#
Accelerator

The Accelerator is the main class for enabling distributed training on any type of training setup. Read the Add Accelerator to your code tutorial to learn more about how to add the Accelerator to your script.

##
Accelerator

###
class accelerate.Accelerator

< source >

( device_placement: bool = True split_batches: bool = <object object at 0x7f2a88cae6e0> mixed_precision: PrecisionType | str | None = None gradient_accumulation_steps: int = 1 cpu: bool = False dataloader_config: DataLoaderConfiguration | None = None deepspeed_plugin: DeepSpeedPlugin | dict[str, DeepSpeedPlugin] | None = None fsdp_plugin: FullyShardedDataParallelPlugin | None = None torch_tp_plugin: TorchTensorParallelPlugin | None = None megatron_lm_plugin: MegatronLMPlugin | None = None rng_types: list[str | RNGType] | None = None log_with: str | LoggerType | GeneralTracker | list[str | LoggerType | GeneralTracker] | None = None project_dir: str | os.PathLike | None = None project_config: ProjectConfiguration | None = None gradient_accumulation_plugin: GradientAccumulationPlugin | None = None step_scheduler_with_optimizer: bool = True kwargs_handlers: list[KwargsHandler] | None = None dynamo_backend: DynamoBackend | str | None = None dynamo_plugin: TorchDynamoPlugin | None = None deepspeed_plugins: DeepSpeedPlugin | dict[str, DeepSpeedPlugin] | None = None parallelism_config: ParallelismConfig | None = None )

Parameters

- device_placement (`bool`, optional, defaults to `True`) — Whether or not the accelerator should put objects on device (tensors yielded by the dataloader, model, etc…).
- mixed_precision (`str`, optional) — Whether or not to use mixed precision training. Choose from ‘no’,‘fp16’,‘bf16’ or ‘fp8’. Will default to the value in the environment variable `ACCELERATE_MIXED_PRECISION`, which will use the default value in the accelerate config of the current system or the flag passed with the `accelerate.launch` command. ‘fp8’ requires the installation of transformers-engine.
- gradient_accumulation_steps (`int`, optional, default to 1) — The number of steps that should pass before gradients are accumulated. A number > 1 should be combined with `Accelerator.accumulate`. If not passed, will default to the value in the environment variable `ACCELERATE_GRADIENT_ACCUMULATION_STEPS`. Can also be configured through a `GradientAccumulationPlugin`.
- cpu (`bool`, optional) — Whether or not to force the script to execute on CPU. Will ignore GPU available if set to `True` and force the execution on one process only.
- dataloader_config (`DataLoaderConfiguration`, optional) — A configuration for how the dataloaders should be handled in distributed scenarios.
- deepspeed_plugin (DeepSpeedPlugin or dict of `str` — DeepSpeedPlugin, optional): Tweak your DeepSpeed related args using this argument. This argument is optional and can be configured directly using accelerate config. If using multiple plugins, use the configured `key` property of each plugin to access them from `accelerator.state.get_deepspeed_plugin(key)`. Alias for `deepspeed_plugins`.
- fsdp_plugin (FullyShardedDataParallelPlugin, optional) — Tweak your FSDP related args using this argument. This argument is optional and can be configured directly using accelerate config
- torch_tp_plugin (`TorchTensorParallelPlugin`, optional) — Deprecated: use `parallelism_config` with `tp_size` instead.
- megatron_lm_plugin (MegatronLMPlugin, optional) — Tweak your MegatronLM related args using this argument. This argument is optional and can be configured directly using accelerate config
- rng_types (list of `str` or RNGType) — The list of random number generators to synchronize at the beginning of each iteration in your prepared dataloaders. Should be one or several of:
  - `"torch"`: the base torch random number generator
  - `"cuda"`: the CUDA random number generator (GPU only)
  - `"xla"`: the XLA random number generator (TPU only)
  - `"generator"`: the `torch.Generator` of the sampler (or batch sampler if there is no sampler in your dataloader) or of the iterable dataset (if it exists) if the underlying dataset is of that type.

  Will default to `["torch"]` for PyTorch versions <=1.5.1 and `["generator"]` for PyTorch versions >= 1.6.

- log_with (list of `str`, LoggerType or GeneralTracker, optional) — A list of loggers to be setup for experiment tracking. Should be one or several of:
  - `"all"`
  - `"tensorboard"`
  - `"wandb"`
  - `"trackio"`
  - `"aim"`
  - `"comet_ml"`
  - `"mlflow"`
  - `"dvclive"`
  - `"swanlab"` If `"all"` is selected, will pick up all available trackers in the environment and initialize them. Can also accept implementations of `GeneralTracker` for custom trackers, and can be combined with `"all"`.
- project_config (ProjectConfiguration, optional) — A configuration for how saving the state can be handled.
- project_dir (`str`, `os.PathLike`, optional) — A path to a directory for storing data such as logs of locally-compatible loggers and potentially saved checkpoints.
- step_scheduler_with_optimizer (`bool`, optional, defaults to `True`) — Set `True` if the learning rate scheduler is stepped at the same time as the optimizer, `False` if only done under certain circumstances (at the end of each epoch, for instance).
- kwargs_handlers (list of KwargsHandler, optional) — A list of KwargsHandler to customize how the objects related to distributed training, profiling or mixed precision are created. See kwargs for more information.
- dynamo_backend (`str` or DynamoBackend, optional, defaults to `"no"`) — Set to one of the possible dynamo backends to optimize your training with torch dynamo.
- dynamo_plugin (TorchDynamoPlugin, optional) — A configuration for how torch dynamo should be handled, if more tweaking than just the `backend` or `mode` is needed.
- gradient_accumulation_plugin (GradientAccumulationPlugin, optional) — A configuration for how gradient accumulation should be handled, if more tweaking than just the `gradient_accumulation_steps` is needed.

