Index

Symbols

**datamodule_kwargs (lightning.pytorch.core.LightningDataModule.from_datasets parameter) **kwargs (lightning.pytorch.callbacks.LambdaCallback parameter), [1], [2] (lightning.pytorch.core.LightningDataModule.load_from_checkpoint parameter) (lightning.pytorch.core.LightningModule.forward parameter) (lightning.pytorch.core.LightningModule.load_from_checkpoint parameter) (lightning.pytorch.core.LightningModule.manual_backward parameter) (lightning.pytorch.core.LightningModule.print parameter) (lightning.pytorch.core.LightningModule.to_onnx parameter) (lightning.pytorch.core.LightningModule.to_torchscript parameter) (lightning.pytorch.core.module.LightningModule.forward parameter) (lightning.pytorch.core.module.LightningModule.load_from_checkpoint parameter) (lightning.pytorch.core.module.LightningModule.manual_backward parameter) (lightning.pytorch.core.module.LightningModule.print parameter) (lightning.pytorch.core.module.LightningModule.to_onnx parameter) (lightning.pytorch.core.module.LightningModule.to_torchscript parameter) (lightning.pytorch.loggers.comet.CometLogger parameter), [1] (lightning.pytorch.loggers.CometLogger parameter), [1] (lightning.pytorch.loggers.tensorboard.TensorBoardLogger parameter), [1] (lightning.pytorch.loggers.TensorBoardLogger parameter), [1] (lightning.pytorch.loggers.wandb.WandbLogger parameter), [1] (lightning.pytorch.loggers.WandbLogger parameter), [1] (lightning.pytorch.plugins.precision.DeepSpeedPrecision.backward parameter) (lightning.pytorch.plugins.precision.Precision.backward parameter) (lightning.pytorch.strategies.DDPStrategy.optimizer_step parameter) (lightning.pytorch.strategies.FSDPStrategy parameter), [1] (lightning.pytorch.strategies.SingleDeviceStrategy.reduce parameter) (lightning.pytorch.strategies.Strategy.backward parameter) (lightning.pytorch.strategies.Strategy.optimizer_step parameter)

**neptune_run_kwargs (lightning.pytorch.loggers.neptune.NeptuneLogger parameter), [1] (lightning.pytorch.loggers.NeptuneLogger parameter), [1] **profiler_kwargs (lightning.pytorch.profilers.PyTorchProfiler parameter), [1] **summarize_kwargs (lightning.pytorch.callbacks.ModelSummary parameter), [1], [2] (lightning.pytorch.callbacks.RichModelSummary parameter), [1], [2] *args (lightning.pytorch.core.LightningModule.forward parameter) (lightning.pytorch.core.LightningModule.manual_backward parameter) (lightning.pytorch.core.LightningModule.print parameter) (lightning.pytorch.core.module.LightningModule.forward parameter) (lightning.pytorch.core.module.LightningModule.manual_backward parameter) (lightning.pytorch.core.module.LightningModule.print parameter) (lightning.pytorch.plugins.precision.DeepSpeedPrecision.backward parameter) (lightning.pytorch.plugins.precision.Precision.backward parameter) (lightning.pytorch.strategies.SingleDeviceStrategy.reduce parameter) (lightning.pytorch.strategies.Strategy.backward parameter)

A

Accelerator (class in lightning.pytorch.accelerators) accelerator (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] accumulate_grad_batches (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] activation_checkpointing (lightning.pytorch.strategies.FSDPStrategy parameter), [1] activation_checkpointing_policy (lightning.pytorch.strategies.FSDPStrategy parameter), [1] add_arguments_to_parser() (lightning.pytorch.cli.LightningCLI method) add_core_arguments_to_parser() (lightning.pytorch.cli.LightningCLI method) add_dataloader_idx (lightning.pytorch.core.LightningModule.log parameter) (lightning.pytorch.core.LightningModule.log_dict parameter) (lightning.pytorch.core.module.LightningModule.log parameter) (lightning.pytorch.core.module.LightningModule.log_dict parameter) add_default_arguments_to_parser() (lightning.pytorch.cli.LightningCLI method) add_lightning_class_args() (lightning.pytorch.cli.LightningArgumentParser method) add_lr_scheduler_args() (lightning.pytorch.cli.LightningArgumentParser method) add_optimizer_args() (lightning.pytorch.cli.LightningArgumentParser method) AdvancedProfiler (class in lightning.pytorch.profilers) after_save_checkpoint() (lightning.pytorch.loggers.logger.Logger method) (lightning.pytorch.loggers.mlflow.MLFlowLogger method) (lightning.pytorch.loggers.MLFlowLogger method) (lightning.pytorch.loggers.neptune.NeptuneLogger method) (lightning.pytorch.loggers.NeptuneLogger method) (lightning.pytorch.loggers.tensorboard.TensorBoardLogger method) (lightning.pytorch.loggers.TensorBoardLogger method) (lightning.pytorch.loggers.wandb.WandbLogger method) (lightning.pytorch.loggers.WandbLogger method) agg_key_funcs (lightning.pytorch.loggers.logger.merge_dicts parameter), [1] all (lightning.pytorch.strategies.ParallelStrategy.reduce_boolean_decision parameter) all_gather() (lightning.pytorch.core.LightningModule method) (lightning.pytorch.strategies.ParallelStrategy method) (lightning.pytorch.strategies.SingleDeviceStrategy method) (lightning.pytorch.strategies.Strategy method) (lightning.pytorch.strategies.XLAStrategy method) allgather_bucket_size (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] allgather_partitions (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] allow_zero_length_dataloader_with_multiple_devices (lightning.pytorch.core.hooks.DataHooks attribute) (lightning.pytorch.core.LightningDataModule attribute) amount (lightning.pytorch.callbacks.ModelPruning parameter), [1], [2] annealing_epochs (lightning.pytorch.callbacks.StochasticWeightAveraging parameter), [1], [2] annealing_strategy (lightning.pytorch.callbacks.StochasticWeightAveraging parameter), [1], [2] anonymous (lightning.pytorch.loggers.wandb.WandbLogger parameter), [1] (lightning.pytorch.loggers.WandbLogger parameter), [1]

