transformer weight decay transformer weight decay

To help you get started, we've selected a few transformers examples, based on popular ways it is used in public projects. The current mode used for parallelism if multiple GPUs/TPU cores are available. models should have a greater metric or not. One of: - :obj:`ParallelMode.NOT_PARALLEL`: no parallelism (CPU or one GPU). ( main_oc20.py is the code for training and evaluating. num_training_steps (int) The total number of training steps. ( We also combine this with an early stopping algorithm, Asynchronous Hyperband, where we stop bad performing trials early to avoid wasting resources on them. ", "Total number of training epochs to perform. gradients if required, and pass the result to apply_gradients. Create a schedule with a constant learning rate, using the learning rate set in optimizer. Unified API to get any scheduler from its name. The whole experiment took ~6 min to run, which is roughly on par with our basic grid search. Just adding the square of the weights to the ), ( name: str = 'AdamWeightDecay' backwards pass and update the weights: Alternatively, you can just get the logits and calculate the loss yourself. lr is included for backward compatibility, num_cycles (float, optional, defaults to 0.5) The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0 Applies a warmup schedule on a given learning rate decay schedule. num_cycles: float = 0.5 (14), we set them to 1, 1 and 0.1 in the following comparison experiments. including scripts for training and fine-tuning on GLUE, SQuAD, and several other tasks. lr_end = 1e-07 and get access to the augmented documentation experience, ( Edit. closure (Callable, optional) A closure that reevaluates the model and returns the loss. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large scale biomedical literature. Adam keeps track of (exponential moving) averages of the gradient (called the first moment, from now on denoted as m) and the square of the gradients (called raw second moment, from now on denoted as v).. The cell successfully executes, but it does nothing - does not start training at all. are initialized in eval mode by default. compatibility to allow time inverse decay of learning rate. TFTrainer() expects the passed datasets to be dataset gradients by norm; clipvalue is clip gradients by value, decay is included for backward implementation at Now you have access to many transformer-based models including the pre-trained Bert models in pytorch. Jan 2021 Aravind Srinivas power (float, optional, defaults to 1.0) The power to use for PolynomialDecay. Sanitized serialization to use with TensorBoards hparams. :obj:`XxxForQuestionAnswering` in which case it will default to :obj:`["start_positions". If none is passed, weight decay is applied to all parameters . If none is passed, weight decay is applied to all parameters except bias . There are 3 . BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2) initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases However, we will show that in rather standard feedforward networks, they need residual connections to be effective (in a sense I will clarify below). fp16_backend (:obj:`str`, `optional`, defaults to :obj:`"auto"`): The backend to use for mixed precision training. Users should This is a new post in my NER series. num_cycles (float, optional, defaults to 0.5) The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0 Paper: Adafactor: Adaptive Learning Rates with Sublinear Memory Cost https://arxiv.org/abs/1804.04235 Note that You can train, fine-tune, Google Scholar Fine-tuning in the HuggingFace's transformers library involves using a pre-trained model and a tokenizer that is compatible with that model's architecture and . init_lr (float) The desired learning rate at the end of the warmup phase. Weight Decay, or $L_{2}$ Regularization, is a regularization technique applied to the weights of a neural network. # Copyright 2020 The HuggingFace Team. Often weight decay refers to the implementation where we specify it directly in the weight update rule (whereas L2 regularization is usually the implementation which is specified in the objective function). num_training_steps (int, optional) The number of training steps to do. Copyright 2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0. The second is for training Transformer-based architectures such as BERT, . transformers.create_optimizer (init_lr: float, . Recommended T5 finetuning settings (https://discuss.huggingface.co/t/t5-finetuning-tips/684/3): Training without LR warmup or clip_threshold is not recommended. Copyright 2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0, tf.keras.optimizers.schedules.LearningRateSchedule], https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py, https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37. Will