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NeMo: a toolkit for building AI applications using Neural Modules

Authors :
Kuchaiev, Oleksii
Li, Jason
Nguyen, Huyen
Hrinchuk, Oleksii
Leary, Ryan
Ginsburg, Boris
Kriman, Samuel
Beliaev, Stanislav
Lavrukhin, Vitaly
Cook, Jack
Castonguay, Patrice
Popova, Mariya
Huang, Jocelyn
Cohen, Jonathan M.
Publication Year :
2019

Abstract

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<br />Comment: 6 pages plus references

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.1909.09577
Document Type :
Working Paper