OneFlow is a deep learning framework designed for user-friendly model development, scalable distributed training, and efficient inference.
OneFlow addresses the challenge of building and scaling deep learning models by providing a PyTorch-like API that developers can use to program models with familiar syntax. The framework introduces a Global Tensor abstraction that enables n-dimensional parallel execution across distributed systems, allowing models to scale efficiently. Additionally, OneFlow includes a Graph Compiler that optimizes models for acceleration and deployment, bridging the gap between research code and production inference.
Developers should choose OneFlow if they want PyTorch-compatible syntax while gaining native support for distributed training without extensive code refactoring. The framework suits projects that require scaling across multiple GPUs or nodes, as well as those needing optimized inference through graph compilation. The tool supports Linux with Python versions from 3.7 through 3.11 and requires CUDA architecture 60 or above with CUDA Toolkit 10.0 or later for GPU acceleration, though CPU-only installation is available. Installation is straightforward through pip for prebuilt releases, with source compilation also supported for customization.
The project maintains active development with stable releases available alongside nightly builds. Documentation is comprehensive, covering API reference, usage guides, and system design details. The framework provides official Docker images and conda environments to simplify setup, and includes troubleshooting resources for common compilation and runtime issues. A model zoo and benchmark suite are available to help developers evaluate performance and explore example implementations.