api_key (lightning.pytorch.loggers.comet.CometLogger parameter), [1] (lightning.pytorch.loggers.CometLogger parameter), [1] (lightning.pytorch.loggers.neptune.NeptuneLogger parameter), [1] (lightning.pytorch.loggers.NeptuneLogger parameter), [1] apply() (lightning.pytorch.plugins.LayerSync method) (lightning.pytorch.plugins.TorchSyncBatchNorm method) apply_lottery_ticket_hypothesis() (lightning.pytorch.callbacks.ModelPruning method) apply_pruning (lightning.pytorch.callbacks.ModelPruning parameter), [1], [2] apply_pruning() (lightning.pytorch.callbacks.ModelPruning method) args (lightning.pytorch.cli.LightningCLI parameter), [1] (lightning.pytorch.core.mixins.HyperparametersMixin.save_hyperparameters parameter) (lightning.pytorch.core.module.LightningModule.save_hyperparameters parameter) (lightning.pytorch.loggers.comet.CometLogger.log_hyperparams parameter) (lightning.pytorch.loggers.CometLogger.log_hyperparams parameter) (lightning.pytorch.loggers.csv_logs.CSVLogger.log_hyperparams parameter) (lightning.pytorch.loggers.CSVLogger.log_hyperparams parameter) (lightning.pytorch.loggers.logger.DummyLogger.log_hyperparams parameter) (lightning.pytorch.loggers.mlflow.MLFlowLogger.log_hyperparams parameter) (lightning.pytorch.loggers.MLFlowLogger.log_hyperparams parameter) (lightning.pytorch.loggers.wandb.WandbLogger.log_hyperparams parameter) (lightning.pytorch.loggers.WandbLogger.log_hyperparams parameter) artifact (lightning.pytorch.loggers.wandb.WandbLogger.download_artifact parameter) (lightning.pytorch.loggers.wandb.WandbLogger.use_artifact parameter) (lightning.pytorch.loggers.WandbLogger.download_artifact parameter) (lightning.pytorch.loggers.WandbLogger.use_artifact parameter) artifact_location (lightning.pytorch.loggers.mlflow.MLFlowLogger parameter), [1] (lightning.pytorch.loggers.MLFlowLogger parameter), [1] artifact_type (lightning.pytorch.loggers.wandb.WandbLogger.download_artifact parameter) (lightning.pytorch.loggers.wandb.WandbLogger.use_artifact parameter) (lightning.pytorch.loggers.WandbLogger.download_artifact parameter) (lightning.pytorch.loggers.WandbLogger.use_artifact parameter) AsyncCheckpointIO (class in lightning.pytorch.plugins.io) attr_name (lightning.pytorch.callbacks.LearningRateFinder parameter), [1], [2] (lightning.pytorch.tuner.tuning.Tuner.lr_find parameter) AttributeDict (class in lightning.pytorch.utilities.parsing) auto_device_count() (lightning.pytorch.accelerators.CPUAccelerator static method) (lightning.pytorch.accelerators.CUDAAccelerator static method) auto_insert_metric_name (lightning.pytorch.callbacks.ModelCheckpoint parameter), [1], [2] auto_requeue (lightning.pytorch.plugins.environments.SLURMEnvironment parameter), [1], [2] auto_wrap_policy (lightning.pytorch.strategies.FSDPStrategy parameter), [1] automatic_optimization (lightning.pytorch.core.LightningModule property) avg_fn (lightning.pytorch.callbacks.StochasticWeightAveraging parameter), [1], [2] avg_fn() (lightning.pytorch.callbacks.StochasticWeightAveraging static method)