Creates an instance of an accelerator for distributed training or mixed precision training.

Available attributes:

- device (`torch.device`) — The device to use.
- distributed_type (DistributedType) — The distributed training configuration.
- local_process_index (`int`) — The process index on the current machine.
- mixed_precision (`str`) — The configured mixed precision mode.
- num_processes (`int`) — The total number of processes used for training.
- optimizer_step_was_skipped (`bool`) — Whether or not the optimizer update was skipped (because of gradient overflow in mixed precision), in which case the learning rate should not be changed.
- process_index (`int`) — The overall index of the current process among all processes.
- state (AcceleratorState) — The distributed setup state.
- sync_gradients (`bool`) — Whether the gradients are currently being synced across all processes.
- use_distributed (`bool`) — Whether the current configuration is for distributed training.

####
accumulate

< source >

( *models )

Parameters

- *models (list of `torch.nn.Module`) — PyTorch Modules that were prepared with `Accelerator.prepare`. Models passed to `accumulate()` will skip gradient syncing during backward pass in distributed training

A context manager that will lightly wrap around and perform gradient accumulation automatically

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator(gradient_accumulation_steps=1)
    >>> dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler)

    >>> for input, output in dataloader:
    ...     with accelerator.accumulate(model):
    ...         outputs = model(input)
    ...         loss = loss_func(outputs)
    ...         loss.backward()
    ...         optimizer.step()
    ...         scheduler.step()
    ...         optimizer.zero_grad()

####
autocast

< source >

( autocast_handler: AutocastKwargs = None )

Will apply automatic mixed-precision inside the block inside this context manager, if it is enabled. Nothing different will happen otherwise.

A different `autocast_handler` can be passed in to override the one set in the `Accelerator` object. This is useful in blocks under `autocast` where you want to revert to fp32.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator(mixed_precision="fp16")
    >>> with accelerator.autocast():
    ...     train()

####
backward

< source >

( loss **kwargs )

Scales the gradients in accordance to the `GradientAccumulationPlugin` and calls the correct `backward()` based on the configuration.

Should be used in lieu of `loss.backward()`.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator(gradient_accumulation_steps=2)
    >>> outputs = model(inputs)
    >>> loss = loss_fn(outputs, labels)
    >>> accelerator.backward(loss)

####
check_trigger

< source >

( )

Checks if the internal trigger tensor has been set to 1 in any of the processes. If so, will return `True` and reset the trigger tensor to 0.

Note: Does not require `wait_for_everyone()`

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> # Assume later in the training script
    >>> # `should_do_breakpoint` is a custom function to monitor when to break,
    >>> # e.g. when the loss is NaN
    >>> if should_do_breakpoint(loss):
    ...     accelerator.set_trigger()
    >>> # Assume later in the training script
    >>> if accelerator.check_trigger():
    ...     break

####
clear

< source >

( *objects )

Alias for `Accelerate.free_memory`, releases all references to the internal objects stored and call the garbage collector. You should call this method between two trainings with different models/optimizers.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> model, optimizer, scheduler = ...
    >>> model, optimizer, scheduler = accelerator.prepare(model, optimizer, scheduler)
    >>> model, optimizer, scheduler = accelerator.clear(model, optimizer, scheduler)

####
clip_grad_norm_

< source >

( parameters max_norm norm_type = 2 ) →
`torch.Tensor`

Returns

`torch.Tensor`

Total norm of the parameter gradients (viewed as a single vector).

Should be used in place of `torch.nn.utils.clip_grad_norm_`.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator(gradient_accumulation_steps=2)
    >>> dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler)

    >>> for input, target in dataloader:
    ...     optimizer.zero_grad()
    ...     output = model(input)
    ...     loss = loss_func(output, target)
    ...     accelerator.backward(loss)
    ...     if accelerator.sync_gradients:
    ...         accelerator.clip_grad_norm_(model.parameters(), max_grad_norm)
    ...     optimizer.step()

####
clip_grad_value_

< source >

( parameters clip_value )

Should be used in place of `torch.nn.utils.clip_grad_value_`.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator(gradient_accumulation_steps=2)
    >>> dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler)

    >>> for input, target in dataloader:
    ...     optimizer.zero_grad()
    ...     output = model(input)
    ...     loss = loss_func(output, target)
    ...     accelerator.backward(loss)
    ...     if accelerator.sync_gradients:
    ...         accelerator.clip_grad_value_(model.parameters(), clip_value)
    ...     optimizer.step()

####
end_training

< source >

( )

Runs any special end training behaviors, such as stopping trackers on the main process only or destoying process group. Should always be called at the end of your script if using experiment tracking.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator(log_with="tensorboard")
    >>> accelerator.init_trackers("my_project")
    >>> # Do training
    >>> accelerator.end_training()

####
free_memory

< source >

( *objects )

Will release all references to the internal objects stored and call the garbage collector. You should call this method between two trainings with different models/optimizers. Also will reset `Accelerator.step` to 0.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> model, optimizer, scheduler = ...
    >>> model, optimizer, scheduler = accelerator.prepare(model, optimizer, scheduler)
    >>> model, optimizer, scheduler = accelerator.free_memory(model, optimizer, scheduler)

####
gather

< source >

( tensor ) →
`torch.Tensor`, or a nested tuple/list/dictionary of `torch.Tensor`

Parameters

- tensor (`torch.Tensor`, or a nested tuple/list/dictionary of `torch.Tensor`) — The tensors to gather across all processes.

