Accelerate documentation


You are viewing v0.27.2 version. A newer version v0.30.1 is available.
Hugging Face's logo
Join the Hugging Face community

and get access to the augmented documentation experience

to get started


When training a PyTorch model with 🤗 Accelerate, you may often want to save and continue a state of training. Doing so requires saving and loading the model, optimizer, RNG generators, and the GradScaler. Inside 🤗 Accelerate are two convenience functions to achieve this quickly:

  • Use save_state() for saving everything mentioned above to a folder location
  • Use load_state() for loading everything stored from an earlier save_state

To further customize where and how states are saved through save_state() the ProjectConfiguration class can be used. For example if automatic_checkpoint_naming is enabled each saved checkpoint will be located then at Accelerator.project_dir/checkpoints/checkpoint_{checkpoint_number}.

It should be noted that the expectation is that those states come from the same training script, they should not be from two separate scripts.

  • By using register_for_checkpointing(), you can register custom objects to be automatically stored or loaded from the two prior functions, so long as the object has a state_dict and a load_state_dict functionality. This could include objects such as a learning rate scheduler.

Below is a brief example using checkpointing to save and reload a state during training:

from accelerate import Accelerator
import torch

accelerator = Accelerator(project_dir="my/save/path")

my_scheduler = torch.optim.lr_scheduler.StepLR(my_optimizer, step_size=1, gamma=0.99)
my_model, my_optimizer, my_training_dataloader = accelerator.prepare(my_model, my_optimizer, my_training_dataloader)

# Register the LR scheduler

# Save the starting state

device = accelerator.device

# Perform training
for epoch in range(num_epochs):
    for batch in my_training_dataloader:
        inputs, targets = batch
        inputs =
        targets =
        outputs = my_model(inputs)
        loss = my_loss_function(outputs, targets)

# Restore the previous state

Restoring the state of the DataLoader

After resuming from a checkpoint, it may also be desirable to resume from a particular point in the active DataLoader if the state was saved during the middle of an epoch. You can use skip_first_batches() to do so.

from accelerate import Accelerator

accelerator = Accelerator(project_dir="my/save/path")

train_dataloader = accelerator.prepare(train_dataloader)

# Assume the checkpoint was saved 100 steps into the epoch
skipped_dataloader = accelerator.skip_first_batches(train_dataloader, 100)

# After the first iteration, go back to `train_dataloader`

# First epoch
for batch in skipped_dataloader:
    # Do something

# Second epoch
for batch in train_dataloader:
    # Do something