transformers/main/ko/main_classes/trainer.md

Trainer [[trainer]]

Trainer 클래스는 PyTorch에서 완전한 기능(feature-complete)의 훈련을 위한 API를 제공하며, 다중 GPU/TPU에서의 분산 훈련, NVIDIA GPU, AMD GPU를 위한 혼합 정밀도, 그리고 PyTorch의 torch.amp를 지원합니다. Trainer는 모델의 훈련 방식을 커스터마이즈할 수 있는 다양한 옵션을 제공하는 TrainingArguments 클래스와 함께 사용됩니다. 이 두 클래스는 함께 완전한 훈련 API를 제공합니다.

Seq2SeqTrainer와 Seq2SeqTrainingArguments는 Trainer와 TrainingArguments 클래스를 상속하며, 요약이나 번역과 같은 시퀀스-투-시퀀스 작업을 위한 모델 훈련에 적합하게 조정되어 있습니다.

Trainer 클래스는 🤗 Transformers 모델에 최적화되어 있으며, 다른 모델과 함께 사용될 때 예상치 못한 동작을 하게 될 수 있습니다. 자신만의 모델을 사용할 때는 다음을 확인하세요:

• 모델은 항상 튜플이나 ModelOutput의 서브클래스를 반환해야 합니다.
• 모델은 labels 인자가 제공되면 손실을 계산할 수 있고, 모델이 튜플을 반환하는 경우 그 손실이 튜플의 첫 번째 요소로 반환되어야 합니다.
• 모델은 여러 개의 레이블 인자를 수용할 수 있어야 하며, Trainer에게 이름을 알리기 위해 TrainingArguments에서 label_names를 사용하지만, 그 중 어느 것도 "label"로 명명되어서는 안 됩니다.

Trainer [[transformers.Trainer]][[transformers.Trainer]]

transformers.Trainer[[transformers.Trainer]]

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Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers.

Important attributes:

• model -- Always points to the core model. If using a transformers model, it will be a PreTrainedModel subclass.
• model_wrapped -- Always points to the most external model in case one or more other modules wrap the original model. This is the model that should be used for the forward pass. For example, under DeepSpeed, the inner model is wrapped in DeepSpeed and then again in torch.nn.DistributedDataParallel. If the inner model hasn't been wrapped, then self.model_wrapped is the same as self.model.
• is_model_parallel -- Whether or not a model has been switched to a model parallel mode (different from data parallelism, this means some of the model layers are split on different GPUs).
• place_model_on_device -- Whether or not to automatically place the model on the device. Defaults to True unless model parallel, DeepSpeed, FSDP, full fp16/bf16 eval, or SageMaker MP is active. Can be overridden by subclassing TrainingArguments and overriding the place_model_on_device property.
• is_in_train -- Whether or not a model is currently running train (e.g. when evaluate is called while in train)

add_callbacktransformers.Trainer.add_callbackhttps://github.com/huggingface/transformers/blob/main/src/transformers/trainer.py#L4350[{"name": "callback", "val": ": type[transformers.trainer_callback.TrainerCallback] | transformers.trainer_callback.TrainerCallback"}]- callback (type or [~transformers.TrainerCallback]) -- A TrainerCallback class or an instance of a TrainerCallback. In the first case, will instantiate a member of that class.0

Add a callback to the current list of TrainerCallback.

Parameters:

model (PreTrainedModel or torch.nn.Module, optional) : The model to train, evaluate or use for predictions. If not provided, a model_init must be passed. Trainer is optimized to work with the PreTrainedModel provided by the library. You can still use your own models defined as torch.nn.Module as long as they work the same way as the 🤗 Transformers models.

args (TrainingArguments, optional) : The arguments to tweak for training. Will default to a basic instance of TrainingArguments with the output_dir set to a directory named tmp_trainer in the current directory if not provided.

data_collator (DataCollator, optional) : The function to use to form a batch from a list of elements of train_dataset or eval_dataset. Will default to default_data_collator() if no processing_class is provided, an instance of DataCollatorWithPadding otherwise if the processing_class is a feature extractor or tokenizer.

train_dataset (torch.utils.data.Dataset | torch.utils.data.IterableDataset | datasets.Dataset, optional) : The dataset to use for training. If it is a Dataset, columns not accepted by the model.forward() method are automatically removed. Note that if it's a torch.utils.data.IterableDataset with some randomization and you are training in a distributed fashion, your iterable dataset should either use a internal attribute generator that is a torch.Generator for the randomization that must be identical on all processes (and the Trainer will manually set the seed of this generator at each epoch) or have a set_epoch() method that internally sets the seed of the RNGs used.

eval_dataset (torch.utils.data.Dataset | dict[str, torch.utils.data.Dataset] | datasets.Dataset, optional) : The dataset to use for evaluation. If it is a Dataset, columns not accepted by the model.forward() method are automatically removed. If it is a dictionary, it will evaluate on each dataset prepending the dictionary key to the metric name.

processing_class (PreTrainedTokenizerBase or BaseImageProcessor or FeatureExtractionMixin or ProcessorMixin, optional) : Processing class used to process the data. If provided, will be used to automatically process the inputs for the model, and it will be saved along the model to make it easier to rerun an interrupted training or reuse the fine-tuned model.

model_init (Callable[[], PreTrainedModel], optional) : A function that instantiates the model to be used. If provided, each call to train() will start from a new instance of the model as given by this function. The function may have zero argument, or a single one containing the optuna/Ray Tune trial object, to be able to choose different architectures according to hyperparameters (such as layer count, sizes of inner layers, dropout probabilities etc).

compute_loss_func (Callable, optional) : A function that accepts the raw model outputs, labels, and the number of items in the entire accumulated batch (batch_size * gradient_accumulation_steps) and returns the loss. For example, see the default loss function used by Trainer.

