Caffe2

Caffe2 is an open-source deep learning framework that originated from Facebook AI Research (FAIR). It was designed to be fast, lightweight, and portable, making it suitable for production deployment on servers and mobile devices. Building on ideas from the Caffe framework, Caffe2 emphasized modularity, a clean C++ core, and simple APIs for model construction and deployment across CPU, GPU, and mobile accelerators.

Caffe2 was introduced by Facebook around 2017 as a successor aimed at scalable production workflows and mobile-minded inference. It offered a production-oriented workflow, including a stable runtime, graph execution, and tooling for exporting models to mobile platforms (iOS and Android) and servers. In 2018, Facebook announced a strategic plan to merge Caffe2 with PyTorch, aiming to combine PyTorch’s flexible, Pythonic development experience with Caffe2’s deployment capabilities.

Merge and status: Over 2018–2019, development efforts were consolidated into PyTorch. The Caffe2 project was largely folded into PyTorch, and subsequent PyTorch releases incorporated many of Caffe2’s production-oriented features. Today, Caffe2 is generally described as archived or deprecated; PyTorch serves as the primary framework, with its production and mobile support informed by the Caffe2 lineage.

Features: Key features included a modular operator library, a graph-based execution engine, cross-platform support for CPUs and GPUs, and tools for exporting models to production environments. Caffe2 supported both Python and C++ interfaces and was interoperable with ONNX, enabling model exchange with other frameworks.

Legacy: The Caffe2 project contributed to PyTorch’s split between research-focused development and production deployment, influencing mobile and edge deployment capabilities within PyTorch.