Optimize PyTorch model training

Execute battle-tested PyTorch training recipes across LLM, vision, diffusion, medical imaging domains

Starting model training quickly with expert-vetted defaults instead of tuning from scratch

• · domain type (LLM, vision, diffusion, etc.)
• · training config (lr, batch_size, epochs, etc.)
• · dataset

• · trained model checkpoint
• · training metrics (loss, accuracy, etc.)
• · validation results

• · torch
• · pytorch-lightning
• · transformers
• · domain-specific libraries

GPU available (NVIDIA/Metal); datasets prepared; hyperparams within sane ranges; memory sufficient for batch_size

NaN loss if learning rate too high; underfitting if epochs too few; overfitting if regularization insufficient; OOM if batch_size too large

• · Covers 8+ domains (LLM, vision, diffusion, medical, protein, spatial, genomics)
• · Battle-tested means multiple successful deployments
• · Recipes include checkpointing strategy