compute_metrics (Callable[[EvalPrediction], Dict], optional) : The function that will be used to compute metrics at evaluation. Must take a EvalPrediction and return a dictionary string to metric values. Note When passing TrainingArgs with batch_eval_metrics set to True, your compute_metrics function must take a boolean compute_result argument. This will be triggered after the last eval batch to signal that the function needs to calculate and return the global summary statistics rather than accumulating the batch-level statistics

callbacks (List of TrainerCallback, optional) : A list of callbacks to customize the training loop. Will add those to the list of default callbacks detailed in here. If you want to remove one of the default callbacks used, use the Trainer.remove_callback() method.

optimizers (tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR], optional, defaults to (None, None)) : A tuple containing the optimizer and the scheduler to use. Will default to an instance of AdamW on your model and a scheduler given by get_linear_schedule_with_warmup() controlled by args.

optimizer_cls_and_kwargs (tuple[Type[torch.optim.Optimizer], dict[str, Any]], optional) : A tuple containing the optimizer class and keyword arguments to use. Overrides optim and optim_args in args. Incompatible with the optimizers argument. Unlike optimizers, this argument avoids the need to place model parameters on the correct devices before initializing the Trainer.

preprocess_logits_for_metrics (Callable[[torch.Tensor, torch.Tensor], torch.Tensor], optional) : A function that preprocess the logits right before caching them at each evaluation step. Must take two tensors, the logits and the labels, and return the logits once processed as desired. The modifications made by this function will be reflected in the predictions received by compute_metrics. Note that the labels (second parameter) will be None if the dataset does not have them.

autocast_smart_context_manager[[transformers.Trainer.autocast_smart_context_manager]]

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A helper wrapper that creates an appropriate context manager for autocast while feeding it the desired arguments, depending on the situation. We rely on accelerate for autocast, hence we do nothing here.

call_model_init[[transformers.Trainer.call_model_init]]

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Invoke model_init to get a fresh model instance, optionally conditioned on a hyperparameter trial.

compute_loss[[transformers.Trainer.compute_loss]]

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How the loss is computed by Trainer. By default, all models return the loss in the first element.

Subclass and override for custom behavior. If you are not using num_items_in_batch when computing your loss, make sure to overwrite self.model_accepts_loss_kwargs to False. Otherwise, the loss calculation might be slightly inaccurate when performing gradient accumulation.

Parameters:

model (nn.Module) : The model to compute the loss for.

inputs (dict[str, torch.Tensor | Any]) : The input data for the model.

return_outputs (bool, optional, defaults to False) : Whether to return the model outputs along with the loss.

num_items_in_batch (Optional[torch.Tensor], optional) : The number of items in the batch. If not passed, the loss is computed using the default batch size reduction logic.

Returns:

The loss of the model along with its output if return_outputs was set to True

compute_loss_context_manager[[transformers.Trainer.compute_loss_context_manager]]

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A helper wrapper to group together context managers.

create_accelerator_and_postprocess[[transformers.Trainer.create_accelerator_and_postprocess]]

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Create the accelerator and perform post-creation setup (FSDP, DeepSpeed, etc.).

create_model_card[[transformers.Trainer.create_model_card]]

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Creates a draft of a model card using the information available to the Trainer.

Parameters:

language (str, optional) : The language of the model (if applicable)

license (str, optional) : The license of the model. Will default to the license of the pretrained model used, if the original model given to the Trainer comes from a repo on the Hub.

tags (str or list[str], optional) : Some tags to be included in the metadata of the model card.

model_name (str, optional) : The name of the model.

finetuned_from (str, optional) : The name of the model used to fine-tune this one (if applicable). Will default to the name of the repo of the original model given to the Trainer (if it comes from the Hub).

tasks (str or list[str], optional) : One or several task identifiers, to be included in the metadata of the model card.

dataset_tags (str or list[str], optional) : One or several dataset tags, to be included in the metadata of the model card.

dataset (str or list[str], optional) : One or several dataset identifiers, to be included in the metadata of the model card.

dataset_args (str or list[str], optional) : One or several dataset arguments, to be included in the metadata of the model card.

create_optimizer[[transformers.Trainer.create_optimizer]]

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Setup the optimizer.

We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the Trainer's init through optimizers, or subclass and override this method in a subclass.

Returns:

torch.optim.Optimizer

The optimizer instance.

create_optimizer_and_scheduler[[transformers.Trainer.create_optimizer_and_scheduler]]

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Setup the optimizer and the learning rate scheduler.

We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the Trainer's init through optimizers, or subclass and override this method (or create_optimizer and/or create_scheduler) in a subclass.

create_scheduler[[transformers.Trainer.create_scheduler]]

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Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or passed as an argument.

Parameters:

num_training_steps (int) : The number of training steps to do.

Returns:

torch.optim.lr_scheduler.LRScheduler

The learning rate scheduler instance.

evaluate[[transformers.Trainer.evaluate]]

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Run evaluation and returns metrics.

The calling script will be responsible for providing a method to compute metrics, as they are task-dependent (pass it to the init compute_metrics argument).

You can also subclass and override this method to inject custom behavior.

Parameters:

eval_dataset (Dataset | dict[str, Dataset], optional) : Pass a dataset if you wish to override self.eval_dataset. If it is a Dataset, columns not accepted by the model.forward() method are automatically removed. If it is a dictionary, it will evaluate on each dataset, prepending the dictionary key to the metric name. Datasets must implement the __len__ method. If you pass a dictionary with names of datasets as keys and datasets as values, evaluate will run separate evaluations on each dataset. This can be useful to monitor how training affects other datasets or simply to get a more fine-grained evaluation. When used with load_best_model_at_end, make sure metric_for_best_model references exactly one of the datasets. If you, for example, pass in {"data1": data1, "data2": data2} for two datasets data1 and data2, you could specify metric_for_best_model="eval_data1_loss" for using the loss on data1 and metric_for_best_model="eval_data2_loss" for the loss on data2.

ignore_keys (list[str], optional) : A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions.

metric_key_prefix (str, optional, defaults to "eval") : An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named "eval_bleu" if the prefix is "eval" (default)

Returns:

A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The dictionary also contains the epoch number which comes from the training state.

evaluation_loop[[transformers.Trainer.evaluation_loop]]

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Prediction/evaluation loop, shared by Trainer.evaluate() and Trainer.predict().