B

backbone_initial_lr (lightning.pytorch.callbacks.BackboneFinetuning parameter), [1], [2] backbone_initial_ratio_lr (lightning.pytorch.callbacks.BackboneFinetuning parameter), [1], [2] BackboneFinetuning (class in lightning.pytorch.callbacks) backward() (lightning.pytorch.core.LightningModule method) (lightning.pytorch.plugins.precision.DeepSpeedPrecision method) (lightning.pytorch.plugins.precision.Precision method) (lightning.pytorch.strategies.Strategy method) barebones (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] barrier() (lightning.pytorch.strategies.DDPStrategy method) (lightning.pytorch.strategies.FSDPStrategy method) (lightning.pytorch.strategies.SingleDeviceStrategy method) (lightning.pytorch.strategies.Strategy method) (lightning.pytorch.strategies.XLAStrategy method) BaseFinetuning (class in lightning.pytorch.callbacks) BasePredictionWriter (class in lightning.pytorch.callbacks) batch (lightning.pytorch.core.datamodule.LightningDataModule.on_after_batch_transfer parameter) (lightning.pytorch.core.datamodule.LightningDataModule.on_before_batch_transfer parameter) (lightning.pytorch.core.datamodule.LightningDataModule.transfer_batch_to_device parameter) (lightning.pytorch.core.hooks.DataHooks.on_after_batch_transfer parameter) (lightning.pytorch.core.hooks.DataHooks.on_before_batch_transfer parameter) (lightning.pytorch.core.hooks.DataHooks.transfer_batch_to_device parameter) (lightning.pytorch.core.hooks.ModelHooks.on_predict_batch_end parameter) (lightning.pytorch.core.hooks.ModelHooks.on_predict_batch_start parameter) (lightning.pytorch.core.hooks.ModelHooks.on_test_batch_end parameter) (lightning.pytorch.core.hooks.ModelHooks.on_test_batch_start parameter) (lightning.pytorch.core.hooks.ModelHooks.on_train_batch_end parameter) (lightning.pytorch.core.hooks.ModelHooks.on_train_batch_start parameter) (lightning.pytorch.core.hooks.ModelHooks.on_validation_batch_end parameter) (lightning.pytorch.core.hooks.ModelHooks.on_validation_batch_start parameter) (lightning.pytorch.core.LightningModule.predict_step parameter) (lightning.pytorch.core.LightningModule.test_step parameter) (lightning.pytorch.core.LightningModule.training_step parameter) (lightning.pytorch.core.LightningModule.validation_step parameter) (lightning.pytorch.core.module.LightningModule.on_after_batch_transfer parameter) (lightning.pytorch.core.module.LightningModule.on_before_batch_transfer parameter) (lightning.pytorch.core.module.LightningModule.on_predict_batch_end parameter) (lightning.pytorch.core.module.LightningModule.on_predict_batch_start parameter) (lightning.pytorch.core.module.LightningModule.on_test_batch_end parameter) (lightning.pytorch.core.module.LightningModule.on_test_batch_start parameter) (lightning.pytorch.core.module.LightningModule.on_train_batch_end parameter) (lightning.pytorch.core.module.LightningModule.on_train_batch_start parameter) (lightning.pytorch.core.module.LightningModule.on_validation_batch_end parameter) (lightning.pytorch.core.module.LightningModule.on_validation_batch_start parameter) (lightning.pytorch.core.module.LightningModule.predict_step parameter) (lightning.pytorch.core.module.LightningModule.test_step parameter) (lightning.pytorch.core.module.LightningModule.training_step parameter) (lightning.pytorch.core.module.LightningModule.transfer_batch_to_device parameter) (lightning.pytorch.core.module.LightningModule.validation_step parameter) (lightning.pytorch.strategies.Strategy.batch_to_device parameter)

