PyTorch is our preferred framework for AI projects. Its dynamic computation graph and Python-friendly API make it easy to experiment and debug—ideal for fast-moving workflows.
When combined with PyTorch Lightning, routine tasks like training loops, logging, and distributed training are handled with minimal code. Lightning also scales well from prototypes to large models across multiple GPUs or TPUs.
Hydra further simplifies configuration management. It supports structured experiment tracking and flexible hyperparameter tuning using YAML files.
For deployment, PyTorch’s support for ONNX is a major advantage. It allows seamless model export to production environments, including edge devices and platforms outside the PyTorch ecosystem.
Together, PyTorch, Lightning, and Hydra form a robust and flexible AI development stack—from early experimentation to large-scale deployment.