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‎.openpublishing.redirection.json

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"redirect_url": "/azure/machine-learning/data-science-virtual-machine/tools-included",
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‎articles/hdinsight/hdinsight-machine-learning-overview.md

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* [Use Microsoft Cognitive Toolkit deep learning model with Azure HDInsight Spark cluster](spark/apache-spark-microsoft-cognitive-toolkit.md)
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* [Use Caffe on Azure HDInsight Spark for distributed deep learning](spark/apache-spark-deep-learning-caffe.md)
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* [Deep Learning and AI frameworks on the Data Science Virtual Machine (DSVM)](../machine-learning/data-science-virtual-machine/dsvm-deep-learning-ai-frameworks.md)
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* [Deep Learning and AI frameworks on the Data Science Virtual Machine (DSVM)](../machine-learning/data-science-virtual-machine/dsvm-tools-deep-learning-frameworks.md)

‎articles/machine-learning/data-science-virtual-machine/dsvm-data-platforms.md renamed to ‎articles/machine-learning/data-science-virtual-machine/dsvm-tools-data-platforms.md

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author: gvashishtha
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author: lobrien
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ms.author: laobri
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ms.topic: conceptual
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ms.date: 10/3/2019
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ms.date: 12/12/2019
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---
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The following data platform tools are supported on the DSVM.
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## SQL Server 2017 Developer Edition
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## SQL Server Developer Edition
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| | |
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| ------------- | ------------- |
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| What is it? | A local relational database instance |
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| Supported DSVM editions | Windows |
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| Supported DSVM editions | Windows: SQL Server 2017, Windows 2019 (Preview) : SQL Server 2019 |
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| Typical uses | Rapid development locally with smaller dataset <br/> Run In-database R |
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| Links to samples | A small sample of a New York City dataset is loaded into the SQL database:<br/> `nyctaxi` <br/> Jupyter sample showing Microsoft Machine Learning Server and in-database analytics can be found at:<br/> `~notebooks/SQL_R_Services_End_to_End_Tutorial.ipynb` |
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| Related tools on the DSVM | SQL Server Management Studio <br/> ODBC/JDBC drivers<br/> pyodbc, RODBC<br />Apache Drill |
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> [!NOTE]
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> SQL Server 2016 Developer Edition can be used only for development and test purposes. You need a license or one of the SQL Server VMs to run it in production.
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> SQL Server Developer Edition can be used only for development and test purposes. You need a license or one of the SQL Server VMs to run it in production.
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### Setup
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The database server is already preconfigured and the Windows services related to SQL Server (like `SQL Server (MSSQLSERVER)`) are set to run automatically. The only manual step involves enabling In-database analytics by using Microsoft Machine Learning Server. You can do this by running the following command as a one-time action in SQL Server Management Studio (SSMS). Run this command after you log in as the machine administrator, open a new query in SSMS, and make sure the selected database is `master`:
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The database server is already preconfigured and the Windows services related to SQL Server (like `SQL Server (MSSQLSERVER)`) are set to run automatically. The only manual step involves enabling In-database analytics by using Microsoft Machine Learning Server. You can enable analytics by running the following command as a one-time action in SQL Server Management Studio (SSMS). Run this command after you log in as the machine administrator, open a new query in SSMS, and make sure the selected database is `master`:
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CREATE LOGIN [%COMPUTERNAME%\SQLRUserGroup] FROM WINDOWS
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| | |
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| ------------- | ------------- |
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| What is it? | A standalone (single node in-process) instance of the popular Apache Spark platform; a system for fast, large-scale data processing and machine-learning |
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| Supported DSVM editions | Linux <br /> Windows (Experimental) |
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| Supported DSVM editions | Linux |
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| Typical uses | * Rapid development of Spark/PySpark applications locally with a smaller dataset and later deployment on large Spark clusters such as Azure HDInsight<br/> * Test Microsoft Machine Learning Server Spark context <br />* Use SparkML or Microsoft's open-source [MMLSpark](https://github.com/Azure/mmlspark) library to build ML applications |
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| Links to samples | Jupyter sample: <br />&nbsp;&nbsp;* ~/notebooks/SparkML/pySpark <br /> &nbsp;&nbsp;* ~/notebooks/MMLSpark <br /> Microsoft Machine Learning Server (Spark context): /dsvm/samples/MRS/MRSSparkContextSample.R |
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| Related tools on the DSVM | PySpark, Scala<br/>Jupyter (Spark/PySpark Kernels)<br/>Microsoft Machine Learning Server, SparkR, Sparklyr <br />Apache Drill |
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You can use Spark from R by using libraries like SparkR, Sparklyr, and Microsoft Machine Learning Server, which are available on the DSVM. See pointers to samples in the preceding table.
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> [!NOTE]
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> Running Microsoft Machine Learning Server in Spark context of DSVM is supported on the Ubuntu Linux DSVM edition only.
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### Setup
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Before running in a Spark context in Microsoft Machine Learning Server on Ubuntu Linux DSVM edition, you must complete a one-time setup step to enable a local single node Hadoop HDFS and Yarn instance. By default, Hadoop services are installed but disabled on the DSVM. To enable them, run the following commands as root the first time:
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### How is it configured and installed on the DSVM?
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|Platform|Install Location ($SPARK_HOME)|
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|:--------|:--------|
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|Windows | c:\dsvm\tools\spark-X.X.X-bin-hadoopX.X|
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|Linux | /dsvm/tools/spark-X.X.X-bin-hadoopX.X|
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‎articles/machine-learning/data-science-virtual-machine/dsvm-ml-data-science-tools.md renamed to ‎articles/machine-learning/data-science-virtual-machine/dsvm-tools-data-science.md