batch_arg_name (lightning.pytorch.callbacks.BatchSizeFinder parameter), [1], [2] (lightning.pytorch.tuner.tuning.Tuner.scale_batch_size parameter) batch_idx (lightning.pytorch.core.hooks.ModelHooks.on_predict_batch_end parameter) (lightning.pytorch.core.hooks.ModelHooks.on_predict_batch_start parameter) (lightning.pytorch.core.hooks.ModelHooks.on_test_batch_end parameter) (lightning.pytorch.core.hooks.ModelHooks.on_test_batch_start parameter) (lightning.pytorch.core.hooks.ModelHooks.on_train_batch_end parameter) (lightning.pytorch.core.hooks.ModelHooks.on_train_batch_start parameter) (lightning.pytorch.core.hooks.ModelHooks.on_validation_batch_end parameter) (lightning.pytorch.core.hooks.ModelHooks.on_validation_batch_start parameter) (lightning.pytorch.core.LightningModule.optimizer_step parameter) (lightning.pytorch.core.LightningModule.optimizer_zero_grad parameter) (lightning.pytorch.core.LightningModule.predict_step parameter) (lightning.pytorch.core.LightningModule.test_step parameter) (lightning.pytorch.core.LightningModule.training_step parameter) (lightning.pytorch.core.LightningModule.validation_step parameter) (lightning.pytorch.core.module.LightningModule.on_predict_batch_end parameter) (lightning.pytorch.core.module.LightningModule.on_predict_batch_start parameter) (lightning.pytorch.core.module.LightningModule.on_test_batch_end parameter) (lightning.pytorch.core.module.LightningModule.on_test_batch_start parameter) (lightning.pytorch.core.module.LightningModule.on_train_batch_end parameter) (lightning.pytorch.core.module.LightningModule.on_train_batch_start parameter) (lightning.pytorch.core.module.LightningModule.on_validation_batch_end parameter) (lightning.pytorch.core.module.LightningModule.on_validation_batch_start parameter) (lightning.pytorch.core.module.LightningModule.optimizer_step parameter) (lightning.pytorch.core.module.LightningModule.optimizer_zero_grad parameter) (lightning.pytorch.core.module.LightningModule.predict_step parameter) (lightning.pytorch.core.module.LightningModule.test_step parameter) (lightning.pytorch.core.module.LightningModule.training_step parameter) (lightning.pytorch.core.module.LightningModule.validation_step parameter) batch_sampler (lightning.pytorch.utilities.combined_loader.CombinedLoader property) batch_size (lightning.pytorch.core.LightningDataModule.from_datasets parameter) (lightning.pytorch.core.LightningModule.log parameter) (lightning.pytorch.core.LightningModule.log_dict parameter) (lightning.pytorch.core.module.LightningModule.log parameter) (lightning.pytorch.core.module.LightningModule.log_dict parameter) batch_to_device() (lightning.pytorch.strategies.Strategy method) BatchSizeFinder (class in lightning.pytorch.callbacks) before_instantiate_classes() (lightning.pytorch.cli.LightningCLI method) benchmark (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] BitsandbytesPrecision (class in lightning.pytorch.plugins.precision) block_backward_sync() (lightning.pytorch.strategies.ParallelStrategy method) block_size (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] broadcast() (lightning.pytorch.strategies.DDPStrategy method) (lightning.pytorch.strategies.FSDPStrategy method) (lightning.pytorch.strategies.SingleDeviceStrategy method) (lightning.pytorch.strategies.Strategy method) (lightning.pytorch.strategies.XLAStrategy method)

C

Callback (class in lightning.pytorch.callbacks) callback_metrics (lightning.pytorch.trainer.trainer.Trainer property) callbacks (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] check_finite (lightning.pytorch.callbacks.EarlyStopping parameter), [1], [2] check_on_train_epoch_end (lightning.pytorch.callbacks.EarlyStopping parameter), [1], [2] check_val_every_n_epoch (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] checkpoint (lightning.pytorch.callbacks.Callback.on_load_checkpoint parameter), [1] (lightning.pytorch.callbacks.Callback.on_save_checkpoint parameter), [1] (lightning.pytorch.callbacks.ModelPruning.on_save_checkpoint parameter) (lightning.pytorch.core.hooks.CheckpointHooks.on_load_checkpoint parameter) (lightning.pytorch.core.hooks.CheckpointHooks.on_save_checkpoint parameter) (lightning.pytorch.core.module.LightningModule.on_load_checkpoint parameter) (lightning.pytorch.core.module.LightningModule.on_save_checkpoint parameter) (lightning.pytorch.plugins.io.CheckpointIO.save_checkpoint parameter) (lightning.pytorch.plugins.io.TorchCheckpointIO.save_checkpoint parameter) (lightning.pytorch.plugins.io.XLACheckpointIO.save_checkpoint parameter) (lightning.pytorch.strategies.DeepSpeedStrategy.save_checkpoint parameter) (lightning.pytorch.strategies.FSDPStrategy.save_checkpoint parameter) (lightning.pytorch.strategies.Strategy.save_checkpoint parameter) (lightning.pytorch.strategies.XLAStrategy.save_checkpoint parameter) checkpoint_callback (lightning.pytorch.loggers.logger.Logger.after_save_checkpoint parameter) (lightning.pytorch.loggers.mlflow.MLFlowLogger.after_save_checkpoint parameter) (lightning.pytorch.loggers.MLFlowLogger.after_save_checkpoint parameter) (lightning.pytorch.loggers.neptune.NeptuneLogger.after_save_checkpoint parameter) (lightning.pytorch.loggers.NeptuneLogger.after_save_checkpoint parameter) (lightning.pytorch.loggers.tensorboard.TensorBoardLogger.after_save_checkpoint parameter) (lightning.pytorch.loggers.TensorBoardLogger.after_save_checkpoint parameter) (lightning.pytorch.loggers.wandb.WandbLogger.after_save_checkpoint parameter) (lightning.pytorch.loggers.WandbLogger.after_save_checkpoint parameter) (lightning.pytorch.trainer.trainer.Trainer property) checkpoint_callbacks (lightning.pytorch.trainer.trainer.Trainer property) checkpoint_dir (lightning.pytorch.utilities.deepspeed.convert_zero_checkpoint_to_fp32_state_dict parameter), [1] checkpoint_io (lightning.pytorch.plugins.io.AsyncCheckpointIO parameter), [1], [2] checkpoint_name (lightning.pytorch.loggers.wandb.WandbLogger parameter), [1] (lightning.pytorch.loggers.WandbLogger parameter), [1] checkpoint_path (lightning.pytorch.core.LightningDataModule.load_from_checkpoint parameter) (lightning.pytorch.core.LightningModule.load_from_checkpoint parameter) (lightning.pytorch.core.module.LightningModule.load_from_checkpoint parameter) CheckpointHooks (class in lightning.pytorch.core.hooks) CheckpointIO (class in lightning.pytorch.plugins.io) ckpt_path (lightning.pytorch.trainer.Trainer.fit parameter) (lightning.pytorch.trainer.Trainer.predict parameter) (lightning.pytorch.trainer.Trainer.test parameter), [1] (lightning.pytorch.trainer.trainer.Trainer property) (lightning.pytorch.trainer.trainer.Trainer.fit parameter) (lightning.pytorch.trainer.trainer.Trainer.predict parameter) (lightning.pytorch.trainer.trainer.Trainer.test parameter) (lightning.pytorch.trainer.trainer.Trainer.validate parameter) (lightning.pytorch.trainer.Trainer.validate parameter), [1] classes (lightning.pytorch.utilities.parsing.collect_init_args parameter), [1] clean_namespace() (in module lightning.pytorch.utilities.parsing) clip_grad_by_norm() (lightning.pytorch.plugins.precision.FSDPPrecision method) (lightning.pytorch.plugins.precision.Precision method)

