NeMo: a toolkit for building AI applications using Neural Modules
This toolkit addresses the problem of efficient and modular AI development for researchers and practitioners, though it is incremental as it builds on existing concepts of modular frameworks.
The paper introduces NeMo, a toolkit for building AI applications by enabling re-usability, abstraction, and composition of neural modules, with features like semantic correctness checking and support for distributed training and mixed precision on NVIDIA GPUs.
NeMo (Neural Modules) is a Python framework-agnostic toolkit for creating AI applications through re-usability, abstraction, and composition. NeMo is built around neural modules, conceptual blocks of neural networks that take typed inputs and produce typed outputs. Such modules typically represent data layers, encoders, decoders, language models, loss functions, or methods of combining activations. NeMo makes it easy to combine and re-use these building blocks while providing a level of semantic correctness checking via its neural type system. The toolkit comes with extendable collections of pre-built modules for automatic speech recognition and natural language processing. Furthermore, NeMo provides built-in support for distributed training and mixed precision on latest NVIDIA GPUs. NeMo is open-source https://github.com/NVIDIA/NeMo