Trainer¶. ", "Deletes the older checkpoints in the output_dir. Now that we have THE DATA we can finally create our model and start training it! The full documentation is here. launcher options. One of the main benefits of enabling --sharded_ddp is that it uses a lot less GPU memory, so you should be able logging_dir (str, optional) – TensorBoard log directory. ", "Number of subprocesses to use for data loading (PyTorch only). on the `Apex documentation `__. eval_steps (int, optional, defaults to 1000) – Number of update steps before two evaluations. If it is an datasets.Dataset, columns not accepted by the You can still use your own models defined as :obj:`torch.nn.Module` as long as While we are going to discuss the configuration in details next, the key to getting a huge improvement on a single GPU Launch an hyperparameter search using optuna or Ray Tune. A descriptor for the run. This will only be greater than one when you have multiple GPUs available but are not using distributed This tutorial will cover two models – BERT and DistilBERT – and explain how to conduct a hyperparameter search using Sweeps. run_name (str, optional) – A descriptor for the run. data parallelism, this means some of the model layers are split on different GPUs). # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. For a practical usage example of this type of deployment, please, see this post. Training and Evaluating. Override num_train_epochs. To ensure reproducibility across runs, use the, :func:`~transformers.Trainer.model_init` function to instantiate the model if it has some randomly. ", "The list of integrations to report the results and logs to. gcp_project (str, optional) – Google Cloud Project name for the Cloud TPU-enabled project. If labels is a dict, such as when using Currently it provides If you have only 1 GPU to start with, then you don’t need this argument. ... * restore skip * Revert "Remove deprecated `evalutate_during_training` (huggingface#8852)" This reverts commit 5530299. Language model evaluation. interrupted training or reuse the fine-tuned model. If your predictions or labels have different sequence length (for instance because you’re doing dynamic - :obj:`ParallelMode.NOT_DISTRIBUTED`: several GPUs in one single process (uses :obj:`torch.nn.DataParallel`). And the Trainer like that: trainer = Trainer( tokenizer=tokenizer, model=model, args=training_args, train_dataset=train, eval_dataset=dev, compute_metrics=compute_metrics ) I've tried putting the padding and truncation parameters in the tokenizer, in the Trainer, and in the training… to the console, so you can see exactly what the final configuration was passed to it. 0 means that the data will be loaded in the. This is the model that should be used for the forward pass. make use of the past hidden states for their predictions. - :obj:`ParallelMode.TPU`: several TPU cores. The dataset should yield tuples of For this reason, you can specify the --save_hg_transformeroption, which will save the huggingface/transformersmodel whenever a checkpoint is saved using model.save_pretrained(save_path). do_train (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to run training or not. past_index (:obj:`int`, `optional`, defaults to -1): Some models like :doc:`TransformerXL <../model_doc/transformerxl>` or :doc`XLNet <../model_doc/xlnet>` can, make use of the past hidden states for their predictions. If you can install the latest CUDA toolkit it typically should support the newer compiler. If this argument is set to a positive int, the, ``Trainer`` will use the corresponding output (usually index 2) as the past state and feed it to the model. max_steps (int, optional, defaults to -1) – If set to a positive number, the total number of training steps to perform. Perform an evaluation step on model using obj:inputs. Computes the loss of the given features and labels pair. gathering predictions. After taking the course. contained labels). max_grad_norm (:obj:`float`, `optional`, defaults to 1.0): Maximum gradient norm (for gradient clipping). adam_epsilon (:obj:`float`, `optional`, defaults to 1e-8): The epsilon hyperparameter for the :class:`~transformers.AdamW` optimizer. will also return metrics, like in evaluate(). # You may obtain a copy of the License at, # http://www.apache.org/licenses/LICENSE-2.0, # Unless required by applicable law or agreed to in writing, software. If it is an datasets.Dataset, columns not accepted by the In SQuAD, an input consists of a question, and a paragraph for context. floating point operations for every backward + forward pass. `__ for more details. If provided, each call to label_ids (np.ndarray, optional): The labels (if the dataset contained some). inputs (Dict[str, Union[torch.Tensor, Any]]) – The inputs and targets of the model. logs (Dict[str, float]) – The values to log. evaluation_strategy (str or EvaluationStrategy, optional, defaults to "no") –. - :obj:`ParallelMode.DISTRIBUTED`: several GPUs, each ahving its own process (uses. Event called after logging the last logs. normally won’t fit. Remove a callback from the current list of TrainerCallback. :obj:`"auto"` will use AMP or APEX depending on the PyTorch version detected, while the. In that case, this method (int, optional, defaults to 1): This argument is not directly used by, :class:`~transformers.Trainer`, it's intended to be used by your training/evaluation scripts instead. automatically set it to AdamW and will use the supplied values or the defaults for the following command line 1 means no To deploy this feature with multiple GPUs adjust the Trainer command line arguments as This method should be removed once, # those deprecated arguments are removed form TrainingArguments. ", "Total number of training epochs to perform. I am using Trainer from the library to train so I do not use anything fancy. detailed in here. For full support for: Optimizer State Partitioning (ZeRO stage 1). evaluation_strategy (:obj:`str` or :class:`~transformers.trainer_utils.EvaluationStrategy`, `optional`, defaults to :obj:`"no"`): The evaluation strategy to adopt during training. warmup_steps (int, optional, defaults to 0) – Number of steps used for a linear warmup from 0 to learning_rate. learning_rate (float, optional, defaults to 5e-5) – The initial learning rate for AdamW optimizer. Introduction and Getting Started. If sharded_ddp (bool, optional, defaults to False) – Use Sharded DDP training from FairScale (in distributed The list of keys in your dictionary of inputs that correspond to the labels. label_smoothing_factor (:obj:`float`, `optional`, defaults to 0.0): The label smoothing factor to use. gradient_accumulation_steps (:obj:`int`, `optional`, defaults to 1): Number of updates steps to accumulate the gradients for, before performing a backward/update pass. evolve in the future. have any problems or questions with regards to DeepSpeed usage, please, file an issue with DeepSpeed GitHub. This is an experimental feature and its API may report_to (:obj:`List[str]`, `optional`, defaults to the list of integrations platforms installed): The list of integrations to report the results and logs to.