Accelerate Distributed Train

The problem

Rewriting PyTorch training loops for multi-GPU support is error-prone and tedious. Manually managing device placement, gradient synchronization, and mixed precision often leads to boilerplate-heavy code that is difficult to scale or port between different hardware setups.

What it does

• Instruments standard PyTorch scripts with the Hugging Face Accelerate wrapper to enable distributed execution.
• Configures hardware backends including DDP, FSDP, and DeepSpeed via external config or flags rather than code changes.
• Automates device placement and gradient accumulation to handle larger batch sizes and models.
• Implements main-process guards and state saving/loading to ensure consistent logging and checkpointing across ranks.

Frameworks & tools

PyTorch, Hugging Face Accelerate, DeepSpeed, FSDP, and CUDA.

Why this beats prompting it yourself

Generic prompts often fail to handle the specific "contract" of distributed training, such as ensuring logging only happens on rank 0 or correctly replacing loss.backward(). This skill provides the exact touchpoints needed to port a script without breaking the training logic or causing deadlocks.

Use cases

• Scale a local PyTorch training script to a 4-GPU workstation or cloud cluster.
• Enable BF16 or FP16 mixed precision to reduce VRAM usage and speed up training.
• Transition large models to FSDP sharding when they no longer fit on a single GPU.
• Standardize checkpointing and logging across multi-node environments.

Known limitations

Designed strictly for PyTorch scripts. Does not support other ML frameworks or inference-only optimization workflows.