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:

• Get started
• Deployment of a single DSVM
• Deployment of multiple DSVMs
• Do computation on a single DSVM or a cluster of DSVMs
• Monitor data consumption and expense spent on using DSVM(s)
• Putting all together
  • Use case - k-means clustering
  • Use case - Hot spots analysis
  • Use case - Binary classification

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.