eventually default to :obj:`["labels"]` except if the model used is one of the. correct_bias (bool, optional, defaults to True) Whether ot not to correct bias in Adam (for instance, in Bert TF repository they use False). The Transformer reads entire sequences of tokens at once. Image Source: Deep Learning, Goodfellow et al. :obj:`output_dir` points to a checkpoint directory. closure: typing.Callable = None torch.optim.lr_scheduler.LambdaLR with the appropriate schedule. Figure 2: Comparison of nuclear norm (solid line) and nuclear norm upper bound penalized by weight decay on individual factors (dotted line) during the training of ResNet20 on CIFAR-10, showing that for most of training, weight decay is effectively penalizing the . weight_decay (:obj:`float`, `optional`, defaults to 0): The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in. Gradients will be accumulated locally on each replica and without synchronization. The text was updated successfully, but these errors were encountered: Too bad you didn't get an answer on SO. GPT model is essentially a standard transformer with a few tweaks. Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, gradient_accumulation_steps (:obj:`int`, `optional`, defaults to 1): Number of updates steps to accumulate the gradients for, before performing a backward/update pass. When we instantiate a model with ", "Whether or not to replace AdamW by Adafactor. ( debug (:obj:`bool`, `optional`, defaults to :obj:`False`): When training on TPU, whether to print debug metrics or not. lr = None For example, we can apply weight decay to all parameters value Kaggle. Therefore, logging, evaluation, save will be conducted every ``gradient_accumulation_steps * xxx_step`` training. We eps (float, optional, defaults to 1e-6) Adams epsilon for numerical stability. The following is equivalent to the previous example: Of course, you can train on GPU by calling to('cuda') on the model and applied to all parameters except bias and layer norm parameters. Adam enables L2 weight decay and clip_by_global_norm on gradients. decay_schedule_fn: typing.Callable other choices will force the requested backend. this optimizer internally adjusts the learning rate depending on the scale_parameter, relative_step and Using `--per_device_eval_batch_size` is preferred. ", "Batch size per GPU/TPU core/CPU for evaluation. The . ", "Enable deepspeed and pass the path to deepspeed json config file (e.g. linearly between 0 and the initial lr set in the optimizer. Weight decay is a form of regularization-after calculating the gradients, we multiply them by, e.g., 0.99. num_warmup_steps optimizer to end lr defined by lr_end, after a warmup period during which it increases linearly from 0 to the ", "If > 0: set total number of training steps to perform. `__ for more details. pre-trained model. linearly decays to 0 by the end of training. min_lr_ratio (float, optional, defaults to 0) The final learning rate at the end of the linear decay will be init_lr * min_lr_ratio. layers. Allowed to be {clipnorm, clipvalue, lr, decay}. init_lr (float) The desired learning rate at the end of the warmup phase. lr_end (float, optional, defaults to 1e-7) The end LR. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately. Ilya Loshchilov, Frank Hutter. and evaluate any Transformers model with a wide range of training options and adafactor (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to use the :class:`~transformers.Adafactor` optimizer instead of. beta_2: float = 0.999 Note that ", "Batch size per GPU/TPU core/CPU for training. power (float, optional, defaults to 1.0) The power to use for PolynomialDecay. power: float = 1.0 ", smdistributed.dataparallel.torch.distributed. See, the `example scripts `__ for more. If needed, you can also include_in_weight_decay (List[str], optional) - List of the parameter names (or re patterns) to apply weight decay to. weights are instantiated randomly when not present in the specified applied to all parameters by default (unless they are in exclude_from_weight_decay). Does the default weight_decay of 0.0 in transformers.AdamW make sense. eps: float = 1e-06 weight_decay_rate: float = 0.0 The Layer-wise Adaptive Rate Scaling (LARS) optimizer by You et al. load_best_model_at_end (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to load the best model found during training at the end of training. When training on TPU, the number of TPU cores (automatically passed by launcher script). The actual batch size for evaluation (may differ from :obj:`per_gpu_eval_batch_size` in distributed training). ( learning_rate (:obj:`float`, `optional`, defaults to 5e-5): The initial learning rate for :class:`~transformers.AdamW` optimizer. . * :obj:`"steps"`: Evaluation is done (and logged) every :obj:`eval_steps`. weight_decay_rate (float, optional, defaults to 0) The weight decay to use. Therefore, shouldn't make more sense to have the default weight decay for AdamW > 0? num_training_steps: int ICLR 2017Best Paper2017Fixing Weight Decay Regularization in AdamAdamAdamWL2SGD initial lr set in the optimizer. Create a schedule with a learning rate that decreases following the values of the cosine function between the Here, we fit a Gaussian Process model that tries to predict the performance of the parameters (i.e. Model classes in Transformers are designed to be compatible with native PyTorch and TensorFlow 2 and can be used seemlessly with either. optimizer PyTorch and TensorFlow 2 and can be used seemlessly with either. We use a standard uncased BERT model from Hugging Face transformers and we want to fine-tune on the RTE dataset from the SuperGLUE benchmark. a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer. correction as well as weight decay. # deepspeed performs its own DDP internally, and requires the program to be started with: # python -m torch.distributed.launch --nproc_per_node=2 ./program.py, "--deepspeed requires deepspeed: `pip install deepspeed`.". Softmax Regression; 4.2. If none is passed, weight decay is Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the loss function is not the correct way of using L2 regularization/weight decay with Adam, since that will interact Use `Deepspeed `__. - :obj:`False` if :obj:`metric_for_best_model` is not set, or set to :obj:`"loss"` or :obj:`"eval_loss"`. And like @BramVanroy said, it would be such a breaking change that even if we really wanted to change that default, we probably wouldnt. name (str or :obj:`SchedulerType) The name of the scheduler to use. adam_beta1 (float, optional, defaults to 0.9) The beta1 to use in Adam. to tokenize MRPC and convert it to a TensorFlow Dataset object. Only useful if applying dynamic padding. num_training_steps: typing.Optional[int] = None no_deprecation_warning: bool = False last_epoch: int = -1 Pre-trained Transformer models such as BERT have shown great success in a wide range of applications, but at the cost of substantial increases in model complexity. adam_epsilon (:obj:`float`, `optional`, defaults to 1e-8): The epsilon hyperparameter for the :class:`~transformers.AdamW` optimizer. at the next training step under the keyword argument ``mems``. The search space we use for this experiment is as follows: We run only 8 trials, much less than Bayesian Optimization since instead of stopping bad trials, they copy from the good ones. The power transformer model test system is composed of two parts: the transformer discharge model and the automatic discharge simulation test system, which can realize the free switching, automatic rise, and fall of various discharge fault patterns, . A Sparse Transformer is a Transformer based architecture which utilises sparse factorizations of the attention matrix to reduce time/memory to O ( n n). Check here for the full code examples. Will be set to :obj:`True` if, :obj:`evaluation_strategy` is different from :obj:`"no"`. seed (:obj:`int`, `optional`, defaults to 42): Random seed that will be set at the beginning of training. Weight decay involves adding a penalty to the loss function to discourage large weights. Allowed to be {clipnorm, clipvalue, lr, decay}. We can call model.train() to warmup_steps (:obj:`int`, `optional`, defaults to 0): Number of steps used for a linear warmup from 0 to :obj:`learning_rate`. interface through Trainer() and loss function is not the correct way of using L2 regularization/weight decay with Adam, since that will interact I have a question regarding the AdamW optimizer default weight_decay value. In some cases, you might be interested in keeping the weights of the For further details regarding the algorithm we refer to Decoupled Weight Decay Regularization.. Parameters:. configuration and pre-trained weights classification head on top of the encoder with an output size of 2. exclude_from_weight_decay: typing.Optional[typing.List[str]] = None Serializes this instance while replace `Enum` by their values (for JSON serialization support). How to train a language model, initial lr set in the optimizer. epsilon (float, optional, defaults to 1e-7) The epsilon paramenter in Adam, which is a small constant for numerical stability. 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. objects from tensorflow_datasets. (TODO: v5). If a GPT-3 is an autoregressive transformer model with 175 billion parameters. submodule on any task-specific model in the library: Models can also be trained natively in TensorFlow 2. The experiment took a total of ~13 min to run, and while this is longer than grid search, we ran a total of 60 trials and searched over a much larger space. ", "Number of subprocesses to use for data loading (PyTorch only). And this gets amplified even further if we want to tune over even more hyperparameters! 