clip_grad_by_value() (lightning.pytorch.plugins.precision.Precision method) clip_gradients() (lightning.pytorch.core.LightningModule method) (lightning.pytorch.plugins.precision.DeepSpeedPrecision method) (lightning.pytorch.plugins.precision.MixedPrecision method) (lightning.pytorch.plugins.precision.Precision method) closure (lightning.pytorch.core.optimizer.LightningOptimizer.step parameter) (lightning.pytorch.strategies.DDPStrategy.optimizer_step parameter) (lightning.pytorch.strategies.Strategy.optimizer_step parameter) closure_loss (lightning.pytorch.strategies.Strategy.backward parameter) ClusterEnvironment (class in lightning.pytorch.plugins.environments) collect_init_args() (in module lightning.pytorch.utilities.parsing) CombinedLoader (class in lightning.pytorch.utilities.combined_loader) CometLogger (class in lightning.pytorch.loggers) (class in lightning.pytorch.loggers.comet) config (lightning.pytorch.cli.SaveConfigCallback parameter), [1] (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] config_filename (lightning.pytorch.cli.SaveConfigCallback parameter), [1] configure_callbacks() (lightning.pytorch.core.LightningModule method) configure_gradient_clipping() (lightning.pytorch.core.LightningModule method) configure_model() (lightning.pytorch.core.hooks.ModelHooks method) configure_optimizers() (lightning.pytorch.cli.LightningCLI static method) (lightning.pytorch.core.LightningModule method) configure_sharded_model() (lightning.pytorch.core.hooks.ModelHooks method) connect() (lightning.pytorch.plugins.precision.Precision method) (lightning.pytorch.strategies.Strategy method) console_kwargs (lightning.pytorch.callbacks.RichProgressBar parameter), [1], [2] contiguous_gradients (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] contiguous_memory_optimization (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] convert_input() (lightning.pytorch.plugins.precision.DeepSpeedPrecision method) (lightning.pytorch.plugins.precision.DoublePrecision method) (lightning.pytorch.plugins.precision.FSDPPrecision method) (lightning.pytorch.plugins.precision.HalfPrecision method) convert_module() (lightning.pytorch.plugins.precision.DeepSpeedPrecision method) (lightning.pytorch.plugins.precision.DoublePrecision method) (lightning.pytorch.plugins.precision.HalfPrecision method) convert_output() (lightning.pytorch.plugins.precision.FSDPPrecision method) convert_zero_checkpoint_to_fp32_state_dict() (in module lightning.pytorch.utilities.deepspeed) cpu_checkpointing (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] cpu_offload (lightning.pytorch.strategies.FSDPStrategy parameter), [1] cpu_stats (lightning.pytorch.callbacks.DeviceStatsMonitor parameter), [1], [2] CPUAccelerator (class in lightning.pytorch.accelerators) creates_processes_externally (lightning.pytorch.plugins.environments.ClusterEnvironment property) (lightning.pytorch.plugins.environments.KubeflowEnvironment property) (lightning.pytorch.plugins.environments.LightningEnvironment property) (lightning.pytorch.plugins.environments.LSFEnvironment property) (lightning.pytorch.plugins.environments.MPIEnvironment property) (lightning.pytorch.plugins.environments.SLURMEnvironment property) (lightning.pytorch.plugins.environments.TorchElasticEnvironment property) (lightning.pytorch.plugins.environments.XLAEnvironment property) CSVLogger (class in lightning.pytorch.loggers) (class in lightning.pytorch.loggers.csv_logs) CUDAAccelerator (class in lightning.pytorch.accelerators) current_epoch (lightning.pytorch.core.LightningModule property) (lightning.pytorch.trainer.trainer.Trainer property)

