---

name: accelerate description: "Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

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:

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.