Merge branch 'master' of github.com:Azure/AzureDSR

Makefile

@@ -9,5 +9,11 @@ include r.mk
 
 include git.mk
 
-# Cleanup
+# Utilities
+
+deploy: scripts
+	(cd vignettes; Rscript DeployDSVM.R)
+
+delete: scripts
+	(cd vignettes; Rscript DeleteRG.R)
 

r.mk

@@ -20,8 +20,8 @@ build:
 install: build
 	R CMD INSTALL $(PKG)_$(VER).tar.gz
 
-.PHONY: vignettes
-vignettes: $(VR)
+.PHONY: scripts
+scripts: $(VR)
 
 # Cleanup
 

vignettes/ClusterDSVM.Rmd

@@ -14,6 +14,20 @@ cease.
 This script is best run interactively to review its operation and to
 ensure that the interaction with Azure completes.
 
+A common use case is for a Data Scientist to create their R programs
+to analyse a dataset on their local compute platform (e.g., a laptop
+with 6GB RAM running Ubuntu with R installed). Development is
+performed with a subset of the full dataset (a random sample) that
+will not exceed the available memory and will return results
+quickly. When the experimental setup is complete the script can be
+sent across to a considerably more capable compute engine on Azure,
+possibly a cluster of servers to build models in parallel.
+
+This tutorial will deploy several Linux Data Science Virtual Machines
+(DSVMs), distribute a copmute task over those servers, colelct the
+results and generate a report, and then delete the compute
+resources.
+
 # Setup
 
 To get started load our Azure credentials as well as the user's ssh

vignettes/DeleteRG.Rmd

@@ -0,0 +1,113 @@
+---
+title = "Using Azure Data Science Resources: Delete a Resource Group"
+author= "Graham Williams"
+---
+
+# Use Case
+
+A sample deployment of a Linux Data Science Virtual Machine (DSVM) is
+presented in the
+[AzureDSR's Deploy DSMV](https://github.com/Azure/AzureDSR/blob/master/vignettes/DeployDSVM.Rmd)
+document. All of the resources (the virtual machine, public IP
+address, network interface, storage account, network security group,
+and virtual network) will be hosted within a single resource
+group. Thus it is convenient to simply delete the resource group to
+remove the deployment of the DSVM. 
+
+This script can be run after the
+[AzureDSR's Deploy DSMV](https://github.com/Azure/AzureDSR/blob/master/vignettes/DeployDSVM.Rmd)
+script to delete all resources created in that script.
+
+# Preparation
+
+We assume the user already has an Azure subscription and we have
+obtained the credentials required. See
+[AzureSMR's Authentication Guide](https://github.com/Microsoft/AzureSMR/blob/master/vignettes/Authentication.Rmd)
+for details.  We will then ensure the resource group exists and then
+delete it.
+
+# Setup
+
+To get started we need to load our Azure credentials.
+
+```{r credentials, eval=FALSE}
+# Credentials come from app creation in Active Directory within Azure.
+ 
+TID <- "72f9....db47"          # Tenant ID
+CID <- "9c52....074a"          # Client ID
+KEY <- "9Efb....4nwV....ASa8=" # User key
+
+```
+
+We might be able to load such information from the file
+<USER>_credentials.R where <USER> is replaced with your username.
+
+```{r setup}
+# Load the required subscription resources: TID, CID, and KEY.
+
+USER <- Sys.getenv("USER")
+
+source(paste0(USER, "_credentials.R"))
+
+# Install the packages if required.
+
+## devtools::install_github("Microsoft/AzureSMR")
+## devtools::install_github("Azure/AzureDSR", auth_token=GIT_TOKEN)
+```
+
+```{r packages}
+# Load the required packages.
+
+library(AzureSMR)    # Support for managing Azure resources.
+library(AzureDSR)    # Further support for the Data Scientist.
+library(magrittr)
+library(dplyr)
+```
+
+```{r tuning}
+# Parameters for this script: the name for the new resource group and
+# its location across the Azure cloud. The resource name is used to
+# name the resource group that we will create transiently for the
+# purposes of this script.
+
+RG    <- "my_dsvm_rg_sea"  # The resource group to be deleted.
+```
+
+```{r connect}
+# Connect to the Azure subscription and use this as the context for
+# our activities.
+
+context <- createAzureContext(tenantID=TID, clientID=CID, authKey=KEY)
+
+# Check if the resource group already exists. Take note this script
+# will not remove the resource group if it pre-existed.
+
+context %>%
+  azureListRG() %>%
+  filter(name == RG) %>%
+  select(name, location) %T>%
+  print() %>%
+  nrow() %>%
+  equals(0) %>%
+  not() %T>%
+  print() ->
+rg_pre_exists
+```
+
+# Delete the Resource Group
+
+Delete the resource group within which all resources were created.
+
+```{r create resource group}
+if (rg_pre_exists)
+{
+  # Delete the resource group RG.
+
+  # Note that to delete a resource group can take some time, like 10 minutes.
+
+  azureDeleteResourceGroup(context, RG)
+
+}
+```
+
+Once deleted we are consuming no more.

vignettes/DeployDSVM.Rmd

@@ -1,5 +1,5 @@
 ---
-title = "Using Azure Data Science Resources: Connect to Linux DSVM Quick Start"
+title = "Using Azure Data Science Resources: Deploy Linux DSVM"
 author= "Graham Williams"
 ---
 
@@ -11,7 +11,9 @@ not run to then delete the resource group if the resources are no
 longer required. Once deleted consumption will cease.
 
 This script is best run interactively to review its operation and to
-ensure that the interaction with Azure completes.
+ensure that the interaction with Azure completes. As a standalone
+script it can be run to setup a new resource group and single Linux
+DSVM.
 
 # Preparation
 
@@ -69,7 +71,6 @@ library(AzureSMR)    # Support for managing Azure resources.
 library(AzureDSR)    # Further support for the Data Scientist.
 library(magrittr)    
 library(dplyr)
-library(rattle)      # Use weatherAUS as a "large" dataset.
 ```
 
 ```{r tuning}