In Azure/AzureDSVM: Create and Manage Azure Data Science Virtual Machine(s) for Convenient Execution of Scalable and Elastic Analytics.

The Azure Data Science Virtual Machine (DSVM) provides a powerful platform for data scientists supporting a full open source stack of regularly used tools.

Preliminaries

1. AzureDSVM requires users to have access to Azure resources through an Azure subscription which can be obtained through a free Azure account using your credit card (but only to identify you as it will not be charged unless you later decide to take ot a subscription).

2. It is highly recommended that you read through the DSVM documentation:

   • An Overview of the DSVM;
   • Ten things you can do on the DSVM;
   • DSVM GitHub repository.

3. AzureDSVM assumes R (>= 3.3.1), with dependencies on packages of AzureSMR(>= 0.2.2), stringr (>= 1.1.0), stringi (>= 1.1.2), magrittr (>= 1.5), dplyr (>= 0.5.0).

4. Before using the functions in AzureDSVM, one needs to obtain authentication credentials for managing Azure resources. Details are available from the AzureSMR Authentication vignette.

How-to

Installation

AzureDSVM is available from its GitHub repository. To install,

if(!require("devtools")) install.packages("devtools")
devtools::install_github("Azure/AzureDSVM")

Help pages can be loaded with

library(help=AzureDSVM)

Tutorials

Tutorials on how to use AzureDSVM package are provided as 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

Azure/AzureDSVM documentation built on May 20, 2019, 2:03 p.m.