YATT is a scalable, simple, efficient, and production ready training library.
YATT 是一个 scalable、简单、高效且可用于生产环境的训练库。
内部名:gcore
内部可访问:https://gcore.woa.com/docs
You can use Sphinx to build and view the documentation. It’s also fine to read the rst and md files in the docs folder directly.
你可以通过 sphinx build 文档,或者直接阅读 docs 文件夹下的 rst 和 md。
参考 numpy docstring。
readonly WDIR=$PWD
git clone https://github.com/Tencent/Wechat-YATT
git clone git@github.com:NVIDIA/Megatron-LM.git -b core_v0.13.1
pip3 install sphinx-autobuild myst_parser
PYTHONPATH="$PWD:../Megatron-LM:$PYTHONPATH" sphinx-autobuild --port 8080 --host ${__HOST_IP__} docs/source docs/build
# or
PYTHONPATH="$PWD:../Megatron-LM:../mbridge:$PYTHONPATH" sphinx-build -M html docs/source docs/build
python -m http.server -d docs/build/html/ --bind $__HOST_IP__ 8080
Uses yapf (Facebook style, column limit 100) + isort via pre-commit hooks:
pip install pre-commit
git config --global --unset core.hooksPath
pre-commit install
Manual format:
bash gcore-dev/fmt.sh
We have tested it internally at WXG using tlinux, and it’s also possible to set up a similar environment on Ubuntu. In the docker section of the documentation, we have provided a sample Dockerfile for your reference.
我们在 WXG 内部使用 tlinux 测试过,ubuntu 也可以搭建类似的环境。我们在文档的 docker 部分贴了供参考的 dockerfile。
# https://mirrors.tencent.com/#/private/generic2/detail?repo_name=wepsdl
bash tests/download.sh $your_rtx $token
bash tests/test.sh
Please refer to the examples section of the documentation. It is recommended to start with Math SFT and Math GRPO.
请查看文档 examples 部分,建议从 Math SFT 与 Math GRPO 开始。
@misc{wu2025wechatyattscalablesimpleefficient,
title={WeChat-YATT: A Scalable, Simple, Efficient, and Production Ready Training Library},
author={Junyu Wu and Weiming Chang and Xiaotao Liu and Guanyou He and Tingfeng Xian and Haoqiang Hong and Boqi Chen and Hongtao Tian and Tao Yang and Yunsheng Shi and Feng Lin and Ting Yao and Jiatao Xu},
year={2025},
eprint={2508.07970},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.07970},
}
Python
98.5%
Shell
1.5%
YATT is a scalable, simple, efficient, and production ready training library.
YATT 是一个 scalable、简单、高效且可用于生产环境的训练库。
内部名:gcore
内部可访问:https://gcore.woa.com/docs
You can use Sphinx to build and view the documentation. It’s also fine to read the rst and md files in the docs folder directly.
你可以通过 sphinx build 文档,或者直接阅读 docs 文件夹下的 rst 和 md。
参考 numpy docstring。
readonly WDIR=$PWD
git clone https://github.com/Tencent/Wechat-YATT
git clone git@github.com:NVIDIA/Megatron-LM.git -b core_v0.13.1
pip3 install sphinx-autobuild myst_parser
PYTHONPATH="$PWD:../Megatron-LM:$PYTHONPATH" sphinx-autobuild --port 8080 --host ${__HOST_IP__} docs/source docs/build
# or
PYTHONPATH="$PWD:../Megatron-LM:../mbridge:$PYTHONPATH" sphinx-build -M html docs/source docs/build
python -m http.server -d docs/build/html/ --bind $__HOST_IP__ 8080
Uses yapf (Facebook style, column limit 100) + isort via pre-commit hooks:
pip install pre-commit
git config --global --unset core.hooksPath
pre-commit install
Manual format:
bash gcore-dev/fmt.sh
We have tested it internally at WXG using tlinux, and it’s also possible to set up a similar environment on Ubuntu. In the docker section of the documentation, we have provided a sample Dockerfile for your reference.
我们在 WXG 内部使用 tlinux 测试过,ubuntu 也可以搭建类似的环境。我们在文档的 docker 部分贴了供参考的 dockerfile。
# https://mirrors.tencent.com/#/private/generic2/detail?repo_name=wepsdl
bash tests/download.sh $your_rtx $token
bash tests/test.sh
Please refer to the examples section of the documentation. It is recommended to start with Math SFT and Math GRPO.
请查看文档 examples 部分,建议从 Math SFT 与 Math GRPO 开始。
@misc{wu2025wechatyattscalablesimpleefficient,
title={WeChat-YATT: A Scalable, Simple, Efficient, and Production Ready Training Library},
author={Junyu Wu and Weiming Chang and Xiaotao Liu and Guanyou He and Tingfeng Xian and Haoqiang Hong and Boqi Chen and Hongtao Tian and Tao Yang and Yunsheng Shi and Feng Lin and Ting Yao and Jiatao Xu},
year={2025},
eprint={2508.07970},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.07970},
}
Python
98.5%
Shell
1.5%