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Provision the Data Science Virtual Machine for Linux (Ubuntu)

The Data Science Virtual Machine (DSVM) for Linux is an Ubuntu-based virtual machine image that makes it easy to get started with machine learning, including deep learning, on Azure.

The Data Science Virtual Machine for Linux also contains popular tools for data science and development activities, including: Doing data science involves iterating on a sequence of tasks: Data scientists use various tools to complete these tasks.

It can be time consuming to find the appropriate versions of the software, and then to download, compile, and install these versions.

You pay only the Azure hardware usage fees that are assessed based on the size of the virtual machine that you provision.

Here are the steps to create an instance of the Data Science Virtual Machine for Linux: You can access the Ubuntu DSVM by using three methods: You can also attach a Data Science Virtual Machine to Azure Notebooks to run Jupyter notebooks on the VM and bypass the limitations of the free service tier.

To connect to the Linux VM graphical desktop, complete the following procedure on your client: After you sign in to the VM by using either the SSH client or the XFCE graphical desktop through the X2Go client, you're ready to start using the tools that are installed and configured on the VM.

You can set JupyterLab as the default notebook server by adding this line to /etc/jupyterhub/ The Microsoft Cognitive Toolkit is an open-source deep learning toolkit.

To run a basic sample at the command line, run the following commands in the shell: For more information, see the CNTK section of GitHub and the CNTK wiki.

These tasks include managing data, designing and training neural networks on GPU systems, and monitoring performance in real time with advanced visualization.

It's available in /dsvm/tools/torch, and the th interactive session and LuaRocks package manager are available at the command line.

This distribution contains the base Python along with about 300 of the most popular math, engineering, and data analytics packages.

To activate the root (2.7) environment, use this command: To activate the py35 environment again, use this command: To invoke a Python interactive session, enter python in the shell.

For pip, activate the correct environment first if you don't want the default: Or specify the full path to pip: For Conda, you should always specify the environment name (py35 or root): If you're on a graphical interface or have X11 forwarding set up, you can enter pycharm to open the PyCharm Python IDE.

You can see the link to the samples on the notebook home page after you authenticate to the Jupyter notebook by using your local Linux username and password.

standalone instance of Apache Spark is preinstalled on the Linux DSVM to help you develop Spark applications locally before you test and deploy them on large clusters.

Before you run in a Spark context in Microsoft Machine Learning Server, you need to do a one-time setup step to enable a local single-node Hadoop HDFS and Yarn instance.

To enable it, you need to run the following commands as root the first time: You can stop the Hadoop-related services when you don't need them by running systemctl stop hadoop-namenode hadoop-datanode hadoop-yarn.

The /dsvm/samples/MRS directory provides a sample that demonstrates how to develop and test Microsoft Machine Learning Server in a remote Spark context (the standalone Spark instance on the DSVM).

Or you can run the client by using the following command in the shell: Before the first use, set up your drivers and database aliases.

The ODBC driver package for SQL Server also comes with two command-line tools: Libraries are available in R and Python for database access: The following Azure tools are installed on the VM: You can access the Azure portal from the pre-installed Firefox browser.

Azure Machine Learning is a fully managed cloud service that enables you to build, deploy, and share predictive analytics solutions.

Operationalizing machine learning models enables clients written in any language to invoke predictions from those models.

Vowpal Wabbit is a machine learning system that uses techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

The objective of this library is to push the computation limits of machines to the extremes needed to provide large-scale tree boosting that is scalable, portable, and accurate.

Here's a simple example that you can run in an R prompt: To run the xgboost command line, here are the commands to run in the shell: A

It presents statistical and visual summaries of data, transforms data that can be readily modeled, builds both unsupervised and supervised models from the data, presents the performance of models graphically, and scores new data sets.

It also generates R code, replicating the operations in the UI that can be run directly in R or used as a starting point for further analysis.

Especially for beginners in R, this is a way to quickly do analysis and machine learning in a simple graphical interface, while automatically generating code in R to modify or learn.

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