NEOPOLIS AKADEMY
Scalable AI Models with PyTorch Lightning
Deepening best practices for training and optimizing large-scale models with PyTorch Lightning. Covers code structuring, distributed training strategies, robust validation and hyperparameter tuning for reproducible model development.
View this course on Akademy ↗Enrolment and practical details are available on Neopolis Akademy.
What you will explore
Structuring code and Lightning modules for clarity and reusability.
Distributed training strategies and handling large-scale datasets.
Validation protocols, metrics and logging to monitor performance.
Optimization approaches, schedulers, regularization and hyperparameter search.
Integration of experiment-tracking tools for audit and training debugging.
STEP BY STEP
Course programme
01Scalable AI Models with PyTorch Lightning
Design and optimization of scalable models with PyTorch Lightning: LightningModule architecture, data handling with LightningDataModule, DataLoader creation, validation and optimizations such as quantization and pruning.
Explains PyTorch Lightning architecture and using LightningModule to define reusable models.
Demonstrates data structuring with LightningDataModule, creating DataLoaders and integrating validation and testing phases.
Covers scalability optimizations: dynamic quantization, comparing quantized model performance and model pruning techniques.
Explore this module on Akademy ↗Programme source: Neopolis Akademy. Original course page