dl-engineering

What problem does it solve?

Deep Learning engineering in practice requires robust training loops, careful memory management, and reliable debugging workflows. This skill consolidates PyTorch and Lightning guidance to streamline neural network development from setup to deployment.

Core Features & Use Cases

• Training loop recipes for PyTorch and PyTorch Lightning, including forward/backward passes, optimization, and logging.
• Memory management and mixed-precision strategies (BF16/FP16) for scalable training on single or multi-GPU setups.
• Checkpointing, reproducibility, and distributed training practices to keep experiments organized and reusable.

Quick Start

Install dependencies and run the example training script to begin a PyTorch-based DL workflow.