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Vincent Nguyen
@vince62s
how do people do with new V100 then ?
Jean Senellart
@jsenellart
I am trying to narrow down - it seems to be coming from incompatibility with some other library.
Vincent Nguyen
@vince62s
I guess you tried this export TORCH_NVCC_FLAGS="-D__CUDA_NO_HALF_OPERATORS__"right ?
Jean Senellart
@jsenellart
yes - it was not the point
Ratish Puduppully
@ratishsp
Hi, how do we disable dropout during testing?
Guillaume Klein
@guillaumekln
Hello, it is disabled automatically.
Ratish Puduppully
@ratishsp
Thanks @guillaumekln.
chiting765
@chiting765
Hi~ I have a question about joiner annotate. There is a -tok_joiner_annotate option at learn_bpe.lua and a -joiner_annotate option at tokenize.lua. Do I need to turn on both to have the joiner in the tokenized files?
Jean Senellart
@jsenellart
@chiting765 - I think the one in learn_bpe is not important. It is just coming with all tokenization options
it does not change the bpe model
so the only one important is the one in tokenize.lua
chiting765
@chiting765
@jsenellart OK~ Thanks! The first time I tired the bpe model for EN to ES translation, I got target word like "Kilogram o" which supposed to be "Kilogramo". I think it is because I didn't add the joiner annotator. I am training a new one with the joiner annotator and hopefully this time I will get the word correctly
chiting765
@chiting765
Hi~ So I tried bpe model with joiner annotator, it did give me correct target words like "Kilogramo". The validation ppl is also lowered from 2.5 to 2.2. However, the BLUE score is lowered too, I am not sure why
BLEU score
Jean Senellart
@jsenellart
is your bleu score calculated on tokenized or detokenized?
chiting765
@chiting765
detokenized
I will try to calculate the BLEU of space tokenized target file
chiting765
@chiting765
I calculated the BLEU of the space tokenized candidate file against the space tokenized reference file. The BLEU did improve from 54 to 55. However, without the BPE, the best BLEU I got for the same file is 65
I wonder maybe I should try a different BPE model other the aggressive one?
What kind of BPE model do you use for EN to ES translation or to similar languages?
Jean Senellart
@jsenellart
your result is unlikely except if you have a very small vocabulary/narrow domain. A BLEU of 65 is suspicious
there are almost no cases where we found BPE hurting the translation
chiting765
@chiting765
It is a pretty narrow domain, the vocabulary is not very small, it has about 40K - 50K vocabulary in total without bpe
and the BLEU score is for the whole test data
zeng
@xjtu-zeng
Hi everyone. I have a question about the StdRNNDecoder, why the rnn and attn can be seperated? The next hidden state needs the context computed by attn. I am confused
@jsenellart
Vincent Nguyen
@vince62s
Just in case one did not notice, but there is a huge performance difference between Cuda 8.0.61 and 8.0.61 patch 2 for the best (I saw about 25% difference)
Jean Senellart
@jsenellart
Registration for first OpenNMT workshop is open! Check here: http://workshop-paris-2018.opennmt.net :)...
ykasimov
@ykasimov
Hi. There is no support for copy attention yet in Python version, right?
Konstantin Glushak
@gsoul
Which of python versions did you mean?
ykasimov
@ykasimov
do you mean python version?
Konstantin Glushak
@gsoul
no, OpenNMT version: OpenNMT-py or OpenNMT-tf?
ykasimov
@ykasimov
ah, sorry. OpenNMT-py. Forgot that there is tf version
Konstantin Glushak
@gsoul
I’m not sure, but perhaps it’s better to ask this question in OpenNMT-py channel? https://gitter.im/OpenNMT/OpenNMT-py
ykasimov
@ykasimov
Thanks.
Konstantin Glushak
@gsoul
np
Ratish Puduppully
@ratishsp

In GlobalAttention.lua, we have the following lines of code
local softmaxAttn = nn.SoftMax() softmaxAttn.name = 'softmaxAttn'

Why don't we set softmaxAttn as an output of nn.gModule like return nn.gModule(inputs, {contextOutput, softmaxAttn(attn)})

Jean Senellart
@jsenellart
what for?
it is not used later
but we name it, so that we can find it by traversing the graph
Ratish Puduppully
@ratishsp
Ok. I was trying to understand the design difference between the two: when should we set it as an output of nn.gModule and when should we not.
Jean Senellart
@jsenellart
the gModule is very powerful but also very complicated - you can not easily tweak it
Ratish Puduppully
@ratishsp
Ok.
I guess if it is output of nn.gModule, then we should manage its backpropagation with gradients too.
Jean Senellart
@jsenellart
yes exactly - for the attention, we are just accessing for visualization of the state
Ratish Puduppully
@ratishsp
Thanks @jsenellart for the details.
sathiyan7987
@sathiyan7987
guys i need AI related project for my final project can you please help me
stribizhev
@stribizhev
Hi, I am using OpenNMT0.9 Lua version on an AWS server with Tesla K80 GPU and Ubuntu 16.04 OS. I ran two trainings in the background and they seem to have been working fine (I used -log_file option, and checked if the log is growing), but once I ran a release model command, at first, I got a segmentation fault core dump message, and when I ran it the second time, the two trainings I mentioned exited. The error log says THCudaCheck FAIL file=/tmp/luarocks_cutorch-scm-1-1460/cutorch/lib/THC/generic/THCStorage.c line=32 error=39 : uncorrectable ECC error encountered /torch/install/bin/luajit: cuda runtime error (39) : uncorrectable ECC error encountered at /tmp/luarocks_cutorch-scm-1-1460/cutorch/lib/THC/generic/THCStorage.c:32. Do I have to be cautious with GPU usage? Is there any documentation/known ways to handle multiple processes using a GPU?
stribizhev
@stribizhev
Ok, I found http://opennmt.net/OpenNMT/issues/ page, and it seems I can try to disable the caching CUDA memory allocator, but won't it make trainings last much longer (as on CPU)? As for the reducing network size, if my corpus is 1,760K segments, aren't the default values OK ( word embeddings size: 500, structure: cell = LSTM; layers = 2; rnn_size = 500; dropout = 0.3)?
Ayushi Dalmia
@ayushidalmia
What does input_feed = true do during training ? I am not able to understand the architecture diagram if it is set to true.