huggingface-accelerate

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

Agent operating procedure

1. Check the environment. Check GPU type, memory and driver/CUDA versions (nvidia-smi), framework versions, and dataset location and size.
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Do a smoke run: tiny model or subset, few steps, and confirm loss decreases and checkpoints save.
4. Execute the full task using the instructions and references above.
5. Validate the result. Track metrics on held-out data, compare against a baseline, and record seeds, configs and hardware.
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
CUDA out-of-memory	Reduce batch size, enable gradient accumulation/checkpointing or mixed precision, or shard the model.
Loss is NaN or diverges	Lower the learning rate, check data for invalid values, and enable gradient clipping.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Never claim training results without logs; estimate compute cost before launching large jobs.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

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