AzureDSVM/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, operating system, authentication method, 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".

• 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 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 of kmeans clustering
• Putting all together - use case of 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.