
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To run these samples, you must have a provisioned[Ubuntu Data Science Virtual Machine](./dsvm-ubuntu-intro.md).
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## Available samples
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| Samples category | Description | Locations |
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| ------------- | ------------- | ------------- |
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| Python language | Samples explain scenarios like how to connect with Azure-based cloud data stores and how to work with Azure Machine Learning. <br/>[Python language](#python-language)| <br/>`~notebooks` <br/><br/>|
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| Julia language | Provides a detailed description of plotting and deep learning in Julia. Also explains how to call C and Python from Julia. <br/> [Julia language](#julia-language)|<br/> Windows:<br/> `~notebooks/Julia_notebooks`<br/><br/> Linux:<br/> `~notebooks/julia`<br/><br/> |
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| Azure Machine Learning |Illustrates how to build machine-learning and deep-learning models with Machine Learning. Deploy models anywhere. Use automated machine learning and intelligent hyperparameter tuning. Also use model management and distributed training. <br/> [Machine Learning](#azure-machine-learning)| <br/>`~notebooks/AzureML`<br/> <br/>|
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| Python language | Samples that explain **how to connect with Azure-based cloud data stores** and **how to work with Azure Machine Learning scenarios**. <br/>[Python language](#python-language)| <br/>`~notebooks` <br/><br/>|
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| Julia language | Provides a detailed description of plotting and deep learning in Julia. Explains how to call C and Python from Julia. <br/> [Julia language](#julia-language)|<br/> Windows:<br/> `~notebooks/Julia_notebooks`<br/><br/> Linux:<br/> `~notebooks/julia`<br/><br/> |
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| Azure Machine Learning |Shows how to build machine-learning and deep-learning models with Machine Learning. Deploy models anywhere. Use automated machine learning and intelligent hyperparameter tuning. Use model management and distributed training. <br/> [Machine Learning](#azure-machine-learning)| <br/>`~notebooks/AzureML`<br/> <br/>|
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| PyTorch notebooks | Deep-learning samples that use PyTorch-based neural networks. Notebooks range from beginner to advanced scenarios. <br/> [PyTorch notebooks](#pytorch)| <br/>`~notebooks/Deep_learning_frameworks/pytorch`<br/> <br/>|
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| TensorFlow |A variety of neural network samples and techniques implemented by using the TensorFlow framework. <br/> [TensorFlow](#tensorflow)| <br/>`~notebooks/Deep_learning_frameworks/tensorflow`<br/><br/> |
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| TensorFlow |Various neural network samples and techniques implemented with the TensorFlow framework. <br/> [TensorFlow](#tensorflow)| <br/>`~notebooks/Deep_learning_frameworks/tensorflow`<br/><br/> |
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| H2O | Python-based samples that use H2O for real-world problem scenarios. <br/> [H2O](#h2o)| <br/>`~notebooks/h2o`<br/><br/> |
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| SparkML language | Samples that use features of the Apache Spark MLLib toolkit through pySpark and MMLSpark: Microsoft Machine Learning for Apache Spark on Apache Spark 2.x. <br/> [SparkML language](#sparkml)| <br/>`~notebooks/SparkML/pySpark`<br/>`~notebooks/MMLSpark`<br/><br/> |
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| XGBoost | Standard machine-learning samples in XGBoost for scenarios like classification and regression. <br/> [XGBoost](#xgboost)| <br/>Windows:<br/>`\dsvm\samples\xgboost\demo`<br/><br/> |
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<br/>
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| SparkML language | Samples that use Apache Spark MLLib toolkit features, through pySpark and MMLSpark: Microsoft Machine Learning for Apache Spark on Apache Spark 2.x. <br/> [SparkML language](#sparkml)| <br/>`~notebooks/SparkML/pySpark`<br/>`~notebooks/MMLSpark`<br/><br/> |
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| XGBoost | Standard machine-learning samples in XGBoost - for example, classification and regression. <br/> [XGBoost](#xgboost)| <br/>Windows:<br/>`\dsvm\samples\xgboost\demo`<br/><br/> |
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## Access Jupyter
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## Access Jupyter
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To access Jupyter, select the **Jupyter** icon on the desktop or application menu. You also can access Jupyter on a Linux edition of a DSVM. To access remotely from a web browser, go to `https://<Full Domain Name or IP Address of the DSVM>:8000` on Ubuntu.
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To add exceptions and make Jupyter access available over a browser, use the following guidance:
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To access Jupyter, select the **Jupyter** icon on the desktop or application menu. You also can access Jupyter on a Linux edition of a DSVM. For remote access from a web browser, visit `https://<Full Domain Name or IP Address of the DSVM>:8000` on Ubuntu.
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To add exceptions, and make Jupyter access available through a browser, use this guidance:
:::image type="content" source="./media/r-language-samples.png" lightbox="./media/r-language-samples.png" alt-text="Screenshot showing R language sample notebooks.":::
:::image type="content" source="./media/julia-samples.png" lightbox="./media/julia-samples.png" alt-text="Screenshot showing Julia language sample notebooks.":::
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