Works both with or without labels.

floating_point_ops[[transformers.Trainer.floating_point_ops]]

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For models that inherit from PreTrainedModel, uses that method to compute the number of floating point operations for every backward + forward pass. If using another model, either implement such a method in the model or subclass and override this method.

Parameters:

inputs (dict[str, torch.Tensor | Any]) : The inputs and targets of the model.

Returns:

int

The number of floating-point operations.

get_batch_samples[[transformers.Trainer.get_batch_samples]]

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Collects a specified number of batches from the epoch iterator and optionally counts the number of items in the batches to properly scale the loss.

get_cp_size[[transformers.Trainer.get_cp_size]]

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Get the context parallel size

get_decay_parameter_names[[transformers.Trainer.get_decay_parameter_names]]

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Get all parameter names that weight decay will be applied to.

This function filters out parameters in two ways:

1. By layer type (instances of layers specified in ALL_LAYERNORM_LAYERS)
2. By parameter name patterns (containing 'bias', or variation of 'norm')

get_eval_dataloader[[transformers.Trainer.get_eval_dataloader]]

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Returns the evaluation ~torch.utils.data.DataLoader.

Subclass and override this method if you want to inject some custom behavior.

Parameters:

eval_dataset (str or torch.utils.data.Dataset, optional) : If a str, will use self.eval_dataset[eval_dataset] as the evaluation dataset. If a Dataset, will override self.eval_dataset and must implement __len__. If it is a Dataset, columns not accepted by the model.forward() method are automatically removed.

get_learning_rates[[transformers.Trainer.get_learning_rates]]

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Returns the learning rate of each parameter from self.optimizer.

get_num_trainable_parameters[[transformers.Trainer.get_num_trainable_parameters]]

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Get the number of trainable parameters.

get_optimizer_cls_and_kwargs[[transformers.Trainer.get_optimizer_cls_and_kwargs]]

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Returns the optimizer class and optimizer parameters based on the training arguments.

Parameters:

args (transformers.training_args.TrainingArguments) : The training arguments for the training session.

model (PreTrainedModel, optional) : The model being trained. Required for some optimizers (GaLore, Apollo, LOMO).

Returns:

A tuple containing the optimizer class and a dictionary of optimizer keyword arguments.

get_optimizer_group[[transformers.Trainer.get_optimizer_group]]

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Returns optimizer group for a parameter if given, else returns all optimizer groups for params.

Parameters:

param (str or torch.nn.parameter.Parameter, optional) : The parameter for which optimizer group needs to be returned.

get_sp_size[[transformers.Trainer.get_sp_size]]

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Get the sequence parallel size

get_test_dataloader[[transformers.Trainer.get_test_dataloader]]

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Returns the test ~torch.utils.data.DataLoader.

Subclass and override this method if you want to inject some custom behavior.

Parameters:

test_dataset (torch.utils.data.Dataset, optional) : The test dataset to use. If it is a Dataset, columns not accepted by the model.forward() method are automatically removed. It must implement __len__.

get_total_train_batch_size[[transformers.Trainer.get_total_train_batch_size]]

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Calculates total batch size (micro_batch * grad_accum * dp_world_size).

Accounts for all parallelism dimensions: TP, CP, and SP.

Formula: dp_world_size = world_size // (tp_size * cp_size * sp_size)

Where:

• TP (Tensor Parallelism): Model layers split across GPUs
• CP (Context Parallelism): Sequences split using Ring Attention (FSDP2)
• SP (Sequence Parallelism): Sequences split using ALST/Ulysses (DeepSpeed)

All dimensions are separate and multiplicative: world_size = dp_size * tp_size * cp_size * sp_size

get_tp_size[[transformers.Trainer.get_tp_size]]

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Get the tensor parallel size from either the model or DeepSpeed config.

get_train_dataloader[[transformers.Trainer.get_train_dataloader]]

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Returns the training ~torch.utils.data.DataLoader.

Will use no sampler if train_dataset does not implement __len__, a random sampler (adapted to distributed training if necessary) otherwise.

Subclass and override this method if you want to inject some custom behavior.

hyperparameter_search[[transformers.Trainer.hyperparameter_search]]

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Launch a hyperparameter search using optuna or Ray Tune. The optimized quantity is determined by compute_objective, which defaults to a function returning the evaluation loss when no metric is provided, the sum of all metrics otherwise.

To use this method, you need to have provided a model_init when initializing your Trainer: we need to reinitialize the model at each new run. This is incompatible with the optimizers argument, so you need to subclass Trainer and override the method create_optimizer_and_scheduler() for custom optimizer/scheduler.

Parameters:

hp_space (Callable[["optuna.Trial"], dict[str, float]], optional) : A function that defines the hyperparameter search space. Will default to default_hp_space_optuna() or default_hp_space_ray() depending on your backend.

compute_objective (Callable[[dict[str, float]], float], optional) : A function computing the objective to minimize or maximize from the metrics returned by the evaluate method. Will default to default_compute_objective().

n_trials (int, optional, defaults to 100) : The number of trial runs to test.

direction (str or list[str], optional, defaults to "minimize") : If it's single objective optimization, direction is str, can be "minimize" or "maximize", you should pick "minimize" when optimizing the validation loss, "maximize" when optimizing one or several metrics. If it's multi objectives optimization, direction is list[str], can be List of "minimize" and "maximize", you should pick "minimize" when optimizing the validation loss, "maximize" when optimizing one or several metrics.

backend (str or ~training_utils.HPSearchBackend, optional) : The backend to use for hyperparameter search. Will default to optuna or Ray Tune, depending on which one is installed. If all are installed, will default to optuna.

hp_name (Callable[["optuna.Trial"], str]], optional) : A function that defines the trial/run name. Will default to None.

kwargs (dict[str, Any], optional) : Additional keyword arguments for each backend: - optuna: parameters from optuna.study.create_study and also the parameters timeout, n_jobs and gc_after_trial from optuna.study.Study.optimize - ray: parameters from tune.run. If resources_per_trial is not set in the kwargs, it defaults to 1 CPU core and 1 GPU (if available). If progress_reporter is not set in the kwargs, ray.tune.CLIReporter is used.

