gpu-training-acceleration

What problem does it solve?

Optimize PyTorch training speed and reduce GPU memory usage on CUDA GPUs by providing actionable, config-gated acceleration patterns, mixed precision guidance, fused optimizer kernels, Triton kernel fusion, and monitoring practices. It helps diagnose low GPU utilization, avoid out-of-memory failures, and safely adopt new compiler or kernel features without destabilizing training runs.

Core Features & Use Cases

• Config-gated acceleration: Patterns to gate features like torch.compile, fused optimizers, and gradient checkpointing behind configuration so they can be toggled and logged.
• Precision and kernel guidance: Advice for TF32, bf16/fp16 mixed precision, cuDNN autotuning, and when to prefer fused kernels or Triton implementations.
• Failure handling and telemetry: Safe defaults, try/except fallbacks for torch.compile, and recommendations to always log acceleration state for reproducibility and debugging.
• Memory and dataflow patterns: Strategies for latent-space training, contiguous memory enforcement, empty_cache placement, and NVCC build flags for custom CUDA extensions.
• Use Case: Speed up transformer or generative model training on multi-GPU clusters by enabling TF32, using bf16 mixed precision where safe, compiling stable decoder submodules, and switching to fused AdamW for optimizer speedups.

Quick Start

Run a short fastrun with TF32 enabled, mixed precision set to bf16, fused optimizer turned on, and compile only stable-shape decoder submodules to validate throughput and memory before full-scale training.