PyTorch is our framework of choice for AI projects, offering unmatched flexibility with its dynamic computation graph and intuitive Python API. It makes experimenting and debugging seamless, which is essential for rapidly evolving AI workflows.
Paired with PyTorch Lightning, repetitive tasks like training loops, logging, and distributed training become effortless. Lightning's scalability ensures smooth transitions from prototypes to large-scale models, even on multi-GPU or TPU setups. Adding Hydra to the mix simplifies configuration management, enabling dynamic hyperparameter tuning and organized experiments with YAML-based configurations.
For deployment, PyTorch's ONNX support is key. Exporting models to ONNX makes it easy to integrate them into production environments, including edge devices and non-PyTorch ecosystems.
The combination of PyTorch, Lightning, and Hydra creates a streamlined, scalable, and production-ready AI workflow that keeps development fast, flexible, and robust.