ParaGen is a PyTorch deep learning framework for parallel sequence generation. Apart from sequence generation, ParaGen also enhances various NLP tasks, including sequence-level classification, extraction and generation.
Requirements and Installation
- Install third-party dependent package:
apt-get install libopenmpi-dev,libssl-dev,openssh-server
- To install ParaGen from source:
cd ParaGen pip install -e .
- For distributed training, you need to make sure
horovodhas been installed.
# require CMake to install horovod. (https://cmake.org/install/) pip install horovod
- Install lightseq to faster train:
pip install lightseq
ParaGen, it would be helpful to overview how
ParaGen is designed as a
task-oriented framework, where
task is regarded as the core of all the codes. A specific task selects all the components for support itself, such as model architectures, training strategies, dataset, and data processing. Any component within
ParaGen can be customized, while the existing modules and methods are used as a plug-in library.
As tasks are considered as the core of
ParaGen, it works with various
modes, such as
serve. Tasks act differently under different modes, by reorganizing the components without code modification.
Please refer to examples for detailed instructions.
ParaGen Usage and Contribution
We welcome any experimental algorithms on ParaGen.
- Install ParaGen;
- Create your own paragen-plugin libraries under
- Experiment your own algorithms;
- Write a reproducible shell script;
- Create a merge request and assign reviewers to any of us.