0 means that the data will be loaded in the main process. It will cover the basics and introduce you to the amazing Trainer class from the transformers library. Serializes this instance to a JSON string. with the m and v parameters in strange ways as shown in Decoupled Weight Decay Regularization. adam_beta2 (float, optional, defaults to 0.999) The beta2 to use in Adam. dataloader_drop_last (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size), Number of update steps between two evaluations if :obj:`evaluation_strategy="steps"`. This argument is not directly used by. transformers.create_optimizer (init_lr: float, num_train_steps: int, . num_warmup_steps: int Decoupled Weight Decay Regularization. Note: If training BERT layers too, try Adam optimizer with weight decay which can help reduce overfitting and improve generalization [1]. This is accomplished by setting the learning rate of the top layer and using a multiplicative decay rate to decrease the learning rate layer-by-layer . Applies a warmup schedule on a given learning rate decay schedule. TPU: Whether to print debug metrics", "Drop the last incomplete batch if it is not divisible by the batch size. weight_decay: float = 0.0 Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate To do so, simply set the requires_grad attribute to False on dataloader_num_workers (:obj:`int`, `optional`, defaults to 0): Number of subprocesses to use for data loading (PyTorch only). WEIGHT DECAY - WORDPIECE - Edit Datasets . When using gradient accumulation, one step is counted as one step with backward pass. ", "TPU: Number of TPU cores (automatically passed by launcher script)", "Deprecated, the use of `--debug` is preferred. train a model with 5% better accuracy in the same amount of time. When used with a distribution strategy, the accumulator should be called in a **kwargs In the tests we ran, the best learning rate with L2 regularization was 1e-6 (with a maximum learning rate of 1e-3) while 0.3 was the best value for weight decay (with a learning rate of 3e-3). import tensorflow_addons as tfa # Adam with weight decay optimizer = tfa.optimizers.AdamW(0.005, learning_rate=0.01) 6. Note: power defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT name: str = None warmup_steps (int) The number of steps for the warmup part of training. . Weight Decay; 4. Using the Hugging Face transformers library, we can easily load a pre-trained NLP model with several extra layers, and run a few epochs of fine-tuning on a specific task. Training without LR warmup or clip threshold is not recommended. num_training_steps: int Given that the whole purpose of AdamW is to decouple the weight decay regularization, is my understanding that the results anyone can get with AdamW and Adam if both are used with weight_decay=0.0 (this is, without weight decay) should be exactly the same. eps (float, optional, defaults to 1e-6) Adams epsilon for numerical stability. To learn more about how researchers and companies use Ray to tune their models in production, join us at the upcoming Ray Summit! clipnorm is clip ", "The metric to use to compare two different models. relative_step=False. of the specified model are used to initialize the model. the loss), and is used to inform future hyperparameters. This way we can start more runs in parallel and thus test a larger number of hyperparameter configurations. ", "Remove columns not required by the model when using an nlp.Dataset. num_training_steps Gradient accumulation utility. weight decay, etc. greater_is_better (:obj:`bool`, `optional`): Use in conjunction with :obj:`load_best_model_at_end` and :obj:`metric_for_best_model` to specify if better. warmup_init options. Image classification with Vision Transformer . logging_steps (:obj:`int`, `optional`, defaults to 500): save_steps (:obj:`int`, `optional`, defaults to 500): Number of updates steps before two checkpoint saves. Applies a warmup schedule on a given learning rate decay schedule. # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`, # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will, # trigger an error that a device index is missing. recommended to use learning_rate instead. Unified API to get any scheduler from its name. of the warmup). The value for the params key should be a list of named parameters (e.g. lr: float = 0.001 ", "See details at https://nvidia.github.io/apex/amp.html", "The backend to be used for mixed precision. warmup_steps: int We compare 3 different optimization strategies Grid Search, Bayesian Optimization, and Population Based Training to see which one results in a more accurate model in less amount of time. params (Iterable[torch.nn.parameter.Parameter]) Iterable of parameters to optimize or dictionaries defining parameter groups. Anyways, here it is: In the Docs we can clearly see that the AdamW optimizer sets. using the standard training tools available in either framework. ", "When performing evaluation and predictions, only returns the loss. :obj:`False` if your metric is better when lower. We also use Weights & Biases to visualize our results- click here to view the plots on W&B! PyTorch Modules, privacy statement. Transformers are not capable of remembering the order or sequence of the inputs. Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT to 3D point cloud, is presented and it is shown that a pure Transformer architecture attains 93.8% accuracy on ModelNet40 and 83.1% accuracy in the hardest setting of ScanObjectNN, surpassing carefully designed point cloud models with much fewer hand-made . returned element is the Cross Entropy loss between the predictions and the weight_decay_rate (float, optional, defaults to 0) The weight decay to use. type = None adam_clipnorm: typing.Optional[float] = None Lets use tensorflow_datasets to load in the MRPC dataset from GLUE. A disciplined approach to neural network hyper-parameters: Part 1-learning rate, batch size, momentum, and weight decay. ", "`output_dir` is only optional if it can get inferred from the environment. last_epoch: int = -1 label_names (:obj:`List[str]`, `optional`): The list of keys in your dictionary of inputs that correspond to the labels. to adding the square of the weights to the loss with plain (non-momentum) SGD. We call for the development of Foundation Transformer for true general-purpose modeling, which serves as a go-to architecture for . The output directory where the model predictions and checkpoints will be written. "The output directory where the model predictions and checkpoints will be written. This is not required by all schedulers (hence the argument being Create a schedule with a constant learning rate, using the learning rate set in optimizer. "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future ", "version. scale_parameter = True Having already set up our optimizer, we can then do a num_warmup_steps (int) The number of warmup steps. Must be one of :obj:`"auto"`, :obj:`"amp"` or, :obj:`"apex"`. ", "Whether or not to load the best model found during training at the end of training. ", "The list of keys in your dictionary of inputs that correspond to the labels. For all the experiments on the proposed method, we use Stochastic Gradient Descent (SGD) with momentum 0.9 and weight decay 1 1 0 4. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Even if its true that Adam and AdamW behave the same way when the weight decay is set to 0, I dont think its enough to change that default behavior (0.01 is a great default otherwise, that is the one we set in fastai for the Learner after countless experiments, but I think it should be set in a higher-level API, not the optimizer itself). include_in_weight_decay (List[str], optional) List of the parameter names (or re patterns) to apply weight decay to. The key takeaway here is that Population Based Training is the most effective approach to tune the hyperparameters of the Transformer model. arXiv preprint arXiv:1803.09820, 2018. One example is here. relative_step = True We are subtracting a constant times the weight from the original weight. last_epoch (int, optional, defaults to -1) The index of the last epoch when resuming training. Interestingly, we see that weight_decay is the second most important hyperparameter, showing the importance of searching over more hyperparameters. Breaking down barriers. oc20/trainer contains the code for energy trainers. Regularization. Implements Adam algorithm with weight decay fix as introduced in Decoupled Weight Decay Regularization. When set to :obj:`True`, the parameters :obj:`save_steps` will be ignored and the model will be saved. Trainer() uses a built-in default function to collate correct_bias: bool = True num_warmup_steps (int) The number of steps for the warmup phase. We can also see below that our best trials are mostly created towards the end of the full experiment, showing that our hyperparameter configurations get better as time goes on and our Bayesian optimizer is working. torch.optim.lr_scheduler.LambdaLR with the appropriate schedule. Surprisingly, a stronger decay on the head yields the best results. The optimizer allows us to apply different hyperpameters for specific Other changes to the Transformer architecture include: (a) a restructured residual block and weight initialization, (b) A set of sparse attention kernels which efficiently compute subsets of . with the m and v parameters in strange ways as shown in Decoupled Weight Decay include_in_weight_decay (List[str], optional) List of the parameter names (or re patterns) to apply weight decay to. num_warmup_steps (int, optional) The number of warmup steps to do. optimizer (Optimizer) The optimizer for which to schedule the learning rate. Will default to :obj:`"loss"` if unspecified and :obj:`load_best_model_at_end=True` (to use the evaluation, If you set this value, :obj:`greater_is_better` will default to :obj:`True`. encoder and easily train it on whatever sequence classification dataset we power (float, optional, defaults to 1.0) Power factor. Decoupled Weight Decay Regularization. - :obj:`ParallelMode.DISTRIBUTED`: several GPUs, each ahving its own process (uses. Well see that compared to the standard grid search baseline, Bayesian optimization provides a 1.5% accuracy improvement, and Population Based training provides a 5% improvement. lr (float, optional) The external learning rate. size for evaluation warmup_steps = 500, # number of warmup steps for learning rate scheduler weight_decay = 0.01, # strength of weight decay logging_dir = './logs', # directory for . can even save the model and then reload it as a PyTorch model (or vice-versa): We also provide a simple but feature-complete training and evaluation Index 0 takes into account the, # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`, # will use the first GPU in that env, i.e. We first start with a simple grid search over a set of pre-defined hyperparameters. warmup_init = False group_by_length (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to group together samples of roughly the same legnth in the training dataset (to minimize. label_smoothing_factor (:obj:`float`, `optional`, defaults to 0.0): The label smoothing factor to use. correct_bias (bool, optional, defaults to True) Whether ot not to correct bias in Adam (for instance, in Bert TF repository they use False). We use the search space recommended by the BERT authors: We run a total of 18 trials, or full training runs, one for each combination of hyperparameters. exclude_from_weight_decay (List[str], optional) List of the parameter names (or re patterns) to exclude from applying weight decay to. Instead we want ot decay the weights in a manner that doesnt interact with the m/v parameters. parameter groups. Then all we have to do is call scheduler.step() after optimizer.step(). ), AdaFactor pytorch implementation can be used as a drop in replacement for Adam original fairseq code: Does the default weight_decay of 0.0 in transformers.AdamW make sense? :obj:`torch.nn.DistributedDataParallel`). If none is passed, weight decay is With Ray Tune we can easily implement scalable PBT without much modification to our standard fine-tuning workflow. The Base Classification Model; . initial_learning_rate: float Create a schedule with a learning rate that decreases following the values of the cosine function between the With Bayesian Optimization, we were able to leverage a guided hyperparameter search. This is equivalent In the original BERT implementation and in earlier versions of this repo, both LayerNorm.weight and LayerNorm.bias are decayed. optimizer to end lr defined by lr_end, after a warmup period during which it increases linearly from 0 to the name (str, optional, defaults to AdamWeightDecay) Optional name for the operations created when applying gradients. without synchronization. Will default to :obj:`True`. To use a manual (external) learning rate schedule you should set scale_parameter=False and This returns a We can train, fine-tune, and evaluate any HuggingFace Transformers model with a wide range of training options and with built-in features like metric logging, gradient accumulation, and mixed precision. The Ray libraries offer a host of features and integrations. But even though we stopped poor performing trials early, subsequent trials would start training from scratch. We highly recommend using Trainer(), discussed below, Lets consider the common task of fine-tuning a masked language model like GPU#1, # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at, # Initializes the distributed backend which will take care of synchronizing nodes/GPUs, This will only be greater than one when you have multiple GPUs available but are not using distributed. ds_config.json)", "The label smoothing epsilon to apply (zero means no label smoothing). The Image Classification Dataset; 4.3. The same data augmentation and ensemble strategies were used for all models. optimizer: Optimizer optimizer: Optimizer When saving a model for inference, it is only necessary to save the trained model's learned parameters. adam_beta1: float = 0.9 However, the folks at fastai have been a little conservative in this respect. Questions & Help Details Hi, I tried to ask in SO before, but apparently the question seems to be irrelevant.