D

data (lightning.pytorch.core.LightningModule.all_gather parameter) (lightning.pytorch.core.module.LightningModule.all_gather parameter) DataHooks (class in lightning.pytorch.core.hooks) dataloader (lightning.pytorch.strategies.Strategy.process_dataloader parameter) (lightning.pytorch.strategies.XLAStrategy.process_dataloader parameter) dataloader_idx (lightning.pytorch.core.datamodule.LightningDataModule.on_after_batch_transfer parameter) (lightning.pytorch.core.datamodule.LightningDataModule.on_before_batch_transfer parameter) (lightning.pytorch.core.datamodule.LightningDataModule.transfer_batch_to_device parameter) (lightning.pytorch.core.hooks.DataHooks.on_after_batch_transfer parameter) (lightning.pytorch.core.hooks.DataHooks.on_before_batch_transfer parameter) (lightning.pytorch.core.hooks.DataHooks.transfer_batch_to_device parameter) (lightning.pytorch.core.hooks.ModelHooks.on_predict_batch_end parameter) (lightning.pytorch.core.hooks.ModelHooks.on_predict_batch_start parameter) (lightning.pytorch.core.hooks.ModelHooks.on_test_batch_end parameter) (lightning.pytorch.core.hooks.ModelHooks.on_test_batch_start parameter) (lightning.pytorch.core.hooks.ModelHooks.on_validation_batch_end parameter) (lightning.pytorch.core.hooks.ModelHooks.on_validation_batch_start parameter) (lightning.pytorch.core.LightningModule.predict_step parameter) (lightning.pytorch.core.LightningModule.test_step parameter) (lightning.pytorch.core.LightningModule.training_step parameter) (lightning.pytorch.core.LightningModule.validation_step parameter) (lightning.pytorch.core.module.LightningModule.on_after_batch_transfer parameter) (lightning.pytorch.core.module.LightningModule.on_before_batch_transfer parameter) (lightning.pytorch.core.module.LightningModule.on_predict_batch_end parameter) (lightning.pytorch.core.module.LightningModule.on_predict_batch_start parameter) (lightning.pytorch.core.module.LightningModule.on_test_batch_end parameter) (lightning.pytorch.core.module.LightningModule.on_test_batch_start parameter) (lightning.pytorch.core.module.LightningModule.on_validation_batch_end parameter) (lightning.pytorch.core.module.LightningModule.on_validation_batch_start parameter) (lightning.pytorch.core.module.LightningModule.predict_step parameter) (lightning.pytorch.core.module.LightningModule.test_step parameter) (lightning.pytorch.core.module.LightningModule.training_step parameter) (lightning.pytorch.core.module.LightningModule.transfer_batch_to_device parameter) (lightning.pytorch.core.module.LightningModule.validation_step parameter) (lightning.pytorch.strategies.Strategy.batch_to_device parameter) dataloaders (lightning.pytorch.trainer.Trainer.predict parameter) (lightning.pytorch.trainer.Trainer.test parameter), [1] (lightning.pytorch.trainer.trainer.Trainer.predict parameter) (lightning.pytorch.trainer.trainer.Trainer.test parameter) (lightning.pytorch.trainer.trainer.Trainer.validate parameter) (lightning.pytorch.trainer.Trainer.validate parameter), [1] (lightning.pytorch.tuner.tuning.Tuner.lr_find parameter) (lightning.pytorch.tuner.tuning.Tuner.scale_batch_size parameter) datamodule (lightning.pytorch.trainer.Trainer.fit parameter) (lightning.pytorch.trainer.Trainer.predict parameter) (lightning.pytorch.trainer.Trainer.test parameter), [1] (lightning.pytorch.trainer.trainer.Trainer.fit parameter) (lightning.pytorch.trainer.trainer.Trainer.predict parameter) (lightning.pytorch.trainer.trainer.Trainer.test parameter) (lightning.pytorch.trainer.trainer.Trainer.validate parameter) (lightning.pytorch.trainer.Trainer.validate parameter), [1] (lightning.pytorch.tuner.tuning.Tuner.lr_find parameter) (lightning.pytorch.tuner.tuning.Tuner.scale_batch_size parameter) datamodule_class (lightning.pytorch.cli.LightningCLI parameter), [1] DDPStrategy (class in lightning.pytorch.strategies) decision (lightning.pytorch.strategies.ParallelStrategy.reduce_boolean_decision parameter)

