зеркало из https://github.com/Azure/AzureDSVM.git
Default is "Ubuntu" instead of "Linux"!
Added CentOS and made Ubuntu as default
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@ -17,8 +17,7 @@
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#' @param size Size of the DSVM. The default is "Standard_D1_v2". All
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#' available sizes can be obtained by function `getVMSizes`.
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#'
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#' @param os Operating system of DSVM. Permitted values are "Linux"
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#' ,"Windows", and "DeepLearning". The default is to deploy a Linux Data Science
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#' @param os Operating system of DSVM. Permitted values are "Ubuntu", "CentOS", "Windows", and "DeepLearning". The default is to deploy a Ubuntu Linux Data Science
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#' Virtual Machine. NOTE Deep learning DSVM is still Windows based but there is an extension which preinstalls GPU related drivers and libraries.
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#'
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#' @param authen Either "Key" for public-key based authentication
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@ -49,8 +48,8 @@
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#'
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#' @examples
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#' \dontrun{
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#' # The following deploys a Linux DSVM with public key based authentication
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#' deployDSVM(context, resource.group="<resource_group>", location="<location>", hostname="<machine_name>", username="<user_name>", os="Linux", pubkey="<a_valid_public_key_string_in_SSH_format>")
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#' # The following deploys a Ubuntu DSVM with public key based authentication
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#' deployDSVM(context, resource.group="<resource_group>", location="<location>", hostname="<machine_name>", username="<user_name>", os="Ubuntu", pubkey="<a_valid_public_key_string_in_SSH_format>")
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#'
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#' # The following deploys a Windows DSVM with password based authentication. The VM size is selected from all the available A-series machines that have the maximum number of computing cores.
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#'
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@ -68,8 +67,8 @@ deployDSVM <- function(context,
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hostname,
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username,
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size="Standard_D1_v2",
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os="Linux",
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authen=ifelse(os=="Linux", "Key", "Password"),
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os="Ubuntu",
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authen=ifelse(os=="Ubuntu", "Key", "Password"),
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pubkey="",
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password="",
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dns.label=hostname,
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@ -148,7 +147,7 @@ deployDSVM <- function(context,
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} else if(os == "DeepLearning") {
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temp_path <- system.file("etc", "template_deeplearning.json", package="AzureDSVM")
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para_path <- system.file("etc", "parameter_deeplearning.json", package="AzureDSVM")
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} else if(os == "Linux")
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} else if(os == "Ubuntu")
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{
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if(authen == "Key")
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{
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@ -162,9 +161,23 @@ deployDSVM <- function(context,
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{
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stop("Please specific a valid authentication method, i.e., either 'Key' for public key based or 'Password' for password based, for Linux OS based DSVM")
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}
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} else if(os == "CentOS")
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{
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if(authen == "Key")
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{
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temp_path <- system.file("etc", "template_linux_key.json", package="AzureDSVM")
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para_path <- system.file("etc", "parameter_linux_key.json", package="AzureDSVM")
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} else if(authen == "Password")
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{
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temp_path <- system.file("etc", "template_linux.json", package="AzureDSVM")
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para_path <- system.file("etc", "parameter_linux.json", package="AzureDSVM")
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} else
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{
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stop("Please specific a valid authentication method, i.e., either 'Key' for public key based or 'Password' for password based, for Linux OS based DSVM")
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}
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} else
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{
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stop("Please specify a valid OS type, i.e., either 'Windows', 'DeepLearning', or 'Linux'.")
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stop("Please specify a valid OS type, i.e., either 'Windows', 'DeepLearning', 'CentOS', or 'Ubuntu'.")
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}
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# Update the parameter JSON with the virtual machine hostname.
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@ -151,7 +151,7 @@ deployDSVMCluster <- function(context,
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hostname=hostnames[i],
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username=usernames[i],
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size=size,
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os="Linux",
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os="Ubuntu",
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authen="Key",
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pubkey=pubkeys[i],
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dns.label=hostnames[i],
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@ -47,7 +47,7 @@ library(rattle)
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USER <- Sys.info()[['user']]
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source(paste0(USER, "_credentials.R"))
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source(file.path("..", paste0(USER, "_credentials.R")))
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```
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```{r}
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@ -98,42 +98,42 @@ if (! rg_pre_exists) azureCreateResourceGroup(context, RG, LOC)
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existsRG(context, RG, LOC)
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```
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## Deploy a Linux Data Science Virtual Machine
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## Deploy a Ubuntu Data Science Virtual Machine
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Create the actual Linux DSVM with public-key based authentication
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Create the actual Ubuntu DSVM with public-key based authentication
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method. Name, username, and size can also be configured.
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```{r deploy}
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# Create the required Linux DSVM - generally 4 minutes.
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# Create the required Ubuntu DSVM - generally 4 minutes.
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ldsvm <- deployDSVM(context,
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resource.group=RG,
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location=LOC,
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name=LDSVM,
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hostname=HOST,
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username=USER,
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size="Standard_DS1_v2",
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os="Linux",
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size="Standard_D12_v2",
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os="Ubuntu",
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authen="Key",
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pubkey=PUBKEY)
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ldsvm
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operateDSVM(context, RG, LDSVM, operation="Check")
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operateDSVM(context, RG, HOST, operation="Check")
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azureListVM(context, RG)
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```
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## Deploy a cluster of Linux Data Science Virtual Machines.
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## Deploy a cluster of Ubuntu Data Science Virtual Machines.
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```{r}
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# Create a set of Linux DSVMs and they will be formed as a cluster.
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# Create a set of Ubuntu DSVMs and they will be formed as a cluster.
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ldsvm_set <- deployDSVMCluster(context,
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resource.group=RG,
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location=LOC,
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count=COUNT,
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name=LDSVM,
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name=HOST,
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username=LUSER,
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pubkey=rep(PUBKEY, COUNT),
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cluster=FALSE)
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@ -248,10 +248,14 @@ mlProcess <- function(formula, data, modelName, modelPara) {
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```
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The worker script can be executed on a remote Linux DSVM or DSVM cluster with AzureDSVM function `executeScript` like what has been done in the previous tutorials.
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The worker script can be executed on a remote Ubuntu DSVM or DSVM cluster with AzureDSVM function `executeScript` like what has been done in the previous tutorials.
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The worker script for binary classification is located in "/test" directory, with name "worker_classficiation.R".
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```{r}
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VM_URL <- paste(HOST, LOC, "cloudapp.azure.com", sep=".")
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```
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```{r execution}
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# remote execution on a single DSVM.
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@ -260,10 +264,10 @@ time1 <- Sys.time()
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executeScript(context,
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resource.group=RG,
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machines=LDSVM,
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machines=HOST,
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remote=VM_URL,
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user=USER,
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script="./workerClassification.R",
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script="../test/workerClassification.R",
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master=VM_URL,
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slaves=VM_URL,
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compute.context="localParallel")
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