I am trying to run a twitter sentiment analysis with Java and got hands on a 1.6 million lines traningset, with each line having either a 0 or a 1 and a twitter text. The .csv is around 250 MB large.
I use OpenNLP for analyzing and try to train my model like this:
InputStream dataIn = new
FileInputStream("secret_path_to_my.csv");
ObjectStream lineStream = new PlainTextByLineStream(dataIn, "UTF-8");
ObjectStream sampleStream = new DocumentSampleStream(lineStream);
int cutoff = 2;
int trainingIterations = 3;
model = DocumentCategorizerME.train("en", sampleStream, cutoff, trainingIterations);
Unfortunately, this is what I get:
Sorting and merging events... done. Reduced 1599474 events to 1579730.
Done indexing.
Incorporating indexed data for training...
done.
Number of Event Tokens: 1579730
Number of Outcomes: 495970
Number of Predicates: 281447
Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
I already went to my Run Configurations in Eclipse and entered exactly the following in the VM section: "-Xmx16g" as my laptop has 16GB RAM.
Unfortunately, this only gets me some more seconds, before the program collapses, throwing the same error.
Please help me with locating the issue. I am not sure, whether decreasing the training set is the only option, or whether I am making some other mistakes. Thanks in advance!
Related
I have a few thousand of video files in my BlobStorage, which I set it as a datastore.
This blob storage receives new files every night and I need to split the data and register each split as a new version of AzureML Dataset.
This is how I do the data split, simply getting the blob paths and splitting them.
container_client = ContainerClient.from_connection_string(AZ_CONN_STR,'keymoments-clips')
blobs = container_client.list_blobs('soccer')
blobs = map(lambda x: Path(x['name']), blobs)
train_set, test_set = get_train_test(blobs, 0.75, 3, class_subset={'goal', 'hitWoodwork', 'penalty', 'redCard', 'contentiousRefereeDecision'})
valid_set, test_set = split_data(test_set, 0.5, 3)
train_set, test_set, valid_set are just nx2 numpy arrays containing blob storage path and class.
Here is when I try to create a new version of my Dataset:
datastore = Datastore.get(workspace, 'clips_datastore')
dataset_train = Dataset.File.from_files([(datastore, b) for b, _ in train_set[:4]], validate=True, partition_format='**/{class_label}/*.mp4')
dataset_train.register(workspace, 'train_video_clips', create_new_version=True)
How is it possible that the Dataset creation seems to hang for an indefinite time even with only 4 paths?
I saw in the doc that providing a list of Tuple[datastore, path] is perfectly fine. Do you know why?
Thanks
Do you have your Azure Machine Learning Workspace and your Azure Storage Account in different Azure Regions? If that's true, latency may be a contributing factor with validate=True.
Another possibility may be slowness in the way datastore paths are resolved. This is an area where improvements are being worked on.
As an experiment, could you try creating the dataset using a url instead of datastore? Let us know if that makes a difference to performance, and whether it can unblock your current issue in the short term.
Something like this:
dataset_train = Dataset.File.from_files(path="https://bloburl/**/*.mp4?accesstoken", validate=True, partition_format='**/{class_label}/*.mp4')
dataset_train.register(workspace, 'train_video_clips', create_new_version=True)
I'd be interested to see what happens if you run the dataset creation code twice in the same notebook/script. Is it faster the second time? I ask because it might be an issue with the .NET core runtime startup (which would only happen on the first time you run the code)
EDIT 9/16/20
While it doesn't seem to make sense that .NET core invoked when not data is moving, is suspect it is the validate=True part of the param that requires that all the data be inspected (which can computationally expensive). I'd be interested to see what happens if that param is False
After having done :
CREATE INDEX ON :Frag(frag)
I applied the following request on neo4j from a jupyter notebook thanks to py2neo.
WITH [f1,f2,f3,...] as list1
CALL apoc.algo.cover(list1)
YIELD rel
RETURN rel
The fi are the ID defined in my csv file, with "frag:ID" on the first line.
I tried different sizes of lists and I obtained the following result:
my result with different sizes
I remark that the delay depend on the try of the same request. Is it normal ?
For my database of 45 GB, neo4j uses all my memory (no uncolored space memory with htop command). Do I have a problem of memory ? If I watch the activity of my CPUs, it seems that neo4j waits a lot (little activity) before that one of the CPU becomes full occupied.
Due to I constantly reach memory size limit in my R Session (8GB Windows PC) I start to remove big objects loaded in. However once I reach this limit, removing objects seems not to work.
So, I was wondering if there's a way to get the R Session size. I know that it's possible to retrieve objects' size (saw in this thread).I want to know if there's a way to count the complete R Session size though (loaded packages, objects, etc).
Thank you!
I personally use this function to get the available memory:
getAvailMem <- function(format = TRUE) {
gc()
if (Sys.info()[["sysname"]] == "Windows") {
memfree <- 1024^2 * (utils::memory.limit() - utils::memory.size())
} else {
# http://stackoverflow.com/a/6457769/6103040
memfree <- 1024 * as.numeric(
system("awk '/MemFree/ {print $2}' /proc/meminfo", intern = TRUE))
}
`if`(format, format(structure(memfree, class = "object_size"),
units = "auto"), memfree)
}
To get the total memory used by R, you may try mem_used() from pryr package. Unlike memory.size, this one is not OS dependent, because it uses the R function gc() underneath it. Try to look in the function body and also this pryr:::node_size and pryr:::show_bytes
pryr::mem_used()
The help file ?pryr::mem_used describes
R breaks down memory usage into Vcells (memory used by vectors) and
Ncells (memory used by everything else). However, neither this
distinction nor the "gc trigger" and "max used" columns are typically
important. What we're usually most interested in is the the first
column: the total memory used. This function wraps around gc() to
return the total amount of memory (in megabytes) currently used by R.
You can also use pryr::mem_change to track the size of the memory used by the R code. Try the example in its documentation page.
The numbers such as 28L and 56L used to refer node size with pryr:::node_size comes from the help file of ?gc, which describes
gc returns a matrix with rows "Ncells" (cons cells), usually 28 bytes
each on 32-bit systems and 56 bytes on 64-bit systems, and "Vcells"
(vector cells, 8 bytes each),
After removing a large object run gc() to free memory
With SparkR, I'm trying for a PoC to collect an RDD that I created from text files which contains around 4M lines.
My Spark cluster is running in Google Cloud, is bdutil deployed and is composed with 1 master and 2 workers with 15gb of RAM and 4 cores each. My HDFS repository is based on Google Storage with gcs-connector 1.4.0.
SparkR is intalled on each machine, and basic tests are working on small files.
Here is the script I use :
Sys.setenv("SPARK_MEM" = "1g")
sc <- sparkR.init("spark://xxxx:7077", sparkEnvir=list(spark.executor.memory="1g"))
lines <- textFile(sc, "gs://xxxx/dir/")
test <- collect(lines)
First time I run this, it seems to be working fine, all the tasks are run successfully, spark's ui says that the job completed, but I never get the R prompt back :
15/06/04 13:36:59 WARN SparkConf: Setting 'spark.executor.extraClassPath' to ':/home/hadoop/hadoop-install/lib/gcs-connector-1.4.0-hadoop1.jar' as a work-around.
15/06/04 13:36:59 WARN SparkConf: Setting 'spark.driver.extraClassPath' to ':/home/hadoop/hadoop-install/lib/gcs-connector-1.4.0-hadoop1.jar' as a work-around.