DeepSpeedPrecision (class in lightning.pytorch.plugins.precision) DeepSpeedStrategy (class in lightning.pytorch.strategies) default_env (lightning.pytorch.cli.LightningArgumentParser parameter), [1] default_func (lightning.pytorch.loggers.logger.merge_dicts parameter), [1] default_hp_metric (lightning.pytorch.loggers.tensorboard.TensorBoardLogger parameter), [1] (lightning.pytorch.loggers.TensorBoardLogger parameter), [1] default_root_dir (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] (lightning.pytorch.trainer.trainer.Trainer property) describe() (lightning.pytorch.profilers.Profiler method) description (lightning.pytorch.cli.LightningArgumentParser parameter), [1] detect() (lightning.pytorch.plugins.environments.ClusterEnvironment static method) (lightning.pytorch.plugins.environments.KubeflowEnvironment static method) (lightning.pytorch.plugins.environments.LightningEnvironment static method) (lightning.pytorch.plugins.environments.LSFEnvironment static method) (lightning.pytorch.plugins.environments.MPIEnvironment static method) (lightning.pytorch.plugins.environments.SLURMEnvironment static method) (lightning.pytorch.plugins.environments.TorchElasticEnvironment static method) (lightning.pytorch.plugins.environments.XLAEnvironment static method) detect_anomaly (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] deterministic (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] device (lightning.pytorch.accelerators.Accelerator.get_device_stats parameter) (lightning.pytorch.accelerators.CUDAAccelerator.get_device_stats parameter) (lightning.pytorch.accelerators.XLAAccelerator.get_device_stats parameter) (lightning.pytorch.callbacks.StochasticWeightAveraging parameter), [1], [2] (lightning.pytorch.core.datamodule.LightningDataModule.transfer_batch_to_device parameter) (lightning.pytorch.core.hooks.DataHooks.transfer_batch_to_device parameter) (lightning.pytorch.core.module.LightningModule.transfer_batch_to_device parameter) (lightning.pytorch.plugins.precision.MixedPrecision parameter), [1], [2] (lightning.pytorch.strategies.Strategy.batch_to_device parameter) device_ids (lightning.pytorch.trainer.trainer.Trainer property) devices (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] DeviceStatsMonitor (class in lightning.pytorch.callbacks) dictionary (lightning.pytorch.core.LightningModule.log_dict parameter) (lightning.pytorch.core.module.LightningModule.log_dict parameter) dicts (lightning.pytorch.loggers.logger.merge_dicts parameter), [1] dir (lightning.pytorch.loggers.wandb.WandbLogger parameter), [1] (lightning.pytorch.loggers.WandbLogger parameter), [1] dirpath (lightning.pytorch.callbacks.ModelCheckpoint parameter), [1], [2] (lightning.pytorch.callbacks.OnExceptionCheckpoint parameter), [1] (lightning.pytorch.profilers.AdvancedProfiler parameter), [1] (lightning.pytorch.profilers.PyTorchProfiler parameter), [1] (lightning.pytorch.profilers.SimpleProfiler parameter), [1] disable() (lightning.pytorch.callbacks.ProgressBar method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.TQDMProgressBar method) divergence_threshold (lightning.pytorch.callbacks.EarlyStopping parameter), [1], [2] DoublePrecision (class in lightning.pytorch.plugins.precision) download_artifact() (lightning.pytorch.loggers.wandb.WandbLogger static method) (lightning.pytorch.loggers.WandbLogger static method) dtype (lightning.pytorch.plugins.precision.BitsandbytesPrecision parameter), [1], [2] (lightning.pytorch.plugins.precision.TransformerEnginePrecision parameter), [1], [2] DummyLogger (class in lightning.pytorch.loggers.logger) duration (lightning.pytorch.callbacks.Timer parameter), [1], [2]

E

early_stop_threshold (lightning.pytorch.callbacks.LearningRateFinder parameter), [1], [2] (lightning.pytorch.tuner.tuning.Tuner.lr_find parameter) early_stopping_callback (lightning.pytorch.trainer.trainer.Trainer property) early_stopping_callbacks (lightning.pytorch.trainer.trainer.Trainer property) EarlyStopping (class in lightning.pytorch.callbacks) emit_nvtx (lightning.pytorch.profilers.PyTorchProfiler parameter), [1] empty_init (lightning.pytorch.strategies.DeepSpeedStrategy.tensor_init_context parameter) (lightning.pytorch.strategies.FSDPStrategy.tensor_init_context parameter) (lightning.pytorch.strategies.Strategy.tensor_init_context parameter) (lightning.pytorch.trainer.trainer.Trainer.init_module parameter) enable() (lightning.pytorch.callbacks.ProgressBar method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.TQDMProgressBar method) enable_checkpointing (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] enable_graph (lightning.pytorch.core.LightningModule.log parameter) (lightning.pytorch.core.LightningModule.log_dict parameter) (lightning.pytorch.core.module.LightningModule.log parameter) (lightning.pytorch.core.module.LightningModule.log_dict parameter) enable_model_summary (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] enable_progress_bar (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] enable_validation (lightning.pytorch.trainer.trainer.Trainer property) enable_version_counter (lightning.pytorch.callbacks.ModelCheckpoint parameter), [1], [2] end_time() (lightning.pytorch.callbacks.Timer method) env_prefix (lightning.pytorch.cli.LightningArgumentParser parameter), [1] epoch (lightning.pytorch.core.LightningModule.optimizer_step parameter) (lightning.pytorch.core.LightningModule.optimizer_zero_grad parameter) (lightning.pytorch.core.module.LightningModule.optimizer_step parameter) (lightning.pytorch.core.module.LightningModule.optimizer_zero_grad parameter)

