rxDForest in RStudio and SSMS - SQL Server

Machine Learning and Modeling
1

For discussions related to modeling, machine learning and deep learning. Related packages include RevoScaleR.

Hi all. I'm looking for an advise/help.
I've created ML solution using rxDForest. I use RStudio as a dev environment and sys.sp_execute_external_script on SQL Server to productionize it. Both, RStudio and SQL Server point into R instance on the server - use R Server.
Although I run the same code on these two environments, I get different results.
I've also created solution using AdventureWorksDW2017 - also have different results on RStudio and SSMS. Moreover, my mate run the same code on his laptop, using SSMS and R Client, and have the same results as my on SSMS and R Server.
Can anybody tell why?

use AdventureWorksDW2014
go
DROP PROC IF EXISTS rxDForest_demo
GO
CREATE PROC rxDForest_demo
AS
BEGIN 
EXECUTE sys.sp_execute_external_script
	@language = N'R'
	, @script = N' 
				
				set.seed(2018);			
								
				splitList <- rxSplit(	
                    inData = train,
                    splitByFactor = "splitLabel",
                    transforms = list(splitLabel =
                                        factor(sample(0:1,
                                                      size = .rxNumRows,
                                                      replace = TRUE,
                                                      prob = c(0.7, 0.3)),
                                               levels = 0:1,
                                               labels = c("Train", "Test"))),
                    overwrite = TRUE
                  );

				# split data to training and test set
				trainDF <- rxDataStep(inData = splitList[[2]], varsToDrop = c("splitLabel"));
				testDF <- rxDataStep(inData = splitList[[1]], varsToDrop = c("splitLabel"));

				buyer_train_Forest <- rxDForest(BikeBuyer ~
												 NumberCarsOwned
												 +CommuteDistance
												 +Gender
												 +NumberChildrenAtHome
												 +MaritalStatus
												 +YearlyIncome,
												  seed = 10,
												  data = trainDF, 
												  cp=0.01, 
												  nTree=100, #500
												  mTry=5);

					 predictBuyer <- rxPredict(modelObject = buyer_train_Forest,
												  data = testDF,
												  #data = cdrTestSQL,
												  type = "prob",
												  overwrite = TRUE);
					buyer_Y_N <- 0.5;
    
					predictBuyer$pred_Y_N <- ifelse(predictBuyer$BikeBuyer_Pred > buyer_Y_N, "Y", "N");
    
					predictDF <- cbind(testDF[,8], predictBuyer)


										trained_model <- data.frame(predictDF); '

	, @input_data_1 = N'select top 2000
      						a.BikeBuyer
					  , a.NumberCarsOwned
					  , a.CommuteDistance
					  , a.Gender
    						, a.NumberChildrenAtHome
    						, a.MaritalStatus
					  , a.YearlyIncome
					  , a.CustomerKey
					  from vTargetMail a
					  order by a.CustomerKey;'
	, @input_data_1_name = N'train' 
    , @output_data_1_name = N'trained_model'
	WITH RESULT SETS (([CustomerKey] int
					, [Probability] decimal(18,6)
					, [Buyer] char(1)
					)) 
END;