15/06/04 13:36:59 INFO Slf4jLogger: Slf4jLogger started
15/06/04 13:37:00 INFO Server: jetty-8.y.z-SNAPSHOT
15/06/04 13:37:00 INFO AbstractConnector: Started SocketConnector#0.0.0.0:52439
15/06/04 13:37:00 INFO Server: jetty-8.y.z-SNAPSHOT
15/06/04 13:37:00 INFO AbstractConnector: Started SelectChannelConnector#0.0.0.0:4040
15/06/04 13:37:54 INFO GoogleHadoopFileSystemBase: GHFS version: 1.4.0-hadoop1
15/06/04 13:37:55 WARN LoadSnappy: Snappy native library is available
15/06/04 13:37:55 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
15/06/04 13:37:55 WARN LoadSnappy: Snappy native library not loaded
15/06/04 13:37:55 INFO FileInputFormat: Total input paths to process : 68
[Stage 0:=======================================================> (27 + 10) / 68]
Then after a CTRL-C to get the R prompt back, I try to run the collect method again, here is the result :
[Stage 1:==========================================================> (28 + 9) / 68]15/06/04 13:42:08 ERROR ActorSystemImpl: Uncaught fatal error from thread [sparkDriver-akka.remote.default-remote-dispatcher-5] shutting down ActorSystem [sparkDriver]
java.lang.OutOfMemoryError: Java heap space
at org.spark_project.protobuf.ByteString.toByteArray(ByteString.java:515)
at akka.remote.serialization.MessageContainerSerializer.fromBinary(MessageContainerSerializer.scala:64)
at akka.serialization.Serialization$$anonfun$deserialize$1.apply(Serialization.scala:104)
at scala.util.Try$.apply(Try.scala:161)
at akka.serialization.Serialization.deserialize(Serialization.scala:98)
at akka.remote.MessageSerializer$.deserialize(MessageSerializer.scala:23)
at akka.remote.DefaultMessageDispatcher.payload$lzycompute$1(Endpoint.scala:58)
at akka.remote.DefaultMessageDispatcher.payload$1(Endpoint.scala:58)
at akka.remote.DefaultMessageDispatcher.dispatch(Endpoint.scala:76)
at akka.remote.EndpointReader$$anonfun$receive$2.applyOrElse(Endpoint.scala:937)
at akka.actor.Actor$class.aroundReceive(Actor.scala:465)
at akka.remote.EndpointActor.aroundReceive(Endpoint.scala:415)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516)
at akka.actor.ActorCell.invoke(ActorCell.scala:487)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238)
at akka.dispatch.Mailbox.run(Mailbox.scala:220)
at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:393)
at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
I understand the exception message, but I don't understand why I am getting this the second time.
Also, why the collect never returns after completing in Spark?
I Googled every piece of information I have, but I had no luck finding a solution. Any help or hint would be greatly appreciated!
Thanks
This does appear to be a simple combination of Java in-memory object representations being inefficient combined with some apparent long-lived object references which cause some collections to fail to be garbage-collected in time for the new collect() call to overwrite the old one in-place.
I experimented with some options, and for my sample 256MB file that contains ~4M lines, I indeed reproduce your behavior where collect is fine the first time, but OOMs the second time, when using SPARK_MEM=1g. I then set SPARK_MEM=4g instead, and then I'm able to ctrl+c and re-run test <- collect(lines) as many times as I want.
For one thing, even if references didn't leak, note that after the first time you ran test <- collect(lines), the variable test is holding that gigantic array of lines, and the second time you call it, the collect(lines) executes before finally being assigned to the test variable and thus in any straightforward instruction-ordering, there's no way to garbage-collect the old contents of test. This means the second run will make the SparkRBackend process hold two copies of the entire collection at the same time, leading to the OOM you saw.
To diagnose, on the master I started SparkR and first ran
dhuo#dhuo-sparkr-m:~$ jps | grep SparkRBackend
8709 SparkRBackend
I also checked top and it was using around 22MB of memory. I fetched a heap profile with jmap:
jmap -heap:format=b 8709
mv heap.bin heap0.bin
Then I ran the first round of test <- collect(lines) at which point running top showed it using ~1.7g of RES memory. I grabbed another heap dump. Finally, I also tried test <- {} to get rid of references to allow garbage-collection. After doing this, and printing out test and showing it to be empty, I grabbed another heap dump and noticed RES still showed 1.7g. I used jhat heap0.bin to analyze the original heap dump, and got:
Heap Histogram
All Classes (excluding platform)
Class Instance Count Total Size
class [B 25126 14174163
class [C 19183 1576884
class [<other> 11841 1067424
class [Lscala.concurrent.forkjoin.ForkJoinTask; 16 1048832
class [I 1524 769384
...
