KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and Quantization
arXiv:2011.14691
Abstract
In recent years, the growing size of neural networks has led to a vast amount of research concerning compression techniques to mitigate the drawbacks of such large sizes. Most of these research works can be categorized into three broad families : Knowledge Distillation, Pruning, and Quantization. While there has been steady research in this domain, adoption and commercial usage of the proposed techniques has not quite progressed at the rate. We present KD-Lib, an open-source PyTorch based library, which contains state-of-the-art modular implementations of algorithms from the three families on top of multiple abstraction layers. KD-Lib is model and algorithm-agnostic, with extended support for hyperparameter tuning using Optuna and Tensorboard for logging and monitoring. The library can be found at - https://github.com/SforAiDl/KD_Lib.
References in corpus (5)
- Distilling the Knowledge in a Neural Network
- Distilling Task-Specific Knowledge from BERT into Simple Neural Networks
- Deep Model Compression: Distilling Knowledge from Noisy Teachers
- Knowledge Distillation via Route Constrained Optimization
- Preparing Lessons: Improve Knowledge Distillation with Better Supervision