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# Machine learning and data science tools on Azure Data Science Virtual Machines
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| Links to samples | Samples are included on the VM, in `/dsvm/tools/xgboost/demo` on Linux, and `C:\dsvm\tools\xgboost\demo` on Windows. |
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| Related tools | LightGBM, MXNet |
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## Apache Drill
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| ------------- | ------------- |
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| What is it? | Open-source SQL query engine on big data |
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| Supported DSVM versions | Windows 2019 (Preview), Linux |
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| How is it configured and installed on the DSVM? | Installed in `/dsvm/tools/drill*` in embedded mode only |
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| Typical uses | For in-place data exploration without requiring extract, transform, load (ETL). Query different data sources and formats, including CSV, JSON, relational tables, and Hadoop. |
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| How to use and run it | Desktop shortcut <br/> [Get started with Drill in 10 minutes](https://drill.apache.org/docs/drill-in-10-minutes/) |
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| Related tools on the DSVM | Rattle, Weka, SQL Server Management Studio |
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‎articles/machine-learning/data-science-virtual-machine/dsvm-deep-learning-ai-frameworks.md renamed to ‎articles/machine-learning/data-science-virtual-machine/dsvm-tools-deep-learning-frameworks.md

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# Deep learning and AI frameworks for the Azure Data Science VM
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| Version(s) supported | 2.5.1 |
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| Supported DSVM editions | Windows and Linux |
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| How is it configured / installed on the DSVM? | CNTK is installed in Python 3.6 on [Windows 2016](dsvm-languages.md#python-windows-server-2016-edition) and in Python 3.5 on [Linux](./dsvm-languages.md#python-linux-edition)) |
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| How is it configured / installed on the DSVM? | CNTK is installed in Python 3.6 on [Windows 2016](dsvm-tools-languages.md#python-windows-server-2016-edition) and in Python 3.5 on [Linux](./dsvm-tools-languages.md#python-linux-edition)) |
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| How to run it | Terminal: Activate the correct environment and run Python. <br/>Jupyter: Connect to [Jupyter](provision-vm.md) or [JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-ubuntu-data-science-virtual-machine), and then open the CNTK directory for samples. |
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## [MXNet](https://mxnet.apache.org/)
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| Version(s) supported | 1.3.0 |
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| How is it configured / installed on the DSVM? | MXNet is installed in `C:\dsvm\tools\mxnet` on Windows and `/dsvm/tools/mxnet` on Ubuntu. Python bindings are installed in Python 3.6 on [Windows 2016](dsvm-languages.md#python-windows-server-2016-edition) and in Python 3.5 on [Linux](./dsvm-languages.md#python-linux-edition)) R bindings are also included in the Ubuntu DSVM. |
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| How is it configured / installed on the DSVM? | MXNet is installed in `C:\dsvm\tools\mxnet` on Windows and `/dsvm/tools/mxnet` on Ubuntu. Python bindings are installed in Python 3.6 on [Windows 2016](dsvm-tools-languages.md#python-windows-server-2016-edition) and in Python 3.5 on [Linux](./dsvm-tools-languages.md#python-linux-edition)) R bindings are also included in the Ubuntu DSVM. |
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| How to run it | Terminal: Activate the correct conda environment, then run `import mxnet`. <br/>Jupyter: Connect to [Jupyter](provision-vm.md#access-the-dsvm) or [JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-ubuntu-data-science-virtual-machine), and then open the `mxnet` directory for samples. |
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## [MXNet Model Server](https://github.com/awslabs/mxnet-model-server#quick-start)
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| How is it configured / installed on the DSVM? | MXNet Model Server is installed in Python 3.6 on [Windows 2016](dsvm-languages.md#python-windows-server-2016-edition) and in Python 3.5 on [Linux](./dsvm-languages.md#python-linux-edition)) |
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| How is it configured / installed on the DSVM? | MXNet Model Server is installed in Python 3.6 on [Windows 2016](dsvm-tools-languages.md#python-windows-server-2016-edition) and in Python 3.5 on [Linux](./dsvm-tools-languages.md#python-linux-edition)) |