Returns:

[trainer_utils.BestRunorlist[trainer_utils.BestRun]]

All the information about the best run or best runs for multi-objective optimization. Experiment summary can be found in run_summary attribute for Ray backend.

init_hf_repo[[transformers.Trainer.init_hf_repo]]

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Initializes a git repo in self.args.hub_model_id.

is_local_process_zero[[transformers.Trainer.is_local_process_zero]]

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Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several machines) main process.

is_world_process_zero[[transformers.Trainer.is_world_process_zero]]

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Whether or not this process is the global main process (when training in a distributed fashion on several machines, this is only going to be True for one process).

log[[transformers.Trainer.log]]

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Log logs on the various objects watching training.

Subclass and override this method to inject custom behavior.

Parameters:

logs (dict[str, float]) : The values to log.

start_time (Optional[float]) : The start of training.

log_metrics[[transformers.Trainer.log_metrics]]

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Log metrics in a specially formatted way.

Under distributed environment this is done only for a process with rank 0.

Notes on memory reports:

In order to get memory usage report you need to install psutil. You can do that with pip install psutil.

Now when this method is run, you will see a report that will include:

init_mem_cpu_alloc_delta   =     1301MB
init_mem_cpu_peaked_delta  =      154MB
init_mem_gpu_alloc_delta   =      230MB
init_mem_gpu_peaked_delta  =        0MB
train_mem_cpu_alloc_delta  =     1345MB
train_mem_cpu_peaked_delta =        0MB
train_mem_gpu_alloc_delta  =      693MB
train_mem_gpu_peaked_delta =        7MB

Understanding the reports:

• the first segment, e.g., train__, tells you which stage the metrics are for. Reports starting with init_ will be added to the first stage that gets run. So that if only evaluation is run, the memory usage for the __init__ will be reported along with the eval_ metrics.
• the third segment, is either cpu or gpu, tells you whether it's the general RAM or the gpu0 memory metric.
• *_alloc_delta - is the difference in the used/allocated memory counter between the end and the start of the stage - it can be negative if a function released more memory than it allocated.
• *_peaked_delta - is any extra memory that was consumed and then freed - relative to the current allocated memory counter - it is never negative. When you look at the metrics of any stage you add up alloc_delta + peaked_delta and you know how much memory was needed to complete that stage.

The reporting happens only for process of rank 0 and gpu 0 (if there is a gpu). Typically this is enough since the main process does the bulk of work, but it could be not quite so if model parallel is used and then other GPUs may use a different amount of gpu memory. This is also not the same under DataParallel where gpu0 may require much more memory than the rest since it stores the gradient and optimizer states for all participating GPUs. Perhaps in the future these reports will evolve to measure those too.

The CPU RAM metric measures RSS (Resident Set Size) includes both the memory which is unique to the process and the memory shared with other processes. It is important to note that it does not include swapped out memory, so the reports could be imprecise.

The CPU peak memory is measured using a sampling thread. Due to python's GIL it may miss some of the peak memory if that thread didn't get a chance to run when the highest memory was used. Therefore this report can be less than reality. Using tracemalloc would have reported the exact peak memory, but it doesn't report memory allocations outside of python. So if some C++ CUDA extension allocated its own memory it won't be reported. And therefore it was dropped in favor of the memory sampling approach, which reads the current process memory usage.

The GPU allocated and peak memory reporting is done with torch.cuda.memory_allocated() and torch.cuda.max_memory_allocated(). This metric reports only "deltas" for pytorch-specific allocations, as torch.cuda memory management system doesn't track any memory allocated outside of pytorch. For example, the very first cuda call typically loads CUDA kernels, which may take from 0.5 to 2GB of GPU memory.

Note that this tracker doesn't account for memory allocations outside of Trainer's __init__, train, evaluate and predict calls.

Because evaluation calls may happen during train, we can't handle nested invocations because torch.cuda.max_memory_allocated is a single counter, so if it gets reset by a nested eval call, train's tracker will report incorrect info. If this pytorch issue gets resolved it will be possible to change this class to be re-entrant. Until then we will only track the outer level of train, evaluate and predict methods. Which means that if eval is called during train, it's the latter that will account for its memory usage and that of the former.

This also means that if any other tool that is used along the Trainer calls torch.cuda.reset_peak_memory_stats, the gpu peak memory stats could be invalid. And the Trainer will disrupt the normal behavior of any such tools that rely on calling torch.cuda.reset_peak_memory_stats themselves.

For best performance you may want to consider turning the memory profiling off for production runs.

Parameters:

split (str) : Mode/split name: one of train, eval, test

metrics (dict[str, float]) : The metrics returned from train/evaluate/predictmetrics: metrics dict

metrics_format[[transformers.Trainer.metrics_format]]

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Reformat Trainer metrics values to a human-readable format.

Parameters:

metrics (dict[str, float]) : The metrics returned from train/evaluate/predict

Returns:

metrics (dict[str, float])

The reformatted metrics

num_examples[[transformers.Trainer.num_examples]]

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Helper to get number of samples in a ~torch.utils.data.DataLoader by accessing its dataset. When dataloader.dataset does not exist or has no length, estimates as best it can

pop_callback[[transformers.Trainer.pop_callback]]

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Remove a callback from the current list of TrainerCallback and returns it.

If the callback is not found, returns None (and no error is raised).

Parameters:

callback (type or [~transformers.TrainerCallback]) : A TrainerCallback class or an instance of a TrainerCallback. In the first case, will pop the first member of that class found in the list of callbacks.