Returns

`torch.Tensor`, or a nested tuple/list/dictionary of `torch.Tensor`

The gathered tensor(s). Note that the first dimension of the result is num_processes multiplied by the first dimension of the input tensors.

Gather the values in tensor across all processes and concatenate them on the first dimension. Useful to regroup the predictions from all processes when doing evaluation.

Note: This gather happens in all processes.

Example:

    >>> # Assuming four processes
    >>> import torch
    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> process_tensor = torch.tensor([accelerator.process_index], device=accelerator.device)
    >>> gathered_tensor = accelerator.gather(process_tensor)
    >>> gathered_tensor
    tensor([0, 1, 2, 3])

####
gather_for_metrics

< source >

( input_data use_gather_object = False )

Parameters

- input (`torch.Tensor`, `object`, a nested tuple/list/dictionary of `torch.Tensor`, or a nested tuple/list/dictionary of `object`) — The tensors or objects for calculating metrics across all processes
- use_gather_object(`bool`) — Whether to forcibly use gather_object instead of gather (which is already done if all objects passed do not contain tensors). This flag can be useful for gathering tensors with different sizes that we don’t want to pad and concatenate along the first dimension. Using it with GPU tensors is not well supported and inefficient as it incurs GPU -> CPU transfer since tensors would be pickled.

Gathers `input_data` and potentially drops duplicates in the last batch if on a distributed system. Should be used for gathering the inputs and targets for metric calculation.

Example:

    >>> # Assuming two processes, with a batch size of 5 on a dataset with 9 samples
    >>> import torch
    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> dataloader = torch.utils.data.DataLoader(range(9), batch_size=5)
    >>> dataloader = accelerator.prepare(dataloader)
    >>> batch = next(iter(dataloader))
    >>> gathered_items = accelerator.gather_for_metrics(batch)
    >>> len(gathered_items)
    9

####
get_state_dict

< source >

( model unwrap = True ) →
`dict`

Parameters

- model (`torch.nn.Module`) — A PyTorch model sent through Accelerator.prepare()
- unwrap (`bool`, optional, defaults to `True`) — Whether to return the original underlying state_dict of `model` or to return the wrapped state_dict

Returns

`dict`

The state dictionary of the model potentially without full precision.

Returns the state dictionary of a model sent through Accelerator.prepare() potentially without full precision.

Example:

    >>> import torch
    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> net = torch.nn.Linear(2, 2)
    >>> net = accelerator.prepare(net)
    >>> state_dict = accelerator.get_state_dict(net)

####
get_tracker

< source >

( name: str unwrap: bool = False ) →
`GeneralTracker`

Parameters

- name (`str`) — The name of a tracker, corresponding to the `.name` property.
- unwrap (`bool`) — Whether to return the internal tracking mechanism or to return the wrapped tracker instead (recommended).

Returns

`GeneralTracker`

The tracker corresponding to `name` if it exists.

Returns a `tracker` from `self.trackers` based on `name` on the main process only.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator(log_with="tensorboard")
    >>> accelerator.init_trackers("my_project")
    >>> tensorboard_tracker = accelerator.get_tracker("tensorboard")

####
join_uneven_inputs

< source >

( joinables even_batches = None )

Parameters

- joinables (`list[torch.distributed.algorithms.Joinable]`) — A list of models or optimizers that subclass `torch.distributed.algorithms.Joinable`. Most commonly, a PyTorch Module that was prepared with `Accelerator.prepare` for DistributedDataParallel training.
- even_batches (`bool`, optional) — If set, this will override the value of `even_batches` set in the `Accelerator`. If it is not provided, the default `Accelerator` value wil be used.

A context manager that facilitates distributed training or evaluation on uneven inputs, which acts as a wrapper around `torch.distributed.algorithms.join`. This is useful when the total batch size does not evenly divide the length of the dataset.

> `join_uneven_inputs` is only supported for Distributed Data Parallel training on multiple GPUs. For any other configuration, this method will have no effect.

> Overriding `even_batches` will not affect iterable-style data loaders.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator(even_batches=True)
    >>> ddp_model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)

    >>> with accelerator.join_uneven_inputs([ddp_model], even_batches=False):
    ...     for input, output in dataloader:
    ...         outputs = model(input)
    ...         loss = loss_func(outputs)
    ...         loss.backward()
    ...         optimizer.step()
    ...         optimizer.zero_grad()

####
load_state

< source >

( input_dir: str | None = None load_kwargs: dict | None = None **load_model_func_kwargs )

Parameters

- input_dir (`str` or `os.PathLike`) — The name of the folder all relevant weights and states were saved in. Can be `None` if `automatic_checkpoint_naming` is used, and will pick up from the latest checkpoint.
- load_kwargs (`dict`, optional) — Additional keyword arguments for the underlying `load` function, such as optional arguments for state_dict and optimizer on.
- load_model_func_kwargs (`dict`, optional) — Additional keyword arguments for loading model which can be passed to the underlying load function, such as optional arguments for DeepSpeed’s `load_checkpoint` function or a `map_location` to load the model and optimizer on.

