Danhartl/r sample

samples/R/iris-caret/README.md

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+# Operationalizing R Models in AzureML
+
+## Setup
+
+Create AzureML cluster
+
+```
+az ml env setup -k
+```
+
+## Create Bundle
+
+```R
+# import bundleService
+source('https://raw.githubusercontent.com/danhartl/Machine-Learning-Operationalization/master/utils/BundleService.R')
+
+library(caret)
+data(iris)
+set.seed(12345)
+inTrain<-createDataPartition(iris$Species,p=0.7,list=FALSE)
+training<-iris[inTrain,]
+testing<-iris[-inTrain,]
+
+# train model
+
+irisModel<-train(Species~.,method="rpart",data=iris)
+
+# define operationalization functions
+
+init <- function() {
+  library(caret)
+}
+
+predictIris <- function(SepalLength, SepalWidth, PetalLength, PetalWidth) {
+  input <- data.frame(Sepal.Length = c(SepalLength), Sepal.Width = c(SepalWidth), Petal.Length = c(PetalLength), Petal.Width = c(PetalWidth))
+  result <<- as.character(predict(model, input))
+}
+
+bundleService(
+  init,
+  predictIris,
+  list(model = irisModel),
+  inputs = list(SepalLength = "numeric", SepalWidth = "numeric", PetalLength = "numeric", PetalWidth = "numeric"),
+  outputs = list(result = "character"),
+  outputFolder = "/tmp")
+
+```
+
+## Deploy
+
+```
+az ml service create realtime -n myservice1 -r mrs -f service.json -d init -d run -d model
+```
+
+## Test
+
+```
+az ml service run realtime -n myservice1 -d '{ "SepalLength": 4.7, "SepalWidth": 3.2, "PetalLength": 1.3, "PetalWidth": 0.2 }'
+```
+
+## Generate client code
+
+You can download the swagger.json metadata from /swagger.json
+
+Store it in a file and you can use [autorest](https://www.nuget.org/packages/AutoRest) to generate the client code:
+
+```
+.\AutoRest.exe -input 'swagger.json' -ClientName Service -CodeGenerator CSharp -Namespace 'AzureML' -OutputDirectory '/tmp'
+```
+
+With this you can create a sample C# application
+
+```csharp
+internal class Program
+{
+    private static void Main(string[] args)
+    {
+        var service = new Service(new Uri("<servername>"));
+
+        var webServiceResult = service.RunMLService(new InputParameters(4.7, 3.2, 1.3, 0.2));
+        Console.WriteLine(webServiceResult.OutputParameters.Result);
+    }
+}
+```