[GitHub] shivonkar commented on issue #8575: mxnet multicore on LInux in R

[GitBox <gi...@apache.org>]

shivonkar commented on issue #8575: mxnet multicore on LInux in R
URL: https://github.com/apache/incubator-mxnet/issues/8575#issuecomment-343836005
 
 
   The working test code is here and error is attached
   
   g_working_directory <- ifelse(Sys.info()['sysname'] == "Windows",  "D:/INFY_BACKUP/Projects/Analytics/Practice/R/Prospects/amex/analytics/rscript", "/data/data/analytics/rscript/")[[1]]
       setwd(g_working_directory);
       if(Sys.info()['sysname'] == "Linux") .libPaths(c("/data/data/analytics/packages/", .libPaths()))
       
       library(logging); library(mxnet)
       
       #Adding logger
       addHandler(handler = writeToFile, file = "log.txt"); basicConfig(level = "INFO")
       
       # Get data for training    
       train.x <- data.matrix(airquality[,c('Ozone', 'Solar.R', 'Wind')])
       train.x[is.na(train.x)] <- 0
       train.y <- as.numeric(ifelse(airquality[,c('Temp')] < 70, 0, 1))
       
       # Prepare the layers
       data <- mx.symbol.Variable("data")
       fc1 <- mx.symbol.FullyConnected(data, name="fc1", num_hidden=3 )
       act1 <- mx.symbol.Activation(fc1, name="relu1", act_type="relu") # "relu" tanh
       fc2 <- mx.symbol.FullyConnected(act1, name="fc2", num_hidden=3 )
       act2 <- mx.symbol.Activation(fc2, name="relu2", act_type="relu")
       fc3 <- mx.symbol.FullyConnected(act2, name="fc3", num_hidden=2)
       softmax <- mx.symbol.SoftmaxOutput(fc3, name="sm")
       
       # Get all available CPUs. http://mxnet.io/how_to/env_var.html
       core <- as.integer(min(7, parallel::detectCores()))
       cpu_devices = lapply(1:core, function(i) {mx.cpu(i)})
       loginfo(paste0('Available cores are: ', parallel::detectCores(), ', and using ', core))
       
       l_list_tuned_param <- list(act_type="relu", num.round=10, array.batch.size=8, learning.rate=0.07, momentum=0.9,  initializer=0.01, optimizer ="sgd")
       
       # Building model
       
       tryCatch(fit_dl <- mx.model.FeedForward.create(softmax, X=data.matrix(train.x), y = as.numeric(train.y), ctx=cpu_devices, num.round=l_list_tuned_param[['num.round']], array.batch.size=l_list_tuned_param[['array.batch.size']], learning.rate=l_list_tuned_param[['learning.rate']], momentum=l_list_tuned_param[['momentum']],  initializer=mx.init.uniform(l_list_tuned_param[['initializer']]), optimizer = l_list_tuned_param[['optimizer']],eval.metric=mx.metric.accuracy, array.layout = "rowmajor"), error = function(cond){logerror(cond); quit(save = "no", status = 0, runLast = F)})
       
       loginfo("Building complete")  
   

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