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Unverified Commit 5d150856 authored by Sergey Edunov's avatar Sergey Edunov Committed by GitHub
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Merge pull request #279 from pytorch/oss-master 

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README.md
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...@@ -60,7 +60,8 @@ We provide the following pre-trained models and pre-processed, binarized test se ...@@ -60,7 +60,8 @@ We provide the following pre-trained models and pre-processed, binarized test se
Description | Dataset | Model | Test set(s) Description | Dataset | Model | Test set(s)
---|---|---|--- ---|---|---|---
Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/models/wmt14.v2.en-fr.fconv-py.tar.bz2) | newstest2014: <br> [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/data/wmt14.v2.en-fr.newstest2014.tar.bz2) <br> newstest2012/2013: <br> [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/data/wmt14.v2.en-fr.ntst1213.tar.bz2) Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/models/wmt14.v2.en-fr.fconv-py.tar.bz2) | newstest2014: <br> [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/data/wmt14.v2.en-fr.newstest2014.tar.bz2) <br> newstest2012/2013: <br> [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/data/wmt14.v2.en-fr.ntst1213.tar.bz2)
Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-German](https://nlp.stanford.edu/projects/nmt) | [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/models/wmt14.v2.en-de.fconv-py.tar.bz2) | newstest2014: <br> [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/data/wmt14.v2.en-de.newstest2014.tar.bz2) Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-German](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/models/wmt14.en-de.fconv-py.tar.bz2) | newstest2014: <br> [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/data/wmt14.en-de.newstest2014.tar.bz2)
Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT17 English-German](http://statmt.org/wmt17/translation-task.html#Download) | [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/models/wmt17.v2.en-de.fconv-py.tar.bz2) | newstest2014: <br> [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/data/wmt17.v2.en-de.newstest2014.tar.bz2)
Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/models/wmt14.en-fr.joined-dict.transformer.tar.bz2) | newstest2014 (shared vocab): <br> [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/data/wmt14.en-fr.joined-dict.newstest2014.tar.bz2) Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/models/wmt14.en-fr.joined-dict.transformer.tar.bz2) | newstest2014 (shared vocab): <br> [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/data/wmt14.en-fr.joined-dict.newstest2014.tar.bz2)
Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) | [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/models/wmt16.en-de.joined-dict.transformer.tar.bz2) | newstest2014 (shared vocab): <br> [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/data/wmt16.en-de.joined-dict.newstest2014.tar.bz2) Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) | [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/models/wmt16.en-de.joined-dict.transformer.tar.bz2) | newstest2014 (shared vocab): <br> [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/data/wmt16.en-de.joined-dict.newstest2014.tar.bz2)
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docs/getting_started.rst
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...@@ -193,10 +193,12 @@ Alternatively you can manually start one process per GPU: ...@@ -193,10 +193,12 @@ Alternatively you can manually start one process per GPU:
> DATA=... # path to the preprocessed dataset, must be visible from all nodes > DATA=... # path to the preprocessed dataset, must be visible from all nodes
> HOST_PORT=master.example.com:9218 # one of the hosts used by the job > HOST_PORT=master.example.com:9218 # one of the hosts used by the job
> RANK=... # the rank of this process, from 0 to 127 in case of 128 GPUs > RANK=... # the rank of this process, from 0 to 127 in case of 128 GPUs
> LOCAL_RANK=... # the local rank of this process, from 0 to 7 in case of 8 GPUs per machine
> python train.py $DATA \ > python train.py $DATA \
--distributed-world-size 128 \ --distributed-world-size 128 \
--distributed-init-method 'tcp://$HOST_PORT' \ --distributed-init-method 'tcp://$HOST_PORT' \
--distributed-rank $RANK \ --distributed-rank $RANK \
--device-id $LOCAL_RANK \
--force-anneal 50 --lr-scheduler fixed --max-epoch 55 \ --force-anneal 50 --lr-scheduler fixed --max-epoch 55 \
--arch fconv_wmt_en_fr --optimizer nag --lr 0.1,4 --max-tokens 3000 \ --arch fconv_wmt_en_fr --optimizer nag --lr 0.1,4 --max-tokens 3000 \
--clip-norm 0.1 --dropout 0.1 --criterion label_smoothed_cross_entropy \ --clip-norm 0.1 --dropout 0.1 --criterion label_smoothed_cross_entropy \
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