Introduction to Azure Data Science Virtual Machine for Linux and Windows. The Data Science Virtual Machine DSVM is a customized VM image on Microsofts Azure cloud built specifically for doing data science. It has many popular data science and other tools pre installed and pre configured to jump start building intelligent applications for advanced analytics. It is available on Windows Server and on Linux. We offer Windows edition of DSVM on Server 2. PNG' alt='Torch Setup For Windows 7' title='Torch Setup For Windows 7' />Should I remove Torch by Torch Media Torch Browser is a freeware Chromium based web browser and Internet suite developed by Torch Media. Server 2. 01. 2. We offer Linux edition of the DSVM on Ubuntu 1. LTS and on Open. Logic 7. Issuu is a digital publishing platform that makes it simple to publish magazines, catalogs, newspapers, books, and more online. Easily share your publications and get. Cent. OS based Linux distributions. This topic discusses what you can do with the Data Science VM, outlines some of the key scenarios for using the VM, itemizes the key features available on the Windows and Linux versions, and provides instructions on how to get started using them. A global leader in mobile communications, BlackBerry offers unrivaled security in smartphones and enterprise software solutions. Learn more and shop now. Special deals on the latest cell phones and smartphones. Get FREE SHIPPING on phones and devices with all new activationsWhat can I do with the Data Science Virtual Machine The goal of the Data Science Virtual Machine is to provide data professionals at all skill levels and roles with a friction free data science environment. This VM saves you considerable time that you would spend if you had rolled out a comparable environment on your own. Instead, start your data science project immediately in a newly created VM instance. The Data Science VM is designed and configured for working with a broad range of usage scenarios. You can scale your environment up or down as your project needs change. You are able to use your preferred language to program data science tasks. You can install other tools and customize the system for your exact needs. Key Scenarios. This section suggests some key scenarios for which the Data Science VM can be deployed. Preconfigured analytics desktop in the cloud. The Data Science VM provides a baseline configuration for data science teams looking to replace their local desktops with a managed cloud desktop. This baseline ensures that all the data scientists on a team have a consistent setup with which to verify experiments and promote collaboration. It also lowers costs by reducing the sysadmin burden and saving on the time needed to evaluate, install, and maintain the various software packages needed to do advanced analytics. Data science training and education. Enterprise trainers and educators that teach data science classes usually provide a virtual machine image to ensure that their students have a consistent setup and that the samples work predictably. The Data Science VM creates an on demand environment with a consistent setup that eases the support and incompatibility challenges. Cases where these environments need to be built frequently, especially for shorter training classes, benefit substantially. On demand elastic capacity for large scale projects. Data science hackathonscompetitions or large scale data modeling and exploration require scaled out hardware capacity, typically for short duration. The Data Science VM can help replicate the data science environment quickly on demand, on scaled out servers that allow experiments requiring high powered computing resources to be run. Short term experimentation and evaluation. The Data Science VM can be used to evaluate or learn tools such as Microsoft ML Server, SQL Server, Visual Studio tools, Jupyter, deep learning ML toolkits, and new tools popular in the community with minimal setup effort. Since the Data Science VM can be set up quickly, it can be applied in other short term usage scenarios such as replicating published experiments, executing demos, following walkthroughs in online sessions or conference tutorials. Deep learning. The data science VM can be used for training model using deep learning algorithms on GPU Graphics processing units based hardware. Utilizing VM scaling capabilites of Azure cloud, DSVM helps you use GPU based hardware on the cloud as per need. One can switch to a GPU based VM when training large models or need high speed computations while keeping the same OS disk. The Windows Server 2. DSVM comes pre installed with GPU drivers, frameworks and GPU version of the deep learning algorithms. On the Linux, deep learning on GPU is enabled only on the Data Science Virtual Machine for Linux Ubuntu edition. You can deploy the UbuntuWindows 2. Data Science VM to non GPU based Azure virtual machine in which case all the deep learning frameworks will fallback to the CPU mode. Earlier, for Windows Server 2. Deep learning toolkit but now we recommend using Windows Server 2. Windows based deep learning workloads. The Cent. OS based Linux edition of the DSVM contains only the CPU builds of some of the deep learning tools Microsoft Cognitive Toolkit, Tensor. Flow, MXNet but does not come preinstalled with the GPU drivers and frameworks. Whats included in the Data Science VM The Data Science Virtual Machine has many popular data science and deep learning tools already installed and configured. It also includes tools that make it easy to work with various Azure data and analytics products. You can explore and build predictive models on large scale data sets using the Microsoft ML Server R, Python or using SQL Server 2. A host of other tools from the open source community and from Microsoft are also included, as well as sample code and notebooks. The following table itemizes and compares the main components included in the Windows and Linux editions of the Data Science Virtual Machine. Tool. Windows Edition. Linux Edition. Microsoft R Open with popular packages pre installed. YYMicrosoft ML Server R, Python Developer Edition includes, Revo. Scale. Rrevoscalepy parallel and distributed high performance framework R PythonMicrosoft. ML New state of the art ML algorithms from Microsoft R and Python Operationalization. YYMicrosoft Office Pro Plus with shared activation Excel, Word and Power. Point. YNAnaconda Python 2. YYJulia. Pro with popular packages for Julia language pre installed. YYRelational Databases. SQL Server 2. 01. Developer Edition. Postgre. SQLCent. OS onlyDatabase toolsSQL Server Management Studio SQL Server Integration Servicesbcp, sqlcmdODBCJDBC driversSQuirre. L SQL querying tool, bcp, sqlcmd ODBCJDBC drivers. Scalable in database analytics with SQL Server ML services R, PythonYNJupyter Notebook Server with following kernels,YY    RYY    Python 2. YY    Julia. YY    Py. Spark. YY    Sparkmagic. NY Ubuntu Only    Spark. RNYJupyter. Hub Multi user notebooks serverNYDevelopment tools, IDEs and Code editors    Visual Studio 2. Community Edition with Git Plugin, Azure HDInsight Hadoop, Data Lake, SQL Server Data tools, Node. Python, and R Tools for Visual Studio RTVSYN    Visual Studio Code. YY    RStudio Desktop. YY    RStudio Server. NY    Py. Charm. NY    Atom. NY    Juno Julia IDEYY    Vim and Emacs. YY    Git and Git. Bash. YY    Open. JDKYY    . Net Framework. Adobe Photoshop Horror Brushes For Photoshop. YNPower. BI Desktop. YNSDKs to access Azure and Cortana Intelligence Suite of services. YYData Movement and management Tools    Azure Storage Explorer. YY    Azure CLIYY    Azure Powershell. YN    Azcopy. YN    AdlcopyAzure Data Lake StorageYN    Doc. DB Data Migration Tool. YN    Microsoft Data Management Gateway Move data between On. Prem and Cloud. YN    UnixLinux Command Line Utilities. YYApache Drill for Data exploration. YYMachine Learning Tools    Integration with Azure Machine Learning R, PythonYY    Xgboost. YY    Vowpal Wabbit. YY    Weka. YY    Rattle. YY    Light. GBMNY Ubuntu Only    H2.