Provision a Linux Ubuntu Data Science Virtual Machine on Azure. The Data Science Virtual Machine for Linux is an Ubuntu based virtual machine image that makes it easy to get started with deep learning on Azure. Deep learning tools include Caffe A deep learning framework built for speed, expressivity, and modularity. Caffe. 2 A cross platform version of Caffe. Microsoft Cognitive Toolkit A deep learning software toolkit from Microsoft Research. H2. O An open source big data platform and graphical user interface. Keras A high level neural network API in Python for Theano and Tensor. Flow. MXNet A flexible, efficient deep learning library with many language bindings. NVIDIA DIGITS A graphical system that simplifies common deep learning tasks. Tensor. Flow An open source library for machine intelligence from Google. Theano A Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi dimensional arrays. Torch A scientific computing framework with wide support for machine learning algorithms. CUDA, cu. DNN, and the NVIDIA driver. Many sample Jupyter notebooks. Install Mysql Query Browser On Ubuntu Studio' title='Install Mysql Query Browser On Ubuntu Studio' />All libraries are the GPU versions, though they also run on the CPU. The Data Science Virtual Machine for Linux also contains popular tools for data science and development activities, including Microsoft R Server Developer Edition with Microsoft R Open. Anaconda Python distribution versions 2. Julia. Pro a curated distribution of Julia language with popular scientific and data analytics libraries. Standalone Spark instance and single node Hadoop HDFS, YarnJupyter. Hub a multiuser Jupyter notebook server supporting R, Python, Py. Spark, Julia kernels. 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Power management has been one of our focus points for improvements for Red Hat Enterprise Linux 6. Limiting the power used by computer systems is one of the most. Azure Storage Explorer. Azure command line interface CLI for managing Azure resources. Machine learning tools. Vowpal Wabbit A fast machine learning system supporting techniques such as online, hashing, allreduce, reductions, learning. XGBoost A tool providing fast and accurate boosted tree implementation. MariaDB if still using MySQL The original MySQL was created by a FinnishSwedish company, MySQL AB, founded by David Axmark, Allan Larsson. In this article we will explain how to install, maintain and use MS SQL Server on RHEL CentOS and Ubuntu Server. MySQL officially pronounced as m a s k ju l My SQL, is an opensource relational database management system RDBMS. Its name is a. Rattle A graphical tool that makes getting started with data analytics and machine learning in R easy. Light. GBM A fast, distributed, high performance gradient boosting framework. Azure SDK in Java, Python, node. Ruby, PHPLibraries in R and Python for use in Azure Machine Learning and other Azure services. Development tools and editors RStudio, Py. Charm, Intelli. J, Emacs, vimDoing data science involves iterating on a sequence of tasks Finding, loading, and pre processing data. Building and testing models. Deploying the models for consumption in intelligent applications. Data scientists use various tools to complete these tasks. It can be quite time consuming to find the appropriate versions of the software, and then to download, compile, and install these versions. The Data Science Virtual Machine for Linux can ease this burden substantially. Use it to jump start your analytics project. It enables you to work on tasks in various languages, including R, Python, SQL, Java, and C. The Azure SDK included in the VM allows you to build your applications by using various services on Linux for the Microsoft cloud platform. In addition, you have access to other languages like Ruby, Perl, PHP, and node. There are no software charges for this data science VM image. You pay only the Azure hardware usage fees that are assessed based on the size of the virtual machine that you provision. More details on the compute fees can be found on the VM listing page on the Azure Marketplace. Other Versions of the Data Science Virtual Machine. A Cent. OS image is also available, with many of the same tools as the Ubuntu image. A Windows image is available as well. Prerequisites. Before you can create a Data Science Virtual Machine for Linux, you must have an Azure subscription. To obtain one, see Get Azure free trial. Create your Data Science Virtual Machine for Linux. Here are the steps to create an instance of the Data Science Virtual Machine for Linux Navigate to the virtual machine listing on the Azure portal. Click Create at the bottom to bring up the wizard. The following sections provide the inputs for each of the steps in the wizard enumerated on the right of the preceding figure used to create the Microsoft Data Science Virtual Machine. Here are the inputs needed to configure each of these steps a. Basics Name Name of your data science server you are creating. User Name First account sign in ID. Password First account password you can use SSH public key instead of password. Subscription If you have more than one subscription, select the one on which the machine is to be created and billed. You must have resource creation privileges for this subscription. Resource Group You can create a new one or use an existing group. Location Select the data center that is most appropriate. Usually it is the data center that has most of your data, or is closest to your physical location for fastest network access. Size Select one of the server types that meets your functional requirement and cost constraints. Select View All to see more choices of VM sizes. Select an NC class VM for GPU training. Settings Disk Type Choose Premium if you prefer a solid state drive SSD. Otherwise, choose Standard. GPU VMs require a Standard disk. Storage Account You can create a new Azure storage account in your subscription, or use an existing one in the same location that was chosen on the Basics step of the wizard. Other parameters In most cases, you just use the default values. To consider non default values, hover over the informational link for help on the specific fields. Summary Verify that all information you entered is correct. Buy To start the provisioning, click Buy. A link is provided to the terms of the transaction. The VM does not have any additional charges beyond the compute for the server size you chose in the Size step. The provisioning should take about 5 1. The status of the provisioning is displayed on the Azure portal. How to access the Data Science Virtual Machine for Linux. After the VM is created, you can sign in to it by using SSH. Use the account credentials that you created in the Basics section of step 3 for the text shell interface. On Windows, you can download an SSH client tool like Putty. If you prefer a graphical desktop X Windows System, you can use X1. Putty or install the X2. Go client. Note. The X2. Go client performed better than X1. We recommend using the X2. Go client for a graphical desktop interface. Installing and configuring X2. Go client. The Linux VM is already provisioned with X2. Go server and ready to accept client connections. To connect to the Linux VM graphical desktop, complete the following procedure on your client Download and install the X2. Go client for your client platform from X2. Go. Run the X2. Go client, and select New Session. It opens a configuration window with multiple tabs. Enter the following configuration parameters Session tab Host The host name or IP address of your Linux Data Science VM. Login User name on the Linux VM. SSH Port Leave it at 2. Session Type Change the value to XFCE. Currently the Linux VM only supports XFCE desktop. Media tab You can turn off sound support and client printing if you dont need to use them. Shared folders If you want directories from your client machines mounted on the Linux VM, add the client machine directories that you want to share with the VM on this tab. After you sign in to the VM by using either the SSH client or XFCE graphical desktop through the X2. The Storage Enthusiasts Daily Bag. If youre moving huge files around or need to keep your entire media library with you, sometimes the good ol fashioned sneakernet is the best method. 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