Loads the current states of the model, optimizer, scaler, RNG generators, and registered objects.

> Should only be used in conjunction with Accelerator.save_state(). If a file is not registered for checkpointing, it will not be loaded if stored in the directory.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> model, optimizer, lr_scheduler = ...
    >>> model, optimizer, lr_scheduler = accelerator.prepare(model, optimizer, lr_scheduler)
    >>> accelerator.load_state("my_checkpoint")

####
local_main_process_first

< source >

( )

Lets the local main process go inside a with block.

The other processes will enter the with block after the main process exits.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> with accelerator.local_main_process_first():
    ...     # This will be printed first by local process 0 then in a seemingly
    ...     # random order by the other processes.
    ...     print(f"This will be printed by process {accelerator.local_process_index}")

####
lomo_backward

< source >

( loss: torch.Tensor learning_rate: float )

Runs backward pass on LOMO optimizers.

####
main_process_first

< source >

( )

Lets the main process go first inside a with block.

The other processes will enter the with block after the main process exits.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> with accelerator.main_process_first():
    ...     # This will be printed first by process 0 then in a seemingly
    ...     # random order by the other processes.
    ...     print(f"This will be printed by process {accelerator.process_index}")

####
maybe_context_parallel

< source >

( buffers: list[torch.Tensor] | None = None buffer_seq_dims: list[int] | None = None no_restore_buffers: set[torch.Tensor] | None = None )

Parameters

- buffers (`list[torch.Tensor]`, `optional`) — Buffers, which are going to be sharded along the sequence dimension. Common examples are inputs, labels or positional embedding buffers. This context manager will modify these buffers in-place, and after exiting the context, the buffers will be restored to their original state. To avoid unnecessary restores, you can use `no_restore_buffers` to specify which buffers don’t need to be restored.
- buffer_seq_dims (`list[int]`, `optional`) — Sequence dimensions of `buffers`.
- no_restore_buffers (`set[torch.Tensor]`, `optional`) — This set must be a subset of `buffers`. Specifies which buffers from `buffers` argument won’t be restored after the context exits. These buffers will be then kept in sharded state.

A context manager that enables context parallel training.

> `context_parallel` is currently only supported together with FSDP2, and requires `parallelism_config.cp_size` >

> 1. If either of these conditions are not met, this context manager will have no effect, though to enable fewer code changes it will not raise an Exception.

> This context manager has to be recreated with each training step, as shown in the example below.

Example:

    >>> for batch in dataloader:
    ...     with accelerator.maybe_context_parallel(
    ...         buffers=[batch["input_ids"], batch["attention_mask"]],
    ...         buffer_seq_dims=[1, 1],
    ...         no_restore_buffers={batch["input_ids"]},
    ...     ):
    ...         outputs = model(batch)
    ...         ...

####
no_sync

< source >

( model )

Parameters

- model (`torch.nn.Module`) — PyTorch Module that was prepared with `Accelerator.prepare`

A context manager to disable gradient synchronizations across DDP processes by calling `torch.nn.parallel.DistributedDataParallel.no_sync`.

If `model` is not in DDP, this context manager does nothing

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> dataloader, model, optimizer = accelerator.prepare(dataloader, model, optimizer)
    >>> input_a = next(iter(dataloader))
    >>> input_b = next(iter(dataloader))

    >>> with accelerator.no_sync():
    ...     outputs = model(input_a)
    ...     loss = loss_func(outputs)
    ...     accelerator.backward(loss)
    ...     # No synchronization across processes, only accumulate gradients
    >>> outputs = model(input_b)
    >>> accelerator.backward(loss)
    >>> # Synchronization across all processes
    >>> optimizer.step()
    >>> optimizer.zero_grad()

####
on_last_process

< source >

( function: Callable[..., Any] )

Parameters

- function (`Callable`) — The function to decorate.

A decorator that will run the decorated function on the last process only. Can also be called using the `PartialState` class.

Example:

    # Assume we have 4 processes.
    from accelerate import Accelerator

    accelerator = Accelerator()

    @accelerator.on_last_process
    def print_something():
        print(f"Printed on process {accelerator.process_index}")

    print_something()
    "Printed on process 3"

####
on_local_main_process

< source >

( function: Callable[..., Any] | None = None )

Parameters

- function (`Callable`) — The function to decorate.

A decorator that will run the decorated function on the local main process only. Can also be called using the `PartialState` class.

Example:

    # Assume we have 2 servers with 4 processes each.
    from accelerate import Accelerator

    accelerator = Accelerator()

    @accelerator.on_local_main_process
    def print_something():
        print("This will be printed by process 0 only on each server.")

    print_something()
    # On server 1:
    "This will be printed by process 0 only"
    # On server 2:
    "This will be printed by process 0 only"

####
on_local_process

< source >

( function: Callable[..., Any] | None = None local_process_index: int | None = None )

Parameters

- function (`Callable`, optional) — The function to decorate.
- local_process_index (`int`, optional) — The index of the local process on which to run the function.

