Notes on versioning
0.9.2 (2019-09-04)
- Switch to Pytorch 1.2
- Pre/post processing on the translation server
- option to remove the FFN layer in AAN + AAN optimization (faster)
- Coverage loss (per Abisee paper 2017) implementation
- Video Captioning task: Thanks Dylan Flaute!
- Token batch at inference
- Small fixes and add-ons
0.9.1 (2019-06-13)
- New mechanism for MultiGPU training "1 batch producer / multi batch consumers" resulting in big memory saving when handling huge datasets
- New APEX AMP (mixed precision) API
- Option to overwrite shards when preprocessing
- Small fixes and add-ons
0.9.0 (2019-05-16)
- Faster vocab building when processing shards (no reloading)
- New dataweighting feature
- New dropout scheduler.
- Small fixes and add-ons
0.8.2 (2019-02-16)
- Update documentation and Library example
- Revamp args
- Bug fixes, save moving average in FP32
- Allow FP32 inference for FP16 models
0.8.1 (2019-02-12)
- Update documentation
- Random sampling scores fixes
- Bug fixes
0.8.0 (2019-02-09)
- Many fixes and code cleaning thanks @flauted, @guillaumekln
- Datasets code refactor (thanks @flauted) you need to r-preeprocess datasets
- FP16 Support: Experimental, using Apex, Checkpoints may break in future version.
- Continuous exponential moving average (thanks @francoishernandez, and Marian)
- Relative positions encoding (thanks @francoishernanndez, and Google T2T)
- Deprecate the old beam search, fast batched beam search supports all options
0.7.2 (2019-01-31)
- Many fixes and code cleaning thanks @bpopeters, @flauted, @guillaumekln
- Multilevel fields for better handling of text featuer embeddinggs.
0.7.1 (2019-01-24)
- Many fixes and code refactoring thanks @bpopeters, @flauted, @guillaumekln
- Random sampling thanks @daphnei
- Enable sharding for huge files at translation
0.7.0 (2019-01-02)
- Many fixes and code refactoring thanks @benopeters
- Migrated to Pytorch 1.0
0.6.0 (2018-11-28)
- Many fixes and code improvements
- New: Ability to load a yml config file. See examples in config folder.
0.5.0 (2018-10-24)
- Fixed advance n_best beam in translate_batch_fast
- Fixed remove valid set vocab from total vocab
- New: Ability to reset optimizer when using train_from
- New: create_vocabulary tool + fix when loading existing vocab.
0.4.1 (2018-10-11)
- Fixed preprocessing files names, cleaning intermediary files.
0.4.0 (2018-10-08)
-
Fixed Speech2Text training (thanks Yuntian)
-
Removed -max_shard_size, replaced by -shard_size = number of examples in a shard. Default value = 1M which works fine in most Text dataset cases. (will avoid Ram OOM in most cases)
0.3.0 (2018-09-27)
-
Now requires Pytorch 0.4.1
-
Multi-node Multi-GPU with Torch Distributed
New options are: -master_ip: ip address of the master node -master_port: port number of th emaster node -world_size = total number of processes to be run (total GPUs accross all nodes) -gpu_ranks = list of indices of processes accross all nodes
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gpuid is deprecated See examples in https://github.com/OpenNMT/OpenNMT-py/blob/master/docs/source/FAQ.md
-
Fixes to img2text now working
-
New sharding based on number of examples
-
Fixes to avoid 0.4.1 deprecated functions.
0.2.1 (2018-08-31)
- First compatibility steps with Pytorch 0.4.1 (non breaking)
- Fix TranslationServer (when various request try to load the same model at the same time)
- Fix StopIteration error (python 3.7)
- Ensemble at inference (thanks @Waino)
0.2 (2018-08-28)
- Compatibility fixes with Pytorch 0.4 / Torchtext 0.3
- Multi-GPU based on Torch Distributed
- Average Attention Network (AAN) for the Transformer (thanks @francoishernandez )
- New fast beam search (see -fast in translate.py) (thanks @guillaumekln)
- Sparse attention / sparsemax (thanks to @bpopeters)
- Refactoring of many parts of the code base:
- change from -epoch to -train_steps -valid_steps (see opts.py)
- reorg of the logic train => train_multi / train_single => trainer
- Many fixes / improvements in the translationserver (thanks @pltrdy @francoishernandez)
- fix BPTT