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| How to run it | Terminal: Run `sudo systemctl stop jupyterhub` to stop the JupyterHub service first, because both listen on the same port. Then activate the correct conda environment and run `mxnet-model-server --start --models squeezenet=https://s3.amazonaws.com/model-server/model_archive_1.0/squeezenet_v1.1.mar` |
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## [NVidia System Management Interface (nvidia-smi)](https://developer.nvidia.com/nvidia-system-management-interface)
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| How is it configured / installed on the DSVM? | Installed in [Python 3.5](dsvm-languages.md#python-linux-edition). Sample Jupyter notebooks are included, and samples are in /dsvm/samples/pytorch. |
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| How is it configured / installed on the DSVM? | Installed in [Python 3.5](dsvm-tools-languages.md#python-linux-edition). Sample Jupyter notebooks are included, and samples are in /dsvm/samples/pytorch. |
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| How to run it | Terminal: Activate the correct environment, and then run Python.<br/>* [JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-ubuntu-data-science-virtual-machine): Connect, and then open the PyTorch directory for samples. |
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| How is it configured / installed on the DSVM? | Installed in Python 3.5 on [Linux](dsvm-languages.md#python-linux-edition) and Python 3.6 on [Windows 2016](dsvm-languages.md#python-windows-server-2016-edition) |
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| How is it configured / installed on the DSVM? | Installed in Python 3.5 on [Linux](dsvm-tools-languages.md#python-linux-edition) and Python 3.6 on [Windows 2016](dsvm-tools-languages.md#python-windows-server-2016-edition) |
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| How to run it | Terminal: Activate the correct environment, and then run Python. <br/> * Jupyter: Connect to [Jupyter](provision-vm.md) or [JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-ubuntu-data-science-virtual-machine), and then open the TensorFlow directory for samples. |
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‎articles/machine-learning/data-science-virtual-machine/dsvm-tools-development.md

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# Development tools on the Azure Data Science Virtual Machine
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## Visual Studio Community Edition
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| How is it configured and installed on the DSVM? | Data Science Workload (Python and R tools), Azure workload (Hadoop, Data Lake), Node.js, SQL Server tools, [Azure Machine Learning for Visual Studio Code](https://github.com/Microsoft/vs-tools-for-ai) |
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| How to use and run it | Desktop shortcut (`C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\Common7\IDE\devenv.exe`). Graphically, open Visual Studio by using the desktop icon or the **Start** menu. Search for programs (Windows logo key+S), followed by **Visual Studio**. From there, you can create projects in languages like C#, Python, R, and Node.js. |
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| How to use and run it | Desktop shortcut (`C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\devenv.exe`). Graphically, open Visual Studio by using the desktop icon or the **Start** menu. Search for programs (Windows logo key+S), followed by **Visual Studio**. From there, you can create projects in languages like C#, Python, R, and Node.js. |
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| How to use and run it | Desktop shortcut (`C:\Program Files (x86)\Microsoft VS Code\Code.exe`) in Windows, desktop shortcut or terminal (`code`) in Linux |
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| Related tools on the DSVM | Visual Studio, RStudio, Juno |
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## RStudio Desktop
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| What is it? | Client IDE for R language |
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| How to use and run it | Desktop shortcut (`C:\Program Files\RStudio\bin\rstudio.exe`) on Windows, desktop shortcut (`/usr/bin/rstudio`) on Linux |
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| Related tools on the DSVM | Visual Studio, Visual Studio Code, Juno |
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## RStudio Server
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| How to use and run it | Enable the service with _systemctl enable rstudio-server_, and then start the service with _systemctl start rstudio-server_. Then sign in to RStudio Server at http:\//your-vm-ip:8787. |
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| Related tools on the DSVM | Visual Studio, Visual Studio Code, RStudio Desktop |
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| Related tools on the DSVM | Visual Studio, Visual Studio Code, RStudio |
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| How to use and run it | Desktop shortcut (`C:\Program Files\tk`) on Windows. Desktop shortcut (`/usr/bin/pycharm`) on Linux |
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| Related tools on the DSVM | Visual Studio, Visual Studio Code, RStudio |

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