Returns:

[TrainerCallback](/docs/transformers/main/ko/main_classes/callback#transformers.TrainerCallback)

The callback removed, if found.

predict[[transformers.Trainer.predict]]

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Run prediction and returns predictions and potential metrics.

Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method will also return metrics, like in evaluate().

If your predictions or labels have different sequence length (for instance because you're doing dynamic padding in a token classification task) the predictions will be padded (on the right) to allow for concatenation into one array. The padding index is -100.

Returns: NamedTuple A namedtuple with the following keys:

• predictions (np.ndarray): The predictions on test_dataset.
• label_ids (np.ndarray, optional): The labels (if the dataset contained some).
• metrics (dict[str, float], optional): The potential dictionary of metrics (if the dataset contained labels).

Parameters:

test_dataset (Dataset) : Dataset to run the predictions on. If it is an datasets.Dataset, columns not accepted by the model.forward() method are automatically removed. Has to implement the method __len__

ignore_keys (list[str], optional) : A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions.

metric_key_prefix (str, optional, defaults to "test") : An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named "test_bleu" if the prefix is "test" (default)

prediction_step[[transformers.Trainer.prediction_step]]

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Perform an evaluation step on model using inputs.

Subclass and override to inject custom behavior.

Parameters:

model (nn.Module) : The model to evaluate.

inputs (dict[str, torch.Tensor | Any]) : The inputs and targets of the model. The dictionary will be unpacked before being fed to the model. Most models expect the targets under the argument labels. Check your model's documentation for all accepted arguments.

prediction_loss_only (bool) : Whether or not to return the loss only.

ignore_keys (list[str], optional) : A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions.

Returns:

tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]

A tuple with the loss, logits and labels (each being optional).

push_to_hub[[transformers.Trainer.push_to_hub]]

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Upload self.model and self.processing_class to the 🤗 model hub on the repo self.args.hub_model_id.

Parameters:

commit_message (str, optional, defaults to "End of training") : Message to commit while pushing.

blocking (bool, optional, defaults to True) : Whether the function should return only when the git push has finished.

token (str, optional, defaults to None) : Token with write permission to overwrite Trainer's original args.

revision (str, optional) : The git revision to commit from. Defaults to the head of the "main" branch.

kwargs (dict[str, Any], optional) : Additional keyword arguments passed along to create_model_card().

Returns:

The URL of the repository where the model was pushed if blocking=False, or a Future object tracking the progress of the commit if blocking=True.

remove_callback[[transformers.Trainer.remove_callback]]

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Remove a callback from the current list of TrainerCallback.

Parameters:

callback (type or [~transformers.TrainerCallback]) : A TrainerCallback class or an instance of a TrainerCallback. In the first case, will remove the first member of that class found in the list of callbacks.

save_metrics[[transformers.Trainer.save_metrics]]

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Save metrics into a json file for that split, e.g. train_results.json.

Under distributed environment this is done only for a process with rank 0.

To understand the metrics please read the docstring of log_metrics(). The only difference is that raw unformatted numbers are saved in the current method.

Parameters:

split (str) : Mode/split name: one of train, eval, test, all

metrics (dict[str, float]) : The metrics returned from train/evaluate/predict

combined (bool, optional, defaults to True) : Creates combined metrics by updating all_results.json with metrics of this call

save_model[[transformers.Trainer.save_model]]

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Will save the model, so you can reload it using from_pretrained().

Will only save from the main process.

save_state[[transformers.Trainer.save_state]]

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Saves the Trainer state, since Trainer.save_model saves only the tokenizer with the model.

Under distributed environment this is done only for a process with rank 0.

set_initial_training_values[[transformers.Trainer.set_initial_training_values]]

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Calculates and returns the following values:

• num_train_epochs
• num_update_steps_per_epoch
• num_examples
• num_train_samples
• total_train_batch_size
• steps_in_epoch (total batches per epoch)
• max_steps

store_flos[[transformers.Trainer.store_flos]]

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Store the number of floating-point operations that went into the model.

train[[transformers.Trainer.train]]

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Main training entry point.

Parameters:

resume_from_checkpoint (str or bool, optional) : If a str, local path to a saved checkpoint as saved by a previous instance of Trainer. If a bool and equals True, load the last checkpoint in args.output_dir as saved by a previous instance of Trainer. If present, training will resume from the model/optimizer/scheduler states loaded here.

trial (optuna.Trial or dict[str, Any], optional) : The trial run or the hyperparameter dictionary for hyperparameter search.

ignore_keys_for_eval (list[str], optional) : A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions for evaluation during the training.

Returns:

TrainOutput

Object containing the global step count, training loss, and metrics.

training_step[[transformers.Trainer.training_step]]

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Perform a training step on a batch of inputs.

Subclass and override to inject custom behavior.

Parameters:

model (nn.Module) : The model to train.

inputs (dict[str, torch.Tensor | Any]) : The inputs and targets of the model. The dictionary will be unpacked before being fed to the model. Most models expect the targets under the argument labels. Check your model's documentation for all accepted arguments.

Returns:

torch.Tensor

The tensor with training loss on this batch.

Seq2SeqTrainer [[transformers.Seq2SeqTrainer]][[transformers.Seq2SeqTrainer]]

transformers.Seq2SeqTrainer[[transformers.Seq2SeqTrainer]]

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evaluatetransformers.Seq2SeqTrainer.evaluatehttps://github.com/huggingface/transformers/blob/main/src/transformers/trainer_seq2seq.py#L139[{"name": "eval_dataset", "val": ": torch.utils.data.dataset.Dataset | None = None"}, {"name": "ignore_keys", "val": ": list[str] | None = None"}, {"name": "metric_key_prefix", "val": ": str = 'eval'"}, {"name": "**gen_kwargs", "val": ""}]- eval_dataset (Dataset, optional) -- Pass a dataset if you wish to override self.eval_dataset. If it is an Dataset, columns not accepted by the model.forward() method are automatically removed. It must implement the __len__ method.

