scientific-deep-learning

What problem does it solve?

Unify deep learning workflows for scientific data by integrating architecture design, training, evaluation, and deployment into a cohesive toolkit that boosts reproducibility and scalability.

Core Features & Use Cases

• Architecture Design & Transfer Learning: Plan and fine-tune NN architectures (via PyTorch Lightning, Hugging Face Transformers, timm) for scientific domains.
• Distributed Training & Evaluation: Enable multi-GPU/multi-node training and robust evaluation with modern metrics.
• Hyperparameter Optimization & Deployment: Automate hyperparameter search (Optuna/Ray Tune) and export models (ONNX/TorchScript) with model cards.
• Use Case: Rapidly prototype models for protein structure prediction or genomic data analysis and deploy in scalable pipelines.

Quick Start

Define and execute a reproducible end-to-end deep learning workflow for a scientific dataset using PyTorch Lightning and Transformers.