Huggingface accélère
/SKILLAPI de formation distribuée ultra-simple. 4 lignes suffisent pour ajouter la prise en charge distribuée à n’importe quel script PyTorch. Une API unifiée pour DeepSpeed/FSDP/Megatron/DDP. Placement
name: huggingface-accelerate
description: 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.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Distributed Training, HuggingFace, Accelerate, DeepSpeed, FSDP, Mixed Precision, PyTorch, DDP, Unified API, Simple]
dependencies: [accelerate, torch, transformers]
HuggingFace Accelerate - Unified Distributed Training
Quick start
Accelerate simplifies distributed training to 4 lines of code.
Installation:
pip install accelerateConvert 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.pyCommon 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 configQuestions:
- 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.pyWorkflow 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-2deepspeed_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.pyWorkflow 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 
