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