English | 简体中文
LiBai is a large-scale open-source model training toolbox based on OneFlow. The main branch works with OneFlow 0.7.0.
HighlightsSupport a collection of parallel training components
LiBai provides multiple parallelisms such as Data Parallelism, Tensor Parallelism, and Pipeline Parallelism. It's also extensible for other new parallelisms.
Varied training techniques
LiBai provides many out-of-the-box training techniques such as Distributed Training, Mixed Precision Training, Activation Checkpointing, Recomputation, Gradient Accumulation, and Zero Redundancy Optimizer(ZeRO).
Support for both CV and NLP tasks
LiBai has predefined data process for both CV and NLP datasets such as CIFAR, ImageNet, and BERT Dataset.
Easy to use
LiBai's components are designed to be modular for easier usage as follows:
High Efficiency
See Installation instructions.
See Quick Run for the basic usage of LiBai.
See LiBai's documentation for full API documentation and tutorials.
Beta 0.3.0 was released in 03/11/2024, the general changes in 0.3.0 version are as follows:
Features:
New Supported Models:
New Mock Models:
See changelog for details and release history.
We appreciate all contributions to improve LiBai. See CONTRIBUTING for the contributing guideline.
This project is released under the Apache 2.0 license.
If you find this project useful for your research, consider cite:
@misc{of2021libai,
author = {Xingyu Liao and Peng Cheng and Tianhe Ren and Depeng Liang and
Kai Dang and Yi Wang and Xiaoyu Xu},
title = {LiBai},
howpublished = {\url{https://github.com/Oneflow-Inc/libai}},
year = {2021}
}
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4