A decorator that will run the decorated function on a given local process index only. Can also be called using the `PartialState` class.

Example:

    # Assume we have 2 servers with 4 processes each.
    from accelerate import Accelerator

    accelerator = Accelerator()

    @accelerator.on_local_process(local_process_index=2)
    def print_something():
        print(f"Printed on process {accelerator.local_process_index}")

    print_something()
    # On server 1:
    "Printed on process 2"
    # On server 2:
    "Printed on process 2"

####
on_main_process

< source >

( function: Callable[..., Any] | None = None )

Parameters

- function (`Callable`) — The function to decorate.

A decorator that will run the decorated function on the main process only. Can also be called using the `PartialState` class.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()

    >>> @accelerator.on_main_process
    ... def print_something():
    ...     print("This will be printed by process 0 only.")

    >>> print_something()
    "This will be printed by process 0 only"

####
on_process

< source >

( function: Callable[..., Any] | None = None process_index: int | None = None )

Parameters

- function (`Callable`, `optional`) — The function to decorate.
- process_index (`int`, `optional`) — The index of the process on which to run the function.

A decorator that will run the decorated function on a given process index only. Can also be called using the `PartialState` class.

Example:

    # Assume we have 4 processes.
    from accelerate import Accelerator

    accelerator = Accelerator()

    @accelerator.on_process(process_index=2)
    def print_something():
        print(f"Printed on process {accelerator.process_index}")

    print_something()
    "Printed on process 2"

####
pad_across_processes

< source >

( tensor dim = 0 pad_index = 0 pad_first = False ) →
`torch.Tensor`, or a nested tuple/list/dictionary of `torch.Tensor`

Parameters

- tensor (nested list/tuple/dictionary of `torch.Tensor`) — The data to gather.
- dim (`int`, optional, defaults to 0) — The dimension on which to pad.
- pad_index (`int`, optional, defaults to 0) — The value with which to pad.
- pad_first (`bool`, optional, defaults to `False`) — Whether to pad at the beginning or the end.

Returns

`torch.Tensor`, or a nested tuple/list/dictionary of `torch.Tensor`

The padded tensor(s).

Recursively pad the tensors in a nested list/tuple/dictionary of tensors from all devices to the same size so they can safely be gathered.

Example:

    >>> # Assuming two processes, with the first processes having a tensor of size 1 and the second of size 2
    >>> import torch
    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> process_tensor = torch.arange(accelerator.process_index + 1).to(accelerator.device)
    >>> padded_tensor = accelerator.pad_across_processes(process_tensor)
    >>> padded_tensor.shape
    torch.Size([2])

####
prepare

< source >

( *args device_placement = None )

Parameters

- *args (list of objects) — Any of the following type of objects:
  - `torch.utils.data.DataLoader`: PyTorch Dataloader
  - `torch.nn.Module`: PyTorch Module
  - `torch.optim.Optimizer`: PyTorch Optimizer
  - `torch.optim.lr_scheduler.LRScheduler`: PyTorch LR Scheduler
- device_placement (`list[bool]`, optional) — Used to customize whether automatic device placement should be performed for each object passed. Needs to be a list of the same length as `args`. Not compatible with DeepSpeed or FSDP.

Prepare all objects passed in `args` for distributed training and mixed precision, then return them in the same order.

> You don’t need to prepare a model if you only use it for inference without any kind of mixed precision

Examples:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> # Assume a model, optimizer, data_loader and scheduler are defined
    >>> model, optimizer, data_loader, scheduler = accelerator.prepare(model, optimizer, data_loader, scheduler)

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> # Assume a model, optimizer, data_loader and scheduler are defined
    >>> device_placement = [True, True, False, False]
    >>> # Will place the first two items passed in automatically to the right device but not the last two.
    >>> model, optimizer, data_loader, scheduler = accelerator.prepare(
    ...     model, optimizer, data_loader, scheduler, device_placement=device_placement
    ... )

####
prepare_data_loader

< source >

( data_loader: torch.utils.data.DataLoader device_placement = None slice_fn_for_dispatch = None )

Parameters

- data_loader (`torch.utils.data.DataLoader`) — A vanilla PyTorch DataLoader to prepare
- device_placement (`bool`, optional) — Whether or not to place the batches on the proper device in the prepared dataloader. Will default to `self.device_placement`.
- slice_fn_for_dispatch (`Callable`, optional`) -- If passed, this function will be used to slice tensors across` num_processes`. Will default to [slice_tensors()](/docs/accelerate/main/en/package_reference/utilities#accelerate.utils.slice_tensors). This argument is used only when` dispatch_batches`is set to`True` and will be ignored otherwise.

Prepares a PyTorch DataLoader for training in any distributed setup. It is recommended to use Accelerator.prepare() instead.