estimated_stepping_batches (lightning.pytorch.trainer.trainer.Trainer property) every_n_epochs (lightning.pytorch.callbacks.ModelCheckpoint parameter), [1], [2] every_n_train_steps (lightning.pytorch.callbacks.ModelCheckpoint parameter), [1], [2] example_input_array (lightning.pytorch.core.LightningModule property) example_inputs (lightning.pytorch.core.LightningModule.to_torchscript parameter) (lightning.pytorch.core.module.LightningModule.to_torchscript parameter) experiment (lightning.pytorch.loggers.comet.CometLogger property) (lightning.pytorch.loggers.CometLogger property) (lightning.pytorch.loggers.csv_logs.CSVLogger property) (lightning.pytorch.loggers.CSVLogger property) (lightning.pytorch.loggers.logger.DummyLogger property) (lightning.pytorch.loggers.mlflow.MLFlowLogger property) (lightning.pytorch.loggers.MLFlowLogger property) (lightning.pytorch.loggers.neptune.NeptuneLogger property) (lightning.pytorch.loggers.NeptuneLogger property) (lightning.pytorch.loggers.wandb.WandbLogger parameter), [1] (lightning.pytorch.loggers.wandb.WandbLogger property) (lightning.pytorch.loggers.WandbLogger parameter), [1] (lightning.pytorch.loggers.WandbLogger property) experiment_id (lightning.pytorch.loggers.mlflow.MLFlowLogger property) (lightning.pytorch.loggers.MLFlowLogger property) experiment_key (lightning.pytorch.loggers.comet.CometLogger parameter), [1] (lightning.pytorch.loggers.CometLogger parameter), [1] experiment_name (lightning.pytorch.loggers.comet.CometLogger parameter), [1] (lightning.pytorch.loggers.CometLogger parameter), [1] (lightning.pytorch.loggers.mlflow.MLFlowLogger parameter), [1] (lightning.pytorch.loggers.MLFlowLogger parameter), [1] ExperimentWriter (class in lightning.pytorch.loggers.csv_logs) export_to_chrome (lightning.pytorch.profilers.PyTorchProfiler parameter), [1] extended (lightning.pytorch.profilers.SimpleProfiler parameter), [1] extract_batch_size() (in module lightning.pytorch.utilities.data)

F

fast_dev_run (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] file_exists() (lightning.pytorch.callbacks.ModelCheckpoint method) file_path (lightning.pytorch.core.LightningModule.to_onnx parameter) (lightning.pytorch.core.LightningModule.to_torchscript parameter) (lightning.pytorch.core.module.LightningModule.to_onnx parameter) (lightning.pytorch.core.module.LightningModule.to_torchscript parameter) filename (lightning.pytorch.callbacks.ModelCheckpoint parameter), [1], [2] (lightning.pytorch.callbacks.OnExceptionCheckpoint parameter), [1] (lightning.pytorch.profilers.AdvancedProfiler parameter), [1] (lightning.pytorch.profilers.PyTorchProfiler parameter), [1] (lightning.pytorch.profilers.SimpleProfiler parameter), [1] filepath (lightning.pytorch.strategies.DeepSpeedStrategy.save_checkpoint parameter) (lightning.pytorch.strategies.FSDPStrategy.save_checkpoint parameter) (lightning.pytorch.strategies.Strategy.remove_checkpoint parameter) (lightning.pytorch.strategies.Strategy.save_checkpoint parameter) (lightning.pytorch.strategies.XLAStrategy.remove_checkpoint parameter) (lightning.pytorch.strategies.XLAStrategy.save_checkpoint parameter) (lightning.pytorch.trainer.trainer.Trainer.save_checkpoint parameter) filter_on_optimizer() (lightning.pytorch.callbacks.BaseFinetuning static method) filter_parameters_to_prune() (lightning.pytorch.callbacks.ModelPruning method) filter_params() (lightning.pytorch.callbacks.BaseFinetuning static method) finalize() (lightning.pytorch.loggers.comet.CometLogger method) (lightning.pytorch.loggers.CometLogger method) (lightning.pytorch.loggers.mlflow.MLFlowLogger method) (lightning.pytorch.loggers.MLFlowLogger method) (lightning.pytorch.loggers.neptune.NeptuneLogger method) (lightning.pytorch.loggers.NeptuneLogger method) (lightning.pytorch.loggers.tensorboard.TensorBoardLogger method) (lightning.pytorch.loggers.TensorBoardLogger method) (lightning.pytorch.loggers.wandb.WandbLogger method) (lightning.pytorch.loggers.WandbLogger method)

finetune_function() (lightning.pytorch.callbacks.BackboneFinetuning method) (lightning.pytorch.callbacks.BaseFinetuning method) fit() (lightning.pytorch.trainer.trainer.Trainer method) flatten_modules() (lightning.pytorch.callbacks.BaseFinetuning static method) flattened (lightning.pytorch.utilities.combined_loader.CombinedLoader property) flush_logs_every_n_steps (lightning.pytorch.loggers.csv_logs.CSVLogger parameter), [1] (lightning.pytorch.loggers.CSVLogger parