#MXNet

GluonCV offers a comprehensive set of state-of-the-art deep learning models for a variety of computer vision tasks such as image classification and object detection. It facilitates quick prototyping for engineers, researchers, and students by supporting both PyTorch and MXNet frameworks. Key features include scripts for reproducing research results, an extensive collection of pre-trained models, and user-friendly APIs to ease implementation. Ideal for both research and production, GluonCV is well-supported by the community and integrates seamlessly with AutoGluon for enhanced model deployment.

Multi Model Server is a versatile platform for serving deep learning models across different ML/DL frameworks. It includes server CLI, Docker images, and HTTP endpoints for inference in CPU and GPU setups. Features include rapid model deployment, API-based management, and streamlined model archiving. Enhanced security and deployment options are provided through Docker applications. Comprehensive documentation and community support are available.

Sockeye is an open-source framework for Neural Machine Translation using PyTorch. It supports distributed training and optimized inference, utilized by applications like Amazon Translate. The latest 3.1.x version focuses solely on PyTorch, eliminating MXNet support but remains backward-compatible. Although no new features will be added due to its maintenance mode, comprehensive documentation for users and developers is available. Sockeye has significantly contributed to academic and industrial research, evidenced by numerous citations.