Huggingface Accelerate Agent Skill
Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/OpenRaiser/NanoResearch --skill huggingface-accelerateThe instruction itself
13 sections, as written by the authorHuggingFace Accelerate - Unified Distributed Training
Quick start
Accelerate simplifies distributed training to 4 lines of code.
Installation:
pip install accelerate
Convert PyTorch script (4 lines):
import torch
+ from accelerate import Accelerator
+ accelerator = Accelerator()
model = torch.nn.Transformer()
optimizer = torch.optim.Adam(model.parameters())
dataloader = torch.utils.data.DataLoader(dataset)
+ model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
for batch in dataloader:
optimizer.zero_grad()
loss = model(batch)
- loss.backward()
+ accelerator.backward(loss)
optimizer.step()
Run (single command):
accelerate launch train.py
Common workflows
Workflow 1: From single GPU to multi-GPU
Original script:
# train.py
import torch
model = torch.nn.Linear(10, 2).to('cuda')
optimizer = torch.optim.Adam(model.parameters())
dataloader = torch.utils.data.DataLoader(dataset, batch_size=32)
for epoch in range(10):
for batch in dataloader:
batch = batch.to('cuda')
optimizer.zero_grad()
loss = model(batch).mean()
loss.backward()
optimizer.step()
With Accelerate (4 lines added):
# train.py
import torch
from accelerate import Accelerator # +1
accelerator = Accelerator() # +2
model = torch.nn.Linear(10, 2)
optimizer = torch.optim.Adam(model.parameters())
dataloader = torch.utils.data.DataLoader(dataset, batch_size=32)
model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader) # +3
for epoch in range(10):
for batch in dataloader:
# No .to('cuda') needed - automatic!
optimizer.zero_grad()
loss = model(batch).mean()
accelerator.backward(loss) # +4
optimizer.step()
Configure (interactive):
accelerate config
Questions:
- Which machine? (single/multi GPU/TPU/CPU)
- How many machines? (1)
- Mixed precision? (no/fp16/bf16/fp8)
- DeepSpeed? (no/yes)
Launch (works on any setup):
# Single GPU
accelerate launch train.py
# Multi-GPU (8 GPUs)
accelerate launch --multi_gpu --num_processes 8 train.py
# Multi-node
accelerate launch --multi_gpu --num_processes 16 \
--num_machines 2 --machine_rank 0 \
--main_process_ip $MASTER_ADDR \
train.py
Workflow 2: Mixed precision training
Enable FP16/BF16:
from accelerate import Accelerator
# FP16 (with gradient scaling)
accelerator = Accelerator(mixed_precision='fp16')
# BF16 (no scaling, more stable)
accelerator = Accelerator(mixed_precision='bf16')
# FP8 (H100+)
accelerator = Accelerator(mixed_precision='fp8')
model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
# Everything else is automatic!
for batch in dataloader:
with accelerator.autocast(): # Optional, done automatically
loss = model(batch)
accelerator.backward(loss)
Workflow 3: DeepSpeed ZeRO integration
Enable DeepSpeed ZeRO-2:
from accelerate import Accelerator
accelerator = Accelerator(
mixed_precision='bf16',
deepspeed_plugin={
"zero_stage": 2, # ZeRO-2
"offload_optimizer": False,
"gradient_accumulation_steps": 4
}
)
# Same code as before!
model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
Or via config:
accelerate config
# Select: DeepSpeed → ZeRO-2
deepspeed_config.json:
{
"fp16": {"enabled": false},
"bf16": {"enabled": true},
"zero_optimization": {
"stage": 2,
"offload_optimizer": {"device": "cpu"},
"allgather_bucket_size": 5e8,
"reduce_bucket_size": 5e8
}
}
Launch:
accelerate launch --config_file deepspeed_config.json train.py
Workflow 4: FSDP (Fully Sharded Data Parallel)
Enable FSDP:
from accelerate import Accelerator, FullyShardedDataParallelPlugin
fsdp_plugin = FullyShardedDataParallelPlugin(
sharding_strategy="FULL_SHARD", # ZeRO-3 equivalent
auto_wrap_policy="TRANSFORMER_AUTO_WRAP",
cpu_offload=False
)
accelerator = Accelerator(
mixed_precision='bf16',
fsdp_plugin=fsdp_plugin
)
model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
Or via config:
accelerate config
# Select: FSDP → Full Shard → No CPU Offload
Workflow 5: Gradient accumulation
Accumulate gradients:
from accelerate import Accelerator
accelerator = Accelerator(gradient_accumulation_steps=4)
model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
for batch in dataloader:
with accelerator.accumulate(model): # Handles accumulation
optimizer.zero_grad()
loss = model(batch)
accelerator.backward(loss)
optimizer.step()
Effective batch size: batch_size * num_gpus * gradient_accumulation_steps
When to use vs alternatives
Use Accelerate when:
- Want simplest distributed training
- Need single script for any hardware
- Use HuggingFace ecosystem
- Want flexibility (DDP/DeepSpeed/FSDP/Megatron)
- Need quick prototyping
Key advantages:
- 4 lines: Minimal code changes
- Unified API: Same code for DDP, DeepSpeed, FSDP, Megatron
- Automatic: Device placement, mixed precision, sharding
- Interactive config: No manual launcher setup
- Single launch: Works everywhere
Use alternatives instead:
- PyTorch Lightning: Need callbacks, high-level abstractions
- Ray Train: Multi-node orchestration, hyperparameter tuning
- DeepSpeed: Direct API control, advanced features
- Raw DDP: Maximum control, minimal abstraction
Common issues
Issue: Wrong device placement
Don't manually move to device:
# WRONG
batch = batch.to('cuda')
# CORRECT
# Accelerate handles it automatically after prepare()
Issue: Gradient accumulation not working
Use context manager:
# CORRECT
with accelerator.accumulate(model):
optimizer.zero_grad()
accelerator.backward(loss)
optimizer.step()
Issue: Checkpointing in distributed
Use accelerator methods:
# Save only on main process
if accelerator.is_main_process:
accelerator.save_state('checkpoint/')
# Load on all processes
accelerator.load_state('checkpoint/')
Issue: Different results with FSDP
Ensure same random seed:
from accelerate.utils import set_seed
set_seed(42)
Advanced topics
Megatron integration: See references/megatron-integration.md for tensor parallelism, pipeline parallelism, and sequence parallelism setup.
Custom plugins: See references/custom-plugins.md for creating custom distributed plugins and advanced configuration.
Performance tuning: See references/performance.md for profiling, memory optimization, and best practices.
Hardware requirements
- CPU: Works (slow)
- Single GPU: Works
- Multi-GPU: DDP (default), DeepSpeed, or FSDP
- Multi-node: DDP, DeepSpeed, FSDP, Megatron
- TPU: Supported
- Apple MPS: Supported
Launcher requirements:
- DDP:
torch.distributed.run(built-in) - DeepSpeed:
deepspeed(pip install deepspeed) - FSDP: PyTorch 1.12+ (built-in)
- Megatron: Custom setup
Resources
- Docs: https://huggingface.co/docs/accelerate
- GitHub: https://github.com/huggingface/accelerate
- Version: 1.11.0+
- Tutorial: "Accelerate your scripts"
- Examples: https://github.com/huggingface/accelerate/tree/main/examples
- Used by: HuggingFace Transformers, TRL, PEFT, all HF libraries
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How to use it
Take openraiser/huggingface-accelerate from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip.
Without those the skill loads but fails at the first command.