![]() This profiler uses Python's cProfiler to record more detailed information about time spent in each function call recorded during a given action. Interface to save/load checkpoints as they are saved through the Strategy.ĬheckpointIO to save checkpoints for HPU training strategies.ĬheckpointIO that utilizes torch.save() and torch.load() to save and load checkpoints respectively, common for most use cases.ĬheckpointIO that utilizes xm.save() to save checkpoints for TPU training strategies.Ībstract base class for creating plugins that wrap layers of a model with synchronization logic for multiprocessing.Ī plugin that wraps all batch normalization layers of a model with synchronization logic for multiprocessing. The default environment used by Lightning for a single node or free cluster (not managed).Īn environment for running on clusters managed by the LSF resource manager.Ĭluster environment for training on a cluster managed by SLURM.Įnvironment for fault-tolerant and elastic training with torchelasticĬluster environment for training on a TPU Pod with the PyTorch/XLA library.ĪsyncCheckpointIO enables saving the checkpoints asynchronously in a thread. Plugin for Native Mixed Precision (AMP) training with tocast.īase class for all plugins handling the precision-specific parts of the training.Įnvironment for distributed training using the PyTorchJob operator from Kubeflow Plugin that enables bfloat/half support on HPUs. Native AMP for Fully Sharded Native Training. Plugin for training with double ( torch.float64) precision.įullyShardedNativeNativeMixedPrecisionPlugin Precision plugin for DeepSpeed integration. Mixed Precision Plugin based on Nvidia/Apex ( ) Loop to run over dataloaders for prediction. Loop performing prediction on arbitrary sequentially used dataloaders. Loops over all dataloaders for evaluation. This is the loop performing the evaluation. This Loop iterates over the epochs to run the training.Ī special loop implementing what is known in Lightning as Manual Optimization where the optimization happens entirely in the training_step() and therefore the user is responsible for back-propagating gradients and making calls to the optimizers. Runs over all batches in a dataloader (one epoch). Lite accelerates your PyTorch training or inference code with minimal changes required. This class is used to wrap the user optimizers and handle properly the backward and optimizer_step logic across accelerators, AMP, accumulate_grad_batches. ![]() Initializes internal Module state, shared by both nn.Module and ScriptModule. This is the default progress bar used by Lightning.Ī DataModule standardizes the training, val, test splits, data preparation and transforms. The Timer callback tracks the time spent in the training, validation, and test loops and interrupts the Trainer if the given time limit for the training loop is reached. Implements the Stochastic Weight Averaging (SWA) Callback to average a model. Generates a summary of all layers in a LightningModule with rich text formatting.Ĭreate a progress bar with rich text formatting. Quantization allows speeding up inference and decreasing memory requirements by performing computations and storing tensors at lower bitwidths (such as INT8 or FLOAT16) than floating point precision. The base class for progress bars in Lightning. Generates a summary of all layers in a LightningModule. Model pruning Callback, using PyTorch's prune utilities. Save the model periodically by monitoring a quantity. Monitor a metric and stop training when it stops improving.Ĭhange gradient accumulation factor according to scheduling.Ĭreate a simple callback on the fly using lambda functions.Īutomatically monitor and logs learning rate for learning rate schedulers during training. This class implements the base logic for writing your own Finetuning Callback.īase class to implement how the predictions should be stored.Ībstract base class used to build new callbacks.Īutomatically monitors and logs device stats during training stage. From PyTorch to PyTorch Lightning įinetune a backbone model based on a learning rate user-defined scheduling.Multi-agent Reinforcement Learning With WarpDrive.Finetune Transformers Models with PyTorch Lightning. ![]()
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