Example:

    >>> import torch
    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> data_loader = torch.utils.data.DataLoader(...)
    >>> data_loader = accelerator.prepare_data_loader(data_loader, device_placement=True)

####
prepare_model

< source >

( model: torch.nn.Module device_placement: bool | None = None evaluation_mode: bool = False )

Parameters

- model (`torch.nn.Module`) — A PyTorch model to prepare. You don’t need to prepare a model if it is used only for inference without any kind of mixed precision
- device_placement (`bool`, optional) — Whether or not to place the model on the proper device. Will default to `self.device_placement`.
- evaluation_mode (`bool`, optional, defaults to `False`) — Whether or not to set the model for evaluation only, by just applying mixed precision and `torch.compile` (if configured in the `Accelerator` object).

Prepares a PyTorch model for training in any distributed setup. It is recommended to use Accelerator.prepare() instead.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> # Assume a model is defined
    >>> model = accelerator.prepare_model(model)

####
prepare_optimizer

< source >

( optimizer: torch.optim.Optimizer device_placement = None )

Parameters

- optimizer (`torch.optim.Optimizer`) — A vanilla PyTorch optimizer to prepare
- device_placement (`bool`, optional) — Whether or not to place the optimizer on the proper device. Will default to `self.device_placement`.

Prepares a PyTorch Optimizer for training in any distributed setup. It is recommended to use Accelerator.prepare() instead.

Example:

    >>> import torch
    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> optimizer = torch.optim.Adam(...)
    >>> optimizer = accelerator.prepare_optimizer(optimizer, device_placement=True)

####
prepare_scheduler

< source >

( scheduler: LRScheduler )

Parameters

- scheduler (`torch.optim.lr_scheduler.LRScheduler`) — A vanilla PyTorch scheduler to prepare

Prepares a PyTorch Scheduler for training in any distributed setup. It is recommended to use Accelerator.prepare() instead.

Example:

    >>> import torch
    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> optimizer = torch.optim.Adam(...)
    >>> scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, ...)
    >>> scheduler = accelerator.prepare_scheduler(scheduler)

####
print

< source >

( *args **kwargs )

Drop in replacement of `print()` to only print once per server.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> accelerator.print("Hello world!")

####
profile

< source >

( profile_handler: ProfileKwargs | None = None )

Parameters

- profile_handler (`ProfileKwargs`, optional) — The profile handler to use for this context manager. If not passed, will use the one set in the `Accelerator` object.

Will profile the code inside the context manager. The profile will be saved to a Chrome Trace file if `profile_handler.output_trace_dir` is set.

A different `profile_handler` can be passed in to override the one set in the `Accelerator` object.

Example:

    # Profile with default settings
    from accelerate import Accelerator
    from accelerate.utils import ProfileKwargs

    accelerator = Accelerator()
    with accelerator.profile() as prof:
        train()
    accelerator.print(prof.key_averages().table())

    # Profile with the custom handler
    def custom_handler(prof):
        print(prof.key_averages().table(sort_by="self_cpu_time_total", row_limit=10))

    kwargs = ProfileKwargs(schedule_option=dict(wait=1, warmup=1, active=1), on_trace_ready=custom_handler)
    accelerator = Accelerator(kwarg_handler=[kwargs])
    with accelerator.profile() as prof:
        for _ in range(10):
            train_iteration()
            prof.step()

    # Profile and export to Chrome Trace
    kwargs = ProfileKwargs(output_trace_dir="output_trace")
    accelerator = Accelerator(kwarg_handler=[kwargs])
    with accelerator.profile():
        train()

####
reduce

< source >

( tensor reduction = 'sum' scale = 1.0 ) →
`torch.Tensor`, or a nested tuple/list/dictionary of `torch.Tensor`

Parameters

- tensor (`torch.Tensor`, or a nested tuple/list/dictionary of `torch.Tensor`) — The tensors to reduce across all processes.
- reduction (`str`, optional, defaults to “sum”) — A reduction type, can be one of ‘sum’, ‘mean’, or ‘none’. If ‘none’, will not perform any operation.
- scale (`float`, optional, defaults to 1.0) — A default scaling value to be applied after the reduce, only valid on XLA.

Returns

`torch.Tensor`, or a nested tuple/list/dictionary of `torch.Tensor`

The reduced tensor(s).

Reduce the values in tensor across all processes based on reduction.

Note: All processes get the reduced value.

Example:

    >>> # Assuming two processes
    >>> import torch
    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> process_tensor = torch.arange(accelerator.num_processes) + 1 + (2 * accelerator.process_index)
    >>> process_tensor = process_tensor.to(accelerator.device)
    >>> reduced_tensor = accelerator.reduce(process_tensor, reduction="sum")
    >>> reduced_tensor
    tensor([4, 6])

####
register_for_checkpointing

< source >

( *objects )

Makes note of `objects` and will save or load them in during `save_state` or `load_state`.

These should be utilized when the state is being loaded or saved in the same script. It is not designed to be used in different scripts.

> Every `object` must have a `load_state_dict` and `state_dict` function to be stored.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> # Assume `CustomObject` has a `state_dict` and `load_state_dict` function.
    >>> obj = CustomObject()
    >>> accelerator.register_for_checkpointing(obj)
    >>> accelerator.save_state("checkpoint.pt")

####
register_load_state_pre_hook

< source >

( hook: Callable[..., None] ) →
`torch.utils.hooks.RemovableHandle`

Parameters

- hook (`Callable`) — A function to be called in Accelerator.load_state() before `load_checkpoint`.

Returns

`torch.utils.hooks.RemovableHandle`

a handle that can be used to remove the added hook by calling `handle.remove()`

Registers a pre hook to be run before `load_checkpoint` is called in Accelerator.load_state().

