distributed-training-pytorch-lightning

Topics in the source

• PyTorch Lightning - High-Level Training Framework
• Quick start
• Common workflows
• Workflow 1: From PyTorch to Lightning
• Workflow 2: Validation and testing
• Workflow 3: Distributed training (DDP)
• Workflow 4: Callbacks for monitoring
• Workflow 5: Learning rate scheduling

PyTorch Lightning organizes PyTorch code to eliminate boilerplate while maintaining flexibility. Installation: Convert PyTorch to Lightning (3 steps): That's it! Trainer handles: GPU/TPU/CPU switching Distributed training (DDP, FSDP, DeepSpeed) Mixed precision (FP16, BF16) Gradient accumulation Checkpointing Logging Progress bars Original PyTorch code: Lightning version: Benefits: 40+ lines → 15 lines, no device management, automatic distributed Automatic features: Validation runs every epoch by default Metrics logged to TensorBoard Best model checkpointing based on valloss Launch: No changes needed: Automatic data distribution Gradient synchronization Multi-node support (just set numnodes=2) Result: Auto-saves best 3 models Stops early if no improvement for 5 epochs Logs learning rate to TensorBoard Use PyTorch Lightning when: Want clean, organized code Need production-ready training loops Switching between single GPU, multi-GPU, TPU Want built-in callbacks and logging Team collaboration (standardized structure) Key advantages: Organized: Separates research code from engineering Automatic: DDP, FSDP, DeepSpeed with 1 line Callbacks: Modular training extensions Reproducible: Less b

This is an excerpt. Refer to the complete source and its current requirements.