if you want fast decoding with more than 16 threads, use Moses2. http://www.statmt.org/moses/?n=Site.Moses2
Hieu Hoang http://moses-smt.org/ On 11 December 2017 at 09:20, liling tan <alvati...@gmail.com> wrote: > Dear Moses community/developers, > > I have a question on how to handle large models created using moses. > > I've a vanilla phrase-based model with > > - PhraseDictionary num-features=4 input-factor=0 output-factor=0 > - LexicalReordering num-features=6 input-factor=0 output-factor=0 > - KENLM order=5 factor=0 > > The size of the model is: > > - compressed phrase table is 5.4GB, > - compressed reordering table is 1.9GB and > - quantized LM is 600MB > > > I'm running on a single 56 cores machine with 256GB RAM. Whenever I'm > decoding I use -threads 56 parameter. > > It's takes really long to load the table and after loading, it breaks > inconsistently at different lines when decoding, I notice that the RAM goes > into swap before it breaks. > > I've tried compact phrased table and get a > > - 3.2GB .minphr > - 1.5GV .minlexr > > And the same kind of random breakage happens when RAM goes into swap after > loading the phrase-table. > > Strangely, it still manage to decode ~500K sentences before it breaks. > > Then I've tried with ondisk phrasetable and it's around 37GB uncompressed. > Using the ondisk PT didn't cause breakage but the decoding time is > significantly increased, now it can only decode 15K sentences in an hour. > > The setup is a little different from normal where we have the > train/dev/test split. Currently, my task is to decode the train set. I've > tried filtering the table with the trainset with > filter-model-given-input.pl but the size of the compressed table didn't > really decrease much. > > The entire training set is made up of 5M sentence pairs and it's taking 3+ > days just to decode ~1.5M sentences with ondisk PT. > > > My questions are: > > - Are there best practices with regards to deploying large Moses models? > - Why does the 5+GB phrase table take up > 250GB RAM when decoding? > - How else should I filter/compress the phrase table? > - Is it normal to decode only ~500K sentence a day given the machine > specs and the model size? > > I understand that I could split the train set up into two and train 2 > models then cross-decode but if the training size is 10M sentence pairs, > we'll face the same issues. > > Thank you for reading the long post and thank you in advances for any > answers, discussions and enlightenment on this issue =) > > Regards, > LIling > > _______________________________________________ > Moses-support mailing list > Moses-support@mit.edu > http://mailman.mit.edu/mailman/listinfo/moses-support > >
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