• ignore_keys (list[str], optional) -- A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions.
• metric_key_prefix (str, optional, defaults to "eval") -- An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named "eval_bleu" if the prefix is "eval" (default)
• max_length (int, optional) -- The maximum target length to use when predicting with the generate method.
• num_beams (int, optional) -- Number of beams for beam search that will be used when predicting with the generate method. 1 means no beam search.
• gen_kwargs -- Additional generate specific kwargs.0A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The dictionary also contains the epoch number which comes from the training state.

Run evaluation and returns metrics.

The calling script will be responsible for providing a method to compute metrics, as they are task-dependent (pass it to the init compute_metrics argument).

You can also subclass and override this method to inject custom behavior.

Parameters:

eval_dataset (Dataset, optional) : Pass a dataset if you wish to override self.eval_dataset. If it is an Dataset, columns not accepted by the model.forward() method are automatically removed. It must implement the __len__ method.

ignore_keys (list[str], optional) : A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions.

metric_key_prefix (str, optional, defaults to "eval") : An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named "eval_bleu" if the prefix is "eval" (default)

max_length (int, optional) : The maximum target length to use when predicting with the generate method.

num_beams (int, optional) : Number of beams for beam search that will be used when predicting with the generate method. 1 means no beam search.

gen_kwargs : Additional generate specific kwargs.

Returns:

A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The dictionary also contains the epoch number which comes from the training state.

predict[[transformers.Seq2SeqTrainer.predict]]

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Run prediction and returns predictions and potential metrics.

Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method will also return metrics, like in evaluate().

If your predictions or labels have different sequence lengths (for instance because you're doing dynamic padding in a token classification task) the predictions will be padded (on the right) to allow for concatenation into one array. The padding index is -100.

Returns: NamedTuple A namedtuple with the following keys:

• predictions (np.ndarray): The predictions on test_dataset.
• label_ids (np.ndarray, optional): The labels (if the dataset contained some).
• metrics (dict[str, float], optional): The potential dictionary of metrics (if the dataset contained labels).

Parameters:

test_dataset (Dataset) : Dataset to run the predictions on. If it is a Dataset, columns not accepted by the model.forward() method are automatically removed. Has to implement the method __len__

ignore_keys (list[str], optional) : A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions.

metric_key_prefix (str, optional, defaults to "eval") : An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named "eval_bleu" if the prefix is "eval" (default)

max_length (int, optional) : The maximum target length to use when predicting with the generate method.

num_beams (int, optional) : Number of beams for beam search that will be used when predicting with the generate method. 1 means no beam search.

gen_kwargs : Additional generate specific kwargs.

TrainingArguments [[transformers.TrainingArguments]][[transformers.TrainingArguments]]

transformers.TrainingArguments[[transformers.TrainingArguments]]

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Configuration class for controlling all aspects of model training with the Trainer. TrainingArguments centralizes all hyperparameters, optimization settings, logging preferences, and infrastructure choices needed for training.

HfArgumentParser can turn this class into argparse arguments that can be specified on the command line.

get_process_log_leveltransformers.TrainingArguments.get_process_log_levelhttps://github.com/huggingface/transformers/blob/main/src/transformers/training_args.py#L1995[]

Returns the log level to be used depending on whether this process is the main process of node 0, main process of node non-0, or a non-main process.

For the main process the log level defaults to the logging level set (logging.WARNING if you didn't do anything) unless overridden by log_level argument.

For the replica processes the log level defaults to logging.WARNING unless overridden by log_level_replica argument.

The choice between the main and replica process settings is made according to the return value of should_log.

Parameters:

output_dir (str, optional, defaults to "trainer_output") : The output directory where the model predictions and checkpoints will be written.

get_warmup_steps[[transformers.TrainingArguments.get_warmup_steps]]

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Get number of steps used for a linear warmup.

main_process_first[[transformers.TrainingArguments.main_process_first]]

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A context manager for torch distributed environment where on needs to do something on the main process, while blocking replicas, and when it's finished releasing the replicas.

One such use is for datasets's map feature which to be efficient should be run once on the main process, which upon completion saves a cached version of results and which then automatically gets loaded by the replicas.

Parameters:

local (bool, optional, defaults to True) : if True first means process of rank 0 of each node if False first means process of rank 0 of node rank 0 In multi-node environment with a shared filesystem you most likely will want to use local=False so that only the main process of the first node will do the processing. If however, the filesystem is not shared, then the main process of each node will need to do the processing, which is the default behavior.

desc (str, optional, defaults to "work") : a work description to be used in debug logs

set_dataloader[[transformers.TrainingArguments.set_dataloader]]

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A method that regroups all arguments linked to the dataloaders creation.

Example:

>>> from transformers import TrainingArguments

>>> args = TrainingArguments("working_dir")
>>> args = args.set_dataloader(train_batch_size=16, eval_batch_size=64)
>>> args.per_device_train_batch_size
16

Parameters:

drop_last (bool, optional, defaults to False) : Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size) or not.

num_workers (int, optional, defaults to 0) : Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process.

pin_memory (bool, optional, defaults to True) : Whether you want to pin memory in data loaders or not. Will default to True.

persistent_workers (bool, optional, defaults to False) : If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will increase RAM usage. Will default to False.

prefetch_factor (int, optional) : Number of batches loaded in advance by each worker. 2 means there will be a total of 2 * num_workers batches prefetched across all workers.

auto_find_batch_size (bool, optional, defaults to False) : Whether to find a batch size that will fit into memory automatically through exponential decay, avoiding CUDA Out-of-Memory errors. Requires accelerate to be installed (pip install accelerate)

ignore_data_skip (bool, optional, defaults to False) : When resuming training, whether or not to skip the epochs and batches to get the data loading at the same stage as in the previous training. If set to True, the training will begin faster (as that skipping step can take a long time) but will not yield the same results as the interrupted training would have.

sampler_seed (int, optional) : Random seed to be used with data samplers. If not set, random generators for data sampling will use the same seed as self.seed. This can be used to ensure reproducibility of data sampling, independent of the model seed.

set_evaluate[[transformers.TrainingArguments.set_evaluate]]

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A method that regroups all arguments linked to evaluation.