The hook should have the following signature:

`hook(models: list[torch.nn.Module], input_dir: str) -> None`

The `models` argument are the models as saved in the accelerator state under `accelerator._models`, and the `input_dir` argument is the `input_dir` argument passed to Accelerator.load_state().

> Should only be used in conjunction with Accelerator.register_save_state_pre_hook(). Can be useful to load configurations in addition to model weights. Can also be used to overwrite model loading with a customized method. In this case, make sure to remove already loaded models from the models list.

####
register_save_state_pre_hook

< source >

( hook: Callable[..., None] ) →
`torch.utils.hooks.RemovableHandle`

Parameters

- hook (`Callable`) — A function to be called in Accelerator.save_state() before `save_checkpoint`.

Returns

`torch.utils.hooks.RemovableHandle`

a handle that can be used to remove the added hook by calling `handle.remove()`

Registers a pre hook to be run before `save_checkpoint` is called in Accelerator.save_state().

The hook should have the following signature:

`hook(models: list[torch.nn.Module], weights: list[dict[str, torch.Tensor]], input_dir: str) -> None`

The `models` argument are the models as saved in the accelerator state under `accelerator._models`, `weights` argument are the state dicts of the `models`, and the `input_dir` argument is the `input_dir` argument passed to Accelerator.load_state().

> Should only be used in conjunction with Accelerator.register_load_state_pre_hook(). Can be useful to save configurations in addition to model weights. Can also be used to overwrite model saving with a customized method. In this case, make sure to remove already loaded weights from the weights list.

####
save

< source >

( obj f safe_serialization = False )

Parameters

- obj (`object`) — The object to save.
- f (`str` or `os.PathLike`) — Where to save the content of `obj`.
- safe_serialization (`bool`, optional, defaults to `False`) — Whether to save `obj` using `safetensors`

Save the object passed to disk once per machine. Use in place of `torch.save`.

Note: If `save_on_each_node` was passed in as a `ProjectConfiguration`, will save the object once per node, rather than only once on the main node.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> arr = [0, 1, 2, 3]
    >>> accelerator.save(arr, "array.pkl")

####
save_model

< source >

( model: torch.nn.Module save_directory: Union[str, os.PathLike] max_shard_size: Union[int, str] = '10GB' safe_serialization: bool = True )

Parameters

- model — (`torch.nn.Module`): Model to be saved. The model can be wrapped or unwrapped.
- save_directory (`str` or `os.PathLike`) — Directory to which to save. Will be created if it doesn’t exist.
- max_shard_size (`int` or `str`, optional, defaults to `"10GB"`) — The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size lower than this size. If expressed as a string, needs to be digits followed by a unit (like `"5MB"`).

  If a single weight of the model is bigger than `max_shard_size`, it will be in its own checkpoint shard which will be bigger than `max_shard_size`.

- safe_serialization (`bool`, optional, defaults to `True`) — Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).

Save a model so that it can be re-loaded using load_checkpoint_in_model

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> model = ...
    >>> accelerator.save_model(model, save_directory)

####
save_state

< source >

( output_dir: str | None = None safe_serialization: bool = True **save_model_func_kwargs )

Parameters

- output_dir (`str` or `os.PathLike`) — The name of the folder to save all relevant weights and states.
- safe_serialization (`bool`, optional, defaults to `True`) — Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
- save_model_func_kwargs (`dict`, optional) — Additional keyword arguments for saving model which can be passed to the underlying save function, such as optional arguments for DeepSpeed’s `save_checkpoint` function.

Saves the current states of the model, optimizer, scaler, RNG generators, and registered objects to a folder.

If a `ProjectConfiguration` was passed to the `Accelerator` object with `automatic_checkpoint_naming` enabled then checkpoints will be saved to `self.project_dir/checkpoints`. If the number of current saves is greater than `total_limit` then the oldest save is deleted. Each checkpoint is saved in separate folders named `checkpoint_<iteration>`.

Otherwise they are just saved to `output_dir`.

> Should only be used when wanting to save a checkpoint during training and restoring the state in the same environment.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> model, optimizer, lr_scheduler = ...
    >>> model, optimizer, lr_scheduler = accelerator.prepare(model, optimizer, lr_scheduler)
    >>> accelerator.save_state(output_dir="my_checkpoint")

####
set_trigger

< source >

( )

Sets the internal trigger tensor to 1 on the current process. A latter check should follow using this which will check across all processes.

Note: Does not require `wait_for_everyone()`

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> # Assume later in the training script
    >>> # `should_do_breakpoint` is a custom function to monitor when to break,
    >>> # e.g. when the loss is NaN
    >>> if should_do_breakpoint(loss):
    ...     accelerator.set_trigger()
    >>> # Assume later in the training script
    >>> if accelerator.check_breakpoint():
    ...     break

####
skip_first_batches

< source >

( dataloader num_batches: int = 0 )

Parameters

- dataloader (`torch.utils.data.DataLoader`) — The data loader in which to skip batches.
- num_batches (`int`, optional, defaults to 0) — The number of batches to skip

Creates a new `torch.utils.data.DataLoader` that will efficiently skip the first `num_batches`.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler)
    >>> skipped_dataloader = accelerator.skip_first_batches(dataloader, num_batches=2)
    >>> # for the first epoch only
    >>> for input, target in skipped_dataloader:
    ...     optimizer.zero_grad()
    ...     output = model(input)
    ...     loss = loss_func(output, target)
    ...     accelerator.backward(loss)
    ...     optimizer.step()

    >>> # subsequent epochs
    >>> for input, target in dataloader:
    ...     optimizer.zero_grad()
    ...     ...

