Read large (1.5GB) file in h2o R
I am using h2o package for modeling in R. For this, I want to read a dataset of about 1.5 GB using h2o.importfile (). I am starting an h2o server using the lines
library(h2oEnsemble)
h2o.init(max_mem_size = '1499m',nthreads=-1)
This creates a log
H2O is not running yet, starting it now...
java version "1.8.0_121"
Java(TM) SE Runtime Environment (build 1.8.0_121-b13)
Java HotSpot(TM) Client VM (build 25.121-b13, mixed mode)
Starting H2O JVM and connecting: . Connection successful!
R is connected to the H2O cluster:
H2O cluster uptime: 3 seconds 665 milliseconds
H2O cluster version: 3.10.4.8
H2O cluster version age: 28 days, 14 hours and 36 minutes
H2O cluster name: H2O_started_from_R_Lucifer_jvn970
H2O cluster total nodes: 1
H2O cluster total memory: 1.41 GB
H2O cluster total cores: 4
H2O cluster allowed cores: 4
H2O cluster healthy: TRUE
H2O Connection ip: localhost
H2O Connection port: 54321
H2O Connection proxy: NA
H2O Internal Security: FALSE
R Version: R version 3.3.2 (2016-10-31)`
The following line gives me an error
train=h2o.importFile(path=normalizePath("C:\\Users\\All data\\traindt.rds"))
DistributedException from localhost/127.0.0.1:54321, caused by java.lang.AssertionError
DistributedException from localhost/127.0.0.1:54321, caused by java.lang.AssertionError
at water.MRTask.getResult(MRTask.java:478)
at water.MRTask.getResult(MRTask.java:486)
at water.MRTask.doAll(MRTask.java:402)
at water.parser.ParseDataset.parseAllKeys(ParseDataset.java:246)
at water.parser.ParseDataset.access$000(ParseDataset.java:27)
at water.parser.ParseDataset$ParserFJTask.compute2(ParseDataset.java:195)
at water.H2O$H2OCountedCompleter.compute(H2O.java:1315)
at jsr166y.CountedCompleter.exec(CountedCompleter.java:468)
at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263)
at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974)
at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477)
at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104)
Caused by: java.lang.AssertionError
at water.parser.Categorical.addKey(Categorical.java:41)
at water.parser.FVecParseWriter.addStrCol(FVecParseWriter.java:127)
at water.parser.CsvParser.parseChunk(CsvParser.java:133)
at water.parser.Parser.readOneFile(Parser.java:187)
at water.parser.Parser.streamParseZip(Parser.java:217)
at water.parser.ParseDataset$MultiFileParseTask.streamParse(ParseDataset.java:907)
at water.parser.ParseDataset$MultiFileParseTask.map(ParseDataset.java:856)
at water.MRTask.compute2(MRTask.java:601)
at water.H2O$H2OCountedCompleter.compute1(H2O.java:1318)
at water.parser.ParseDataset$MultiFileParseTask$Icer.compute1(ParseDataset$MultiFileParseTask$Icer.java)
at water.H2O$H2OCountedCompleter.compute(H2O.java:1314)
... 5 more
Error: DistributedException from localhost/127.0.0.1:54321, caused by java.lang.AssertionError
Any help on fixing this issue? Note. Assigning memory larger than 1499 MB also gives me an error (cannot allocate memory). I am using a 16GB memory environment
Edit: I download 64-bit Java and change my file to a csv file. Then I was able to assign max_mem_size to 5G and the problem was resolved.
For those facing the problem: 1. Download the latest 64-bit jdk 2. Execute the following line of line
h2o.init(max_mem_size = '5g',nthreads=-1)
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train=h2o.importFile(path=normalizePath("C:\\Users\\All data\\traindt.rds")
Are you trying to upload a file .rds
? This is a binary R format that is unreadable h2o.importFile()
, so it won't work. You will need to save your training data in a cross-platform storage format (like CSV, SMVLight, etc.) if you want to read it directly into H2O. If you don't have a copy in another format, just save it in R:
# loads a `train` data.frame for example
load("C:\\Users\\All data\\traindt.rds")
# save as CSV
write.csv(train, "C:\\Users\\All data\\traindt.csv")
# import from CSV into H2O cluster directly
train = h2o.importFile(path = normalizePath("C:\\Users\\All data\\traindt.csv"))
Another option is to load it into R from a file .rds
and use the function as.h2o()
:
# loads a `train` data.frame for example
load("C:\\Users\\All data\\traindt.rds")
# send to H2O cluster
hf <- as.h2o(train)
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You are working with 32 bit java, which limits the memory you can run H2O with. One clue is that it won't start at a higher max_mem_size. Another clue is that it says "Client VM".
You want to use 64 bit Java instead. The 64-bit version will say "VM Server". You can download Java 8 SE JDK:
http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html
Based on what you described, I recommend setting max_mem_size = '6g' or more, which will work fine on your system after installing the correct Java version.
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