Example:

>>> from transformers import TrainingArguments

>>> args = TrainingArguments("working_dir")
>>> args = args.set_evaluate(strategy="steps", steps=100)
>>> args.eval_steps
100

Parameters:

strategy (str or IntervalStrategy, optional, defaults to "no") : The evaluation strategy to adopt during training. Possible values are: - "no": No evaluation is done during training. - "steps": Evaluation is done (and logged) every steps. - "epoch": Evaluation is done at the end of each epoch. Setting a strategy different from "no" will set self.do_eval to True.

steps (int, optional, defaults to 500) : Number of update steps between two evaluations if strategy="steps".

batch_size (int optional, defaults to 8) : The batch size per device (GPU/TPU core/CPU...) used for evaluation.

accumulation_steps (int, optional) : Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but requires more memory).

delay (float, optional) : Number of epochs or steps to wait for before the first evaluation can be performed, depending on the eval_strategy.

loss_only (bool, optional, defaults to False) : Ignores all outputs except the loss.

set_logging[[transformers.TrainingArguments.set_logging]]

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A method that regroups all arguments linked to logging.

Example:

>>> from transformers import TrainingArguments

>>> args = TrainingArguments("working_dir")
>>> args = args.set_logging(strategy="steps", steps=100)
>>> args.logging_steps
100

Parameters:

strategy (str or IntervalStrategy, optional, defaults to "steps") : The logging strategy to adopt during training. Possible values are: - "no": No logging is done during training. - "epoch": Logging is done at the end of each epoch. - "steps": Logging is done every logging_steps.

steps (int, optional, defaults to 500) : Number of update steps between two logs if strategy="steps".

level (str, optional, defaults to "passive") : Logger log level to use on the main process. Possible choices are the log levels as strings: "debug", "info", "warning", "error" and "critical", plus a "passive" level which doesn't set anything and lets the application set the level.

report_to (str or list[str], optional, defaults to "none") : The list of integrations to report the results and logs to. Supported platforms are "azure_ml", "clearml", "codecarbon", "comet_ml", "dagshub", "dvclive", "flyte", "mlflow", "swanlab", "tensorboard", "trackio" and "wandb". Use "all" to report to all integrations installed, "none" for no integrations.

first_step (bool, optional, defaults to False) : Whether to log and evaluate the first global_step or not.

nan_inf_filter (bool, optional, defaults to True) : Whether to filter nan and inf losses for logging. If set to True the loss of every step that is nan or inf is filtered and the average loss of the current logging window is taken instead. nan_inf_filter only influences the logging of loss values, it does not change the behavior the gradient is computed or applied to the model.

on_each_node (bool, optional, defaults to True) : In multinode distributed training, whether to log using log_level once per node, or only on the main node.

replica_level (str, optional, defaults to "passive") : Logger log level to use on replicas. Same choices as log_level

set_lr_scheduler[[transformers.TrainingArguments.set_lr_scheduler]]

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A method that regroups all arguments linked to the learning rate scheduler and its hyperparameters.

Example:

>>> from transformers import TrainingArguments

>>> args = TrainingArguments("working_dir")
>>> args = args.set_lr_scheduler(name="cosine", warmup_steps=0.05)
>>> args.warmup_steps
0.05

Parameters:

name (str or SchedulerType, optional, defaults to "linear") : The scheduler type to use. See the documentation of SchedulerType for all possible values.

num_epochs(float, optional, defaults to 3.0) : Total number of training epochs to perform (if not an integer, will perform the decimal part percents of the last epoch before stopping training).

max_steps (int, optional, defaults to -1) : If set to a positive number, the total number of training steps to perform. Overrides num_train_epochs. For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until max_steps is reached.

warmup_steps (float, optional, defaults to 0) : Number of steps used for a linear warmup from 0 to learning_rate. Should be an integer or a float in range [0,1). If smaller than 1, will be interpreted as ratio of steps used for a linear warmup from 0 to learning_rate.

set_optimizer[[transformers.TrainingArguments.set_optimizer]]

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A method that regroups all arguments linked to the optimizer and its hyperparameters.

Example:

>>> from transformers import TrainingArguments

>>> args = TrainingArguments("working_dir")
>>> args = args.set_optimizer(name="adamw_torch", beta1=0.8)
>>> args.optim
'adamw_torch'

Parameters:

name (str or training_args.OptimizerNames, optional, defaults to "adamw_torch") : The optimizer to use: "adamw_torch", "adamw_torch_fused", "adamw_anyprecision" or "adafactor".

learning_rate (float, optional, defaults to 5e-5) : The initial learning rate.

weight_decay (float, optional, defaults to 0) : The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights.

beta1 (float, optional, defaults to 0.9) : The beta1 hyperparameter for the adam optimizer or its variants.

beta2 (float, optional, defaults to 0.999) : The beta2 hyperparameter for the adam optimizer or its variants.

epsilon (float, optional, defaults to 1e-8) : The epsilon hyperparameter for the adam optimizer or its variants.

args (str, optional) : Optional arguments that are supplied to AnyPrecisionAdamW (only useful when optim="adamw_anyprecision").

set_push_to_hub[[transformers.TrainingArguments.set_push_to_hub]]

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A method that regroups all arguments linked to synchronizing checkpoints with the Hub.

Calling this method will set self.push_to_hub to True, which means the output_dir will begin a git directory synced with the repo (determined by model_id) and the content will be pushed each time a save is triggered (depending on your self.save_strategy). Calling save_model() will also trigger a push.

