#OneFlow

OneFlow is a versatile deep learning framework offering a PyTorch-like API for ease of programming and scalability in n-dimensional-parallel execution. Features such as the Global Tensor and Graph Compiler facilitate efficient model deployment. It supports Linux and Python versions 3.7 to 3.11 and is compatible with CUDA architectures 60 and above. Easily installable via Docker or pip, OneFlow adapts to diverse system configurations. Explore its capabilities in distributed learning with detailed documentation and a supportive community.

Discover efficient approaches to optimize algorithms with CUDA, featuring implementations ranging from PyTorch compilation to advanced techniques like FastAtomicAdd and UpsampleNearest2D. This project provides a thorough guide with comprehensive notes and empirical results, assisting in executing effective CUDA methods for better computational performance, with notable improvements in bandwidth and processing speed. Suitable for developers exploring complex insights and real-world applications in CUDA optimization across multiple platforms.

LiBai utilizes OneFlow to offer a versatile open-source toolbox designed for training large-scale models. It includes data, tensor, and pipeline parallelism features, supporting both CV and NLP tasks. LiBai is efficient in distributed and mixed-precision training. Its modular design and flexible configuration options make it ideal for constructing research projects. The latest version introduces mock transformer support and new model integrations, making it suitable for developers and researchers focused on streamlined AI model deployment.