brk-code

accelerate の API リファレンス

accelerate (huggingface/accelerate) の公開 API 81 件 —— クラス 27、関数 26、メソッド 28。実際のソースを静的解析して抽出した正確なシグネチャを掲載しています。

リポジトリ: huggingface/accelerate

種別件数
クラス27
関数26
メソッド28

API 一覧

methodsrc.accelerate.accelerator.Accelerator.context_parallel_rank() -> int
Context parallelism is not supported yet.
methodsrc.accelerate.accelerator.Accelerator.is_main_process()
True for one process only.
methodsrc.accelerate.accelerator.Accelerator.lomo_backward(loss:torch.Tensor, learning_rate:float) -> None
Runs backward pass on LOMO optimizers.
methodsrc.accelerate.accelerator.Accelerator.pipeline_parallel_rank() -> int
Pipeline parallelism is not supported yet.
methodsrc.accelerate.accelerator.Accelerator.tensor_parallel_rank() -> int
Returns the local rank for tensor parallelism.
classsrc.accelerate.commands.menu.input.KeyHandler
Metaclass that adds the key handlers to the class
funcsrc.accelerate.commands.menu.keymap.get_raw_chars()
Gets raw characters from inputs
funcsrc.accelerate.hooks.add_hook_to_module(module:nn.Module, hook:ModelHook, append:bool=False)
Adds a hook to a given module.
classsrc.accelerate.local_sgd.LocalSGD
A helper class to support local SGD on top of Accelerator.
classsrc.accelerate.logging.MultiProcessAdapter
An adapter to assist with logging in multiprocess.
classsrc.accelerate.optimizer.AcceleratedOptimizer
Internal wrapper around a torch optimizer.
methodsrc.accelerate.optimizer.AcceleratedOptimizer.eval()
Sets the optimizer to "eval" mode.
classsrc.accelerate.tracking.AimTracker
A `Tracker` class that supports `aim`.
methodsrc.accelerate.tracking.AimTracker.finish()
Closes `aim` writer
methodsrc.accelerate.tracking.AimTracker.log(values:dict, step:Optional[int]=None, **kwargs)
Logs `values` to the current run.
classsrc.accelerate.tracking.ClearMLTracker
A `Tracker` class that supports `clearml`.
methodsrc.accelerate.tracking.ClearMLTracker.finish()
Close the ClearML task.
methodsrc.accelerate.tracking.ClearMLTracker.log_images(values:dict, step:Optional[int]=None, **kwargs)
Logs `images` to the current run.
classsrc.accelerate.tracking.CometMLTracker
A `Tracker` class that supports `comet_ml`.
methodsrc.accelerate.tracking.CometMLTracker.finish()
Flush `comet-ml` writer
methodsrc.accelerate.tracking.CometMLTracker.log(values:dict, step:Optional[int]=None, **kwargs)
Logs `values` to the current run.
classsrc.accelerate.tracking.DVCLiveTracker
A `Tracker` class that supports `dvclive`.
methodsrc.accelerate.tracking.DVCLiveTracker.finish()
Closes `dvclive.Live()`.
methodsrc.accelerate.tracking.DVCLiveTracker.log(values:dict, step:Optional[int]=None, **kwargs)
Logs `values` to the current run.
classsrc.accelerate.tracking.MLflowTracker
A `Tracker` class that supports `mlflow`.
methodsrc.accelerate.tracking.MLflowTracker.finish()
End the active MLflow run.
methodsrc.accelerate.tracking.MLflowTracker.log(values:dict, step:Optional[int]=None, **kwargs)
Logs `values` to the current run.
methodsrc.accelerate.tracking.MLflowTracker.log_figure(figure:Any, artifact_file:str, **save_kwargs)
Logs an figure to the current run.
classsrc.accelerate.tracking.SwanLabTracker
A `Tracker` class that supports `swanlab`.
methodsrc.accelerate.tracking.SwanLabTracker.finish()
Closes `swanlab` writer
methodsrc.accelerate.tracking.SwanLabTracker.log(values:dict, step:Optional[int]=None, **kwargs)
Logs `values` to the current run.
methodsrc.accelerate.tracking.SwanLabTracker.log_images(values:dict, step:Optional[int]=None, **kwargs)
Logs `images` to the current run.
classsrc.accelerate.tracking.TensorBoardTracker
A `Tracker` class that supports `tensorboard`.
methodsrc.accelerate.tracking.TensorBoardTracker.finish()
Closes `TensorBoard` writer
methodsrc.accelerate.tracking.TensorBoardTracker.log(values:dict, step:Optional[int]=None, **kwargs)
Logs `values` to the current run.
methodsrc.accelerate.tracking.TensorBoardTracker.log_images(values:dict, step:Optional[int]=None, **kwargs)
Logs `images` to the current run.
classsrc.accelerate.tracking.TrackioTracker
A `Tracker` class that supports `trackio`.
methodsrc.accelerate.tracking.TrackioTracker.finish()
Closes `trackio` run
methodsrc.accelerate.tracking.TrackioTracker.log(values:dict, step:Optional[int]=None, **kwargs)
Logs `values` to the current run.
classsrc.accelerate.tracking.WandBTracker
A `Tracker` class that supports `wandb`.
methodsrc.accelerate.tracking.WandBTracker.finish()
Closes `wandb` writer
methodsrc.accelerate.tracking.WandBTracker.log(values:dict, step:Optional[int]=None, **kwargs)
