Overview

• Quickstart PyTorch Training Pipeline – Configure experiment YAML files, run model training via scripts/train.py, track metrics, and evaluate checkpoints.
• Adding Custom Neural Blocks, Losses & Unit Tests – Extend the 3-tier modular architecture with atomic layers, paper-cited loss functions, and pytest test suites.
• Platform Integrations – Connect experiment runs to Weights & Biases, Hugging Face Hub, Kaggle, and Roboflow.
• Hot-Reloaded GPU Docker Development – Set up containerized training with NVIDIA GPU acceleration and hot-reloaded host volume mounts.