AzureDSVM is an R package that offers convenient harness of Azure DSVM, remote execution of scalable and elastic data science work, and monitoring of on-demand resource consumption.
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README.md

AzureDSVM

The AzureDSVM (Azure Data Science Virtual Machine) is an R Package for Data Scientists working with the Azure compute platform as a complement to the underlying AzureSMR for controlling Azure Data Science Virtual Machines.

Azure Data Science Virtual Machine (DSVM) is a powerful data science development environment with pre-installed tools and packages that empower data scientists for convenient data wrangling, model building, and service deployment.

The R package of AzureDSVM aims at offering functions that can be conveniently used by R data scientists for operating and using Azure Data Science Virtual Machine (DSVM) elastically and economically within local R session.

To install the package from github:

devtools::install_github("Azure/AzureDSVM")

Help pages are also provided for all functions within the package. With RStudio for example type AzureDSVM into search when the package is loaded to see a list of functions/help pages or else

library(help=AzureDSVM)

Note: The package will work with any open source R Session or with Microsoft R extensions.

Features

  • Elasiticity

    • Deployment of a DSVM with customized information such as machine name, machine size (with compute/memory optimized general-purpose CPU, Nvidia K80/M60 GPU, etc.), operating system (Windows Server 2016, Ubunbut 16.04, and CentOS), authentication method (public key based or password based), etc.
    • Enjoy all benefits of a Windows/Linux DSVM. E.g., all tools for data science work such as R/Python/Julia programming languages, SQL Server, Visual Studio with RTVS, etc., remote working environment via RStudio Server or Jupyter Notebook interface, and machine learning & artificial intelligence packages such as Microsoft CNTK, MXNet, and XGBoost.
    • Execution of R analytics on DSVM(s) with various Microsoft R Server computing contexts such as "local parallel" and "cluster parallel".
    • Seamless interaction with remote R Server session with mrsdeploy functions.
    • Post-deployment installation of extension for customizing system environment, reinstalling/uninstalling software, etc.
  • Scalability

    • Deployment of a collection of heterogeneous DSVMs for a group of data scientists.
    • Scale up DSVM and form them into a cluster for parallel/distributed computation with Microsoft R Server backend.
  • Usability

    • Deploy, start, stop, and delete DSVM(s) on demand.
    • Monitor data consumption and estimate expense of using DSVM(s) with hourly aggregation granularity.

Tutorials

To get started with this package, see the Vignettes:

Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.