####
split_between_processes

< source >

( inputs: list | tuple | dict | torch.Tensor apply_padding: bool = False )

Parameters

- inputs (`list`, `tuple`, `torch.Tensor`, or `dict` of `list`/`tuple`/`torch.Tensor`) — The input to split between processes.
- apply_padding (`bool`, `optional`, defaults to `False`) — Whether to apply padding by repeating the last element of the input so that all processes have the same number of elements. Useful when trying to perform actions such as `Accelerator.gather()` on the outputs or passing in less inputs than there are processes. If so, just remember to drop the padded elements afterwards.

Splits `input` between `self.num_processes` quickly and can be then used on that process. Useful when doing distributed inference, such as with different prompts.

Note that when using a `dict`, all keys need to have the same number of elements.

Example:

    # Assume there are two processes
    from accelerate import Accelerator

    accelerator = Accelerator()
    with accelerator.split_between_processes(["A", "B", "C"]) as inputs:
        print(inputs)
    # Process 0
    ["A", "B"]
    # Process 1
    ["C"]

    with accelerator.split_between_processes(["A", "B", "C"], apply_padding=True) as inputs:
        print(inputs)
    # Process 0
    ["A", "B"]
    # Process 1
    ["C", "C"]

####
trigger_sync_in_backward

< source >

( model )

Parameters

- model (`torch.nn.Module`) — The model for which to trigger the gradient synchronization.

Trigger the sync of the gradients in the next backward pass of the model after multiple forward passes under `Accelerator.no_sync` (only applicable in multi-GPU scenarios).

If the script is not launched in distributed mode, this context manager does nothing.

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> dataloader, model, optimizer = accelerator.prepare(dataloader, model, optimizer)

    >>> with accelerator.no_sync():
    ...     loss_a = loss_func(model(input_a))  # first forward pass
    ...     loss_b = loss_func(model(input_b))  # second forward pass
    >>> accelerator.backward(loss_a)  # No synchronization across processes, only accumulate gradients
    >>> with accelerator.trigger_sync_in_backward(model):
    ...     accelerator.backward(loss_b)  # Synchronization across all processes
    >>> optimizer.step()
    >>> optimizer.zero_grad()

####
unscale_gradients

< source >

( optimizer = None )

Parameters

- optimizer (`torch.optim.Optimizer` or `list[torch.optim.Optimizer]`, optional) — The optimizer(s) for which to unscale gradients. If not set, will unscale gradients on all optimizers that were passed to prepare().

Unscale the gradients in mixed precision training with AMP. This is a noop in all other settings.

Likely should be called through Accelerator.clipgrad_norm() or Accelerator.clipgrad_value()

Example:

    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> model, optimizer = accelerator.prepare(model, optimizer)
    >>> outputs = model(inputs)
    >>> loss = loss_fn(outputs, labels)
    >>> accelerator.backward(loss)
    >>> accelerator.unscale_gradients(optimizer=optimizer)

####
unwrap_model

< source >

( model keep_fp32_wrapper: bool = True keep_torch_compile: bool = True ) →
`torch.nn.Module`

Parameters

- model (`torch.nn.Module`) — The model to unwrap.
- keep_fp32_wrapper (`bool`, optional, defaults to `True`) — Whether to not remove the mixed precision hook if it was added.
- keep_torch_compile (`bool`, optional, defaults to `True`) — Whether to not unwrap compiled model if compiled.

Returns

`torch.nn.Module`

The unwrapped model.

Unwraps the `model` from the additional layer possible added by prepare(). Useful before saving the model.

Example:

    >>> # Assuming two GPU processes
    >>> from torch.nn.parallel import DistributedDataParallel
    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> model = accelerator.prepare(MyModel())
    >>> print(model.__class__.__name__)
    DistributedDataParallel

    >>> model = accelerator.unwrap_model(model)
    >>> print(model.__class__.__name__)
    MyModel

####
verify_device_map

< source >

( model: torch.nn.Module )

Verifies that `model` has not been prepared with big model inference with a device-map resembling `auto`.

####
wait_for_everyone

< source >

( )

Will stop the execution of the current process until every other process has reached that point (so this does nothing when the script is only run in one process). Useful to do before saving a model.

Example:

    >>> # Assuming two GPU processes
    >>> import time
    >>> from accelerate import Accelerator

    >>> accelerator = Accelerator()
    >>> if accelerator.is_main_process:
    ...     time.sleep(2)
    >>> else:
    ...     print("I'm waiting for the main process to finish its sleep...")
    >>> accelerator.wait_for_everyone()
    >>> # Should print on every process at the same time
    >>> print("Everyone is here")

##
Utilities

####
accelerate.utils.gather_object

< source >

( object: typing.Any )

Parameters

- object (nested list/tuple/dictionary of picklable object) — The data to gather.

Recursively gather object in a nested list/tuple/dictionary of objects from all devices.

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