Glueon

Gluon, sometimes misspelled glueon, is a high-level interface for building deep neural networks within the Apache MXNet ecosystem. It was introduced to combine the fast prototyping of imperative programming with the scalability and performance of symbolic graphs.

The core ideas are Blocks, HybridBlock, and a modular neural network library. Networks are composed from blocks in a Pythonic, dynamic fashion. HybridBlock can be hybridized to produce a static computation graph, enabling optimizations and efficient deployment while preserving the ability to run interactively during development.

Training and deployment: Gluon provides a Trainer, loss functions, metrics, and data loaders. It supports CPU and GPU execution, distributed training, and mixed-precision arithmetic. Models can be serialized to parameter files and, when hybridized, exported as a standalone graph for production use.

Ecosystem: Gluon underpins MXNet's model zoo and is used with related libraries such as GluonCV for computer vision and GluonNLP for natural language processing. The API emphasizes readability and ease of use, with a gradual path from imperative coding to production-ready graphs.

Reception and status: Gluon gained popularity for rapid experimentation and its unified interface across devices. Since the rise of PyTorch and TensorFlow, its prominence has declined, but it remains officially supported within MXNet and used in certain projects and research.