11-16, 16:00–16:30 (Asia/Hong_Kong), LT8
Language: English
With the rapid advancement in deep learning, models become super large and consume significant resources, making efficiency and simplicity more critical than ever. In this talk, we introduce PyTorch Lightning, a deep learning framework that emerges as a powerful tool that streamlines the process of building, training, and scaling models, allowing researchers and practitioners to focus on what truly matters: innovation.
We will begin with an overview of PyTorch Lightning, discussing the key benefits it offers over traditional PyTorch. We will explore how PyTorch Lightning abstracts away the boilerplate code associated with model training, making it easier to implement and experiment with complex models. Then, we walk through the process of migrating traditional PyTorch to PyTorch Lightning and setting up distributed training.
Henry is a data scientist with 4 years of experience in Python. With broad exposure to classical and modern statistical approaches, he has been developing solutions for HVAC energy optimization, object detection and classification, predictive maintenance, physics-guided machine learning, and survival analysis. As a member of LIGO Scientific Collaboration, Henry has also contributed to academic research in the field of black-hole physics under the Bayesian framework.