huggingface-accelerate

You write the training script once and choose afterwards where it runs — one graphics card, eight of them, several machines, or a TPU — instead of rewriting it for each.

WHEN TO USE

SCOPE

USE FOR

Move a single-GPU training script to multi-GPU without touching the training loop.
Turn on FP16, BF16, or FP8 mixed precision by changing one Accelerator argument.
Enable DeepSpeed ZeRO-2 or FSDP sharding for a model too large for one GPU's memory.
Accumulate gradients across steps to simulate a bigger batch size than memory allows.

NOT FOR

For callbacks and higher-level training abstractions, use PyTorch Lightning instead.
For multi-node orchestration and hyperparameter tuning, use Ray Train instead.
For direct API control over advanced ZeRO features, use DeepSpeed instead.
For maximum control with minimal abstraction, use raw DDP instead.

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In this document (8)

HuggingFace 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

Local tool required

1 FOR THE AGENT

01
Install the Accelerate Python package
Install accelerate (pip install accelerate) alongside torch and transformers so accelerate launch and Accelerator() can run your training script.

This skill ships inside the AI Research Skills plugin.

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What's inside

Files included with this skill.

View on GitHub

HuggingFace Accelerate - Unified Distributed Training

Quick start

Common workflows

Workflow 1: From single GPU to multi-GPU

Workflow 2: Mixed precision training

Workflow 3: DeepSpeed ZeRO integration

Workflow 4: FSDP (Fully Sharded Data Parallel)

Workflow 5: Gradient accumulation

When to use vs alternatives

Common issues

Advanced topics

Hardware requirements

Resources

Local tool required

What's inside

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