deeplearning4j: Deadlock using ParagraphVector

Hi, trying to create a word embegging (I use ParagraphVector) with:

• 0.8.1-SNAPSHOT
• Ubuntu 16.04.2 LTS
• Hardware RAM 16 GB, Intel® Xeon® CPU E5-2620 v2 @ 2.10GHz
• File input contains 513316 lines with size of 873,5 MB. Training corpus is a string (7-bit ASCII) with lenght range of [10, 50 000] more or less.
• DefaultTokenizerFactory with custom MyPreProcessor

The logs is:

11:35:10.937 [Thread-11] INFO  o.d.m.s.SequenceVectors - Starting vocabulary building...
11:35:10.938 [Thread-11] DEBUG o.d.m.w.wordstore.VocabConstructor - Target vocab size before building: [0]
11:35:10.991 [Thread-11] DEBUG o.d.m.w.wordstore.VocabConstructor - Trying source iterator: [0]
11:35:10.991 [Thread-11] DEBUG o.d.m.w.wordstore.VocabConstructor - Target vocab size before building: [0]
11:35:56.217 [Thread-11] INFO  o.d.m.w.wordstore.VocabConstructor - Sequences checked: [100000]; Current vocabulary size: [385141]; Sequences/sec: 2208.58; Words/sec: 962757.81;
?11:58:56.372 [Thread-11] INFO  o.d.m.w.wordstore.VocabConstructor - Sequences checked: [200000]; Current vocabulary size: [588558]; Sequences/sec: 72.46; Words/sec: 26455.10;
11:59:41.377 [Thread-11] INFO  o.d.m.w.wordstore.VocabConstructor - Sequences checked: [300000]; Current vocabulary size: [667109]; Sequences/sec: 2221.98; Words/sec: 902804.93;
11:59:59.203 [Thread-11] DEBUG o.d.m.w.wordstore.VocabConstructor - Wating till all processes stop...
11:59:59.204 [Thread-11] DEBUG o.d.m.w.wordstore.VocabConstructor - Vocab size before truncation: [764577],  NumWords: [142093275], sequences parsed: [366918], counter: [142093265]
11:59:59.658 [Thread-11] DEBUG o.d.m.w.wordstore.VocabConstructor - Scavenger: Words before: 764577; Words after: 342429;
11:59:59.658 [Thread-11] DEBUG o.d.m.w.wordstore.VocabConstructor - Vocab size after truncation: [342429],  NumWords: [140970370], sequences parsed: [366918], counter: [142093265]
12:00:07.468 [Thread-11] INFO  o.d.m.w.wordstore.VocabConstructor - Sequences checked: [366918], Current vocabulary size: [342429]; Sequences/sec: [245.18];
12:00:07.486 [Thread-11] INFO  o.d.m.e.loader.WordVectorSerializer - Projected memory use for model: [391.88 MB]
12:00:07.806 [Thread-11] INFO  o.d.m.e.inmemory.InMemoryLookupTable - Initializing syn1...
12:00:08.038 [Thread-11] INFO  o.d.m.s.SequenceVectors - Building learning algorithms:
12:00:08.038 [Thread-11] INFO  o.d.m.s.SequenceVectors -           building ElementsLearningAlgorithm: [SkipGram]
12:00:08.042 [Thread-11] INFO  o.d.m.s.SequenceVectors - Starting learning process...
12:24:22.518 [VectorCalculationsThread 5] INFO  o.d.m.s.SequenceVectors - Epoch: [1]; Words vectorized so far: [43254792];  Lines vectorized so far: [100000]; Seq/sec: [68.76]; Words/sec: [29740.19]; learningRate: [0.01732922544852691]
13:05:27.656 [VectorCalculationsThread 10] INFO  o.d.m.s.SequenceVectors - Epoch: [1]; Words vectorized so far: [79385098];  Lines vectorized so far: [200000]; Seq/sec: [40.57]; Words/sec: [20253.57]; learningRate: [0.01092213763943002]
13:24:03.599 [VectorCalculationsThread 0] INFO  o.d.m.s.SequenceVectors - Epoch: [1]; Words vectorized so far: [111279196];  Lines vectorized so far: [300000]; Seq/sec: [89.61]; Words/sec: [22098.93]; learningRate: [0.005265719332773217]

then, nothing happend, the CPU usage is 0.3% and memory is 52% (-Xmx10000min java options).

I alredy used the same classes and hardware but the file input had 17984 rows and all worked well.

[MyPreProcessor]

public class MyPreprocessor implements TokenPreProcess {
    public MyPreprocessor() { }

    @Override
    public String preProcess(String token) {

        // Clean
        token = StringCleaning.stripPunct(token).toLowerCase();

        // Accents
        token = StringUtils.stripAccents(token);

        // Bad char (only alphanumeric)
        token = token.replaceAll("[^A-Za-z0-9 ]", "");

        // Check if token contains at least one alphabet
        if (!token.matches(".*[a-zA-Z]+.*")) return "";

        // Small "words"
        if (token.length() <= 1) return  "";

        return token;
    }
}