Logs `values` to the current run.
methodsrc.accelerate.tracking.WandBTracker.log_images(values:dict, step:Optional[int]=None, **kwargs)
Logs `images` to the current run.
classsrc.accelerate.utils.dataclasses.ComputeEnvironment
Represents a type of the compute environment.
classsrc.accelerate.utils.dataclasses.DeepSpeedPlugin
This plugin is used to integrate DeepSpeed.
classsrc.accelerate.utils.dataclasses.DistributedType
Represents a type of distributed environment.
classsrc.accelerate.utils.dataclasses.FP8BackendType
Represents the backend used for FP8.
classsrc.accelerate.utils.dataclasses.FP8RecipeKwargs
Deprecated.
classsrc.accelerate.utils.dataclasses.SageMakerDistributedType
Represents a type of distributed environment.
classsrc.accelerate.utils.deepspeed.DeepSpeedOptimizerWrapper
Internal wrapper around a deepspeed optimizer.
classsrc.accelerate.utils.deepspeed.DeepSpeedSchedulerWrapper
Internal wrapper around a deepspeed scheduler.
funcsrc.accelerate.utils.environment.set_numa_affinity(local_process_index:int, verbose:Optional[bool]=None) -> None
Assigns the current process to a specific NUMA node.
funcsrc.accelerate.utils.fsdp_utils.fsdp2_prepare_model(accelerator, model:torch.nn.Module) -> torch.nn.Module
Prepares the model for FSDP2 in-place.
funcsrc.accelerate.utils.imports.is_fp16_available()
Checks if fp16 is supported
funcsrc.accelerate.utils.imports.is_fp8_available()
Checks if fp8 is supported
funcsrc.accelerate.utils.launch.setup_fp8_env(args:argparse.Namespace, current_env:dict[str, str])
Setup the FP8 environment variables.
classsrc.accelerate.utils.megatron_lm.AbstractTrainStep
Abstract class for batching, forward pass and loss handler.
classsrc.accelerate.utils.megatron_lm.BertTrainStep
Bert train step class.
funcsrc.accelerate.utils.megatron_lm.BertTrainStep.forward_step(data_iterator, model)
Forward step.
funcsrc.accelerate.utils.megatron_lm.BertTrainStep.get_batch_megatron(data_iterator)
Build the batch.
funcsrc.accelerate.utils.megatron_lm.BertTrainStep.get_batch_transformer(data_iterator)
Build the batch.
classsrc.accelerate.utils.megatron_lm.GPTTrainStep
GPT train step class.
funcsrc.accelerate.utils.megatron_lm.GPTTrainStep.forward_step(data_iterator, model)
Forward step.
funcsrc.accelerate.utils.megatron_lm.GPTTrainStep.get_batch_megatron(data_iterator)
Generate a batch
classsrc.accelerate.utils.megatron_lm.T5TrainStep
T5 train step class.
funcsrc.accelerate.utils.megatron_lm.T5TrainStep.forward_step(data_iterator, model)
Forward step.
funcsrc.accelerate.utils.megatron_lm.T5TrainStep.get_batch_megatron(data_iterator)
Build the batch.
funcsrc.accelerate.utils.megatron_lm.T5TrainStep.get_batch_transformer(data_iterator)
Build the batch.
funcsrc.accelerate.utils.modeling.find_tied_parameters(model:torch.nn.Module, **kwargs) -> list[list[str]]
Find the tied parameters in a given model.
funcsrc.accelerate.utils.modeling.id_tensor_storage(tensor:torch.Tensor) -> tuple[torch.device, int, int]
Unique identifier to a tensor storage.
classsrc.accelerate.utils.offload.PrefixedDataset
Will access keys in a given dataset by adding a prefix.
classsrc.accelerate.utils.operations.DistributedOperationException
An exception class for distributed operations.
funcsrc.accelerate.utils.other.get_free_port() -> int
Gets a free port on `localhost`.
funcsrc.accelerate.utils.other.get_pretty_name(obj)
Gets a pretty name from `obj`.
funcsrc.accelerate.utils.other.has_repeated_blocks(module:torch.nn.Module) -> bool
Check whether the module has repeated blocks, i.e.
funcsrc.accelerate.utils.other.is_compiled_module(module:torch.nn.Module) -> bool
Check whether the module was compiled with torch.compile()
funcsrc.accelerate.utils.other.is_port_in_use(port:Optional[int]=None) -> bool
Checks if a port is in use on `localhost`.
funcsrc.accelerate.utils.other.is_repeated_blocks(module:torch.nn.Module) -> bool
Check whether the module is a repeated block, i.e.
funcsrc.accelerate.utils.other.model_has_dtensor(model:torch.nn.Module) -> bool
Check if the model has DTensor parameters.
funcsrc.accelerate.utils.other.recursive_getattr(obj, attr:str)
Recursive `getattr`.
funcsrc.accelerate.utils.other.save(obj, f, save_on_each_node:bool=False, safe_serialization:bool=False)
Save the data to disk.

この情報について

掲載しているシグネチャは huggingface/accelerate の公開ソースコードを Python の ast モジュールで静的解析し、引数名・デフォルト値・ 型注釈・戻り値型をそのまま抽出したものです。実装コードは保存していません。 詳しくは仕組みの解説をご覧ください。

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