Example:

>>> from transformers import TrainingArguments

>>> args = TrainingArguments("working_dir")
>>> args = args.set_push_to_hub("me/awesome-model")
>>> args.hub_model_id
'me/awesome-model'

Parameters:

model_id (str) : The name of the repository to keep in sync with the local output_dir. It can be a simple model ID in which case the model will be pushed in your namespace. Otherwise it should be the whole repository name, for instance "user_name/model", which allows you to push to an organization you are a member of with "organization_name/model".

strategy (str or HubStrategy, optional, defaults to "every_save") : Defines the scope of what is pushed to the Hub and when. Possible values are: - "end": push the model, its configuration, the processing_class e.g. tokenizer (if passed along to the Trainer) and a draft of a model card when the save_model() method is called. - "every_save": push the model, its configuration, the processing_class e.g. tokenizer (if passed along to the Trainer) and a draft of a model card each time there is a model save. The pushes are asynchronous to not block training, and in case the save are very frequent, a new push is only attempted if the previous one is finished. A last push is made with the final model at the end of training. - "checkpoint": like "every_save" but the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to resume training easily with trainer.train(resume_from_checkpoint="last-checkpoint"). - "all_checkpoints": like "checkpoint" but all checkpoints are pushed like they appear in the output folder (so you will get one checkpoint folder per folder in your final repository)

token (str, optional) : The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with hf auth login.

private_repo (bool, optional, defaults to False) : Whether to make the repo private. If None (default), the repo will be public unless the organization's default is private. This value is ignored if the repo already exists.

always_push (bool, optional, defaults to False) : Unless this is True, the Trainer will skip pushing a checkpoint when the previous push is not finished.

revision (str, optional) : The revision to use when pushing to the Hub. Can be a branch name, a tag, or a commit hash.

set_save[[transformers.TrainingArguments.set_save]]

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A method that regroups all arguments linked to checkpoint saving.

Example:

>>> from transformers import TrainingArguments

>>> args = TrainingArguments("working_dir")
>>> args = args.set_save(strategy="steps", steps=100)
>>> args.save_steps
100

Parameters:

strategy (str or IntervalStrategy, optional, defaults to "steps") : The checkpoint save strategy to adopt during training. Possible values are: - "no": No save is done during training. - "epoch": Save is done at the end of each epoch. - "steps": Save is done every save_steps.

steps (int, optional, defaults to 500) : Number of updates steps before two checkpoint saves if strategy="steps".

total_limit (int, optional) : If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in output_dir.

on_each_node (bool, optional, defaults to False) : When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one. This should not be activated when the different nodes use the same storage as the files will be saved with the same names for each node.

set_testing[[transformers.TrainingArguments.set_testing]]

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A method that regroups all basic arguments linked to testing on a held-out dataset.

Calling this method will automatically set self.do_predict to True.

Example:

>>> from transformers import TrainingArguments

>>> args = TrainingArguments("working_dir")
>>> args = args.set_testing(batch_size=32)
>>> args.per_device_eval_batch_size
32

Parameters:

batch_size (int optional, defaults to 8) : The batch size per device (GPU/TPU core/CPU...) used for testing.

loss_only (bool, optional, defaults to False) : Ignores all outputs except the loss.

set_training[[transformers.TrainingArguments.set_training]]

Source

A method that regroups all basic arguments linked to the training.

Calling this method will automatically set self.do_train to True.

Example:

>>> from transformers import TrainingArguments

>>> args = TrainingArguments("working_dir")
>>> args = args.set_training(learning_rate=1e-4, batch_size=32)
>>> args.learning_rate
1e-4

Parameters:

learning_rate (float, optional, defaults to 5e-5) : The initial learning rate for the optimizer.

batch_size (int optional, defaults to 8) : The batch size per device (GPU/TPU core/CPU...) used for training.

weight_decay (float, optional, defaults to 0) : The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in the optimizer.

num_train_epochs(float, optional, defaults to 3.0) : Total number of training epochs to perform (if not an integer, will perform the decimal part percents of the last epoch before stopping training).

max_steps (int, optional, defaults to -1) : If set to a positive number, the total number of training steps to perform. Overrides num_train_epochs. For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until max_steps is reached.

gradient_accumulation_steps (int, optional, defaults to 1) : Number of updates steps to accumulate the gradients for, before performing a backward/update pass. When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging, evaluation, save will be conducted every gradient_accumulation_steps * xxx_step training examples.

seed (int, optional, defaults to 42) : Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the ~Trainer.model_init function to instantiate the model if it has some randomly initialized parameters.

gradient_checkpointing (bool, optional, defaults to False) : If True, use gradient checkpointing to save memory at the expense of slower backward pass.

to_dict[[transformers.TrainingArguments.to_dict]]

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Serializes this instance while replace Enum by their values (for JSON serialization support). It obfuscates the token values by removing their value.

to_json_string[[transformers.TrainingArguments.to_json_string]]

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Serializes this instance to a JSON string.

to_sanitized_dict[[transformers.TrainingArguments.to_sanitized_dict]]

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Sanitized serialization to use with TensorBoard's hparams

Seq2SeqTrainingArguments [[transformers.Seq2SeqTrainingArguments]][[transformers.Seq2SeqTrainingArguments]]

transformers.Seq2SeqTrainingArguments[[transformers.Seq2SeqTrainingArguments]]

Source

Configuration class for controlling all aspects of model training with the Trainer. TrainingArguments centralizes all hyperparameters, optimization settings, logging preferences, and infrastructure choices needed for training.

HfArgumentParser can turn this class into argparse arguments that can be specified on the command line.

to_dicttransformers.Seq2SeqTrainingArguments.to_dicthttps://github.com/huggingface/transformers/blob/main/src/transformers/training_args_seq2seq.py#L84[]

Serializes this instance while replace Enum by their values and GenerationConfig by dictionaries (for JSON serialization support). It obfuscates the token values by removing their value.

Parameters:

output_dir (str, optional, defaults to "trainer_output") : The output directory where the model predictions and checkpoints will be written.