scientific-deep-learning

Build end-to-end deep learning workflows for scientific data by unifying architecture design, training, evaluation, and deployment. It uses PyTorch Lightning and Hugging Face Transformers to enable reproducible, scalable experiments for domain-specific research pipelines.

Yes, you can apply PyTorch Lightning and Hugging Face Transformers for protein structure prediction. The workflow supports fine-tuning neural network architectures and transfer learning specifically for scientific domains like genomics and protein modeling.

Automate hyperparameter tuning for deep learning models by running automated search processes using Optuna or Ray Tune. This workflow integrates these tools to optimize neural network parameters and boost the reproducibility of scientific experiments.

Export trained scientific models to ONNX or TorchScript by using the deployment features included in the workflow. This process generates model cards alongside the exported files to ensure traceability and reproducibility for scalable research pipelines.

Yes, this deep learning workflow supports multi-GPU distributed training. It enables multi-GPU and multi-node training using PyTorch Lightning to ensure scalable experiments across large scientific datasets.