accelerate

# Accelerate Use this repo skill when a task involves Hugging Face Accelerate: adapting PyTorch code with `Accelerator`, building `accelerate launch` commands, validating config files, choosing distributed backends, loading large models with device maps/offload, or saving/logging training state. ## Install And Import Check For normal package use: ```bash pip install accelerate python - <<'PY' import accelerate from accelerate import Accelerator print(accelerate.__version__) print(Accelerator) PY ``` Install optional backends only for workflows that need them, such as DeepSpeed, torch-xla, transformer-engine, torchao, bitsandbytes, experiment trackers, or model libraries. Do not install broad development or testing extras unless the user explicitly asks for repository development coverage. ## Route By Task - Use `sub-skills/training-loop-integration/` to migrate raw PyTorch training/evaluation loops to `Accelerator`, `prepare()`, `backward()`, mixed precision, dataloader handling, gradient accumulation, DDP kwargs, and distributed-loop debugging. - Use `sub-skills/configuration-and-cli/` to create or validate Accelerate config YAML, construct `accelerate launch` commands, inspect `accelerate env`, estimate memory, merge weights, and plan multi-node or SLURM launches without executing them. - Use `sub-skills/distributed-training-backends/` to choose and configure DeepSpeed, FSDP/FSDP2, Megatron-LM, TPU/XLA, FP8, quantization, compilation, tensor/context parallelism, Local SGD, or DDP communication hooks. - Use `sub-skills/big-model-inference/` for `init_empty_weights`, `infer_auto_device_map`, `load_checkpoint_and_dispatch`, CPU/disk offload, model hooks, PiPPy/distributed inference, and memory-sizing workflows. - Use `sub-skills/checkpointing-and-tracking/` for `save_state`, `load_state`, checkpoint hooks, `ProjectConfiguration`, model export, experiment trackers, distributed-safe logging, profiling, and memory cleanup. ## Shared References And Scripts - Read `references/troubleshooting.md` first for cross-cutting install/import, CLI, optional dependency, hardware, and distributed hang triage. - Read `references/repo-provenance.md` before deciding whether this skill matches a current Accelerate checkout or should be refreshed. - Run `scripts/check_accelerate_environment.py --help` or the script itself for a safe import/CLI/backend availability diagnostic. ## Common Decision Points - Prefer `Accelerator` and `accelerator.prepare(...)` for ordinary training loops; do not start with backend-specific plugins until the baseline loop is clear. - Prefer `accelerate config` or a reviewed config YAML when launch commands become long, multi-node, or backend-specific. - Treat DeepSpeed, FSDP, TPU/XLA, FP8, and quantization as optional backend surfaces with package, hardware, and version constraints. - Use big-model dispatch/offload APIs for model loading and inference memory pressure; do not use them as a replacement for normal training-loop preparation. - Use checkpointing/tracking helpers from the nearest sub-skill before adding custom save/load or logging code in distributed jobs. ## Safety Defaults - Run helper scripts with `--help` first when adapting them. - Avoid commands that download models/datasets, launch multi-process distributed jobs, require GPUs/TPUs/SLURM, or contact external tracker services unless the user explicitly asks and the environment is ready. - For verification in limited environments, prefer parser checks, config validation, tiny CPU smoke tests, and static backend diagnostics over full distributed execution.