After running collect, I had:
Heap Histogram
All Classes (excluding platform)
Class Instance Count Total Size
class [C 2784858 579458804
class [B 27768 70519801
class java.lang.String 2782732 44523712
class [Ljava.lang.Object; 2567 22380840
class [I 1538 8460152
class [Lscala.concurrent.forkjoin.ForkJoinTask; 27 1769904
Even after I nulled out test, it remained about the same. This shows us 2784858 instances of char[], for a total size of 579MB, and also 2782732 instances of String, presumably holding those char[]'s above it. I followed the reference graph all the way up, and got something like
char[] -> String -> String[] -> ... -> class scala.collection.mutable.DefaultEntry -> class [Lscala.collection.mutable.HashEntry; -> class scala.collection.mutable.HashMap -> class edu.berkeley.cs.amplab.sparkr.JVMObjectTracker$ -> java.util.Vector#0x785b48cd8 (36 bytes) -> sun.misc.Launcher$AppClassLoader#0x7855c31a8 (138 bytes)
And then AppClassLoader had something like thousands of inbound references. So somewhere along that chain something should've been removing their reference but failing to do so, causing the entire collected array to sit in memory while we try to fetch a second copy of it.
Finally, to answer your question about hanging after the collect, it appears it has to do with the data not fitting in the R process's memory; here's a thread related to that issue: https://www.mail-archive.com/user#spark.apache.org/msg29155.html
I confirmed that using a smaller file with only a handful of lines, and then running collect indeed does not hang.
I am trying to scan for possible SNPs and indels by aligning scaffolds to subsequences from a reference genome. (the raw reads are not available). I am using R/bioconductor and the `pairwiseAlignment function from the Biostrings package.
This was working fine for smaller scaffolds, but failed when I tried to align as 56kbp scaffold with the error message:
Error in QualityScaledXStringSet.pairwiseAlignment(pattern = pattern,
: cannot allocate memory block of size 17179869183.7 Gb
I am not sure if this is a bug or not ? ; I was under the impression that the Needleman-Wunsch algorithm used by pairwiseAlignment is an O(n*m) which I thought would imply the computational demand to be on the order of 3.1E9 operations (56K * 56k ~= 3.1E9). It seems the Needleman-Wunsch similarity matrix should as well take up on the order of 3.1 gigs of memory as well. Not sure if I'm not remembering big-o notation correctly or that is actually the memory overhead that would be needed to build the alignment given the overhead of the R scripting environment.
Does anybody have suggestions for a better alignment algorithm to use for aligning longer sequences? An initial alignment was already done using BLAST to find the region of the reference genome to align. I am not entirely confident BLAST's reliability for correctly placing indels and I have not yet been able to find an api as good as that provided by biostrings for parsing the raw BLAST alignments.
By the way, here is a code snippet that replicates the problem:
library("Biostrings")
scaffold_set = read.DNAStringSet(scaffold_file_name) #scaffold_set is a DNAStringSet instance
scafseq = scaffold_set[[scaffold_name]] #scaf_seq is a "DNAString" instance
genome = read.DNAStringSet(genome_file_name)[[1]] #genome is a "DNAString" instance
#qstart, qend, substart, subend are all from intial BLAST alignment step
scaf_sub = subseq(scafseq, start=qstart, end=qend) #56170-letter "DNAString" instance
genomic_sub = subseq(genome, start=substart, end=subend) #56168-letter "DNAString" instance
curalign = pairwiseAlignment(pattern = scaf_sub, subject = genomic_sub)
#that last line gives the error:
#Error in .Call2("XStringSet_align_pairwiseAlignment", pattern, subject, :
#cannot allocate memory block of size 17179869182.9 Gb
The error does not happen with shorter alignments (hundreds of bases).
I have not yet found the length cutoff where the error starts happening
So I use Clustal as an alignment tool. Not sure about the specific performance, but it has never given me issues when doing multiple sequence alignments of large quantity. Here is a script that runs a whole directory of .fasta files and aligns them. You can modify the flags on the system call to suit your input/output needs. Just look at the clustal documentation. This is in Perl, I don't use R too much for alignments. You need to edit the executable path in the script to match where clustal is on your computer.
#!/usr/bin/perl
use warnings;
print "Please type the list file name of protein fasta files to align (end the directory path with a / or this will fail!): ";
$directory = <STDIN>;
chomp $directory;
opendir (DIR,$directory) or die $!;
my #file = readdir DIR;
closedir DIR;
my $add="_align.fasta";
foreach $file (#file) {
my $infile = "$directory$file";
(my $fileprefix = $infile) =~ s/\.[^.]+$//;
my $outfile="$fileprefix$add";
system "/Users/Wes/Desktop/eggNOG_files/clustalw-2.1-macosx/clustalw2 -INFILE=$infile -OUTFILE=$outfile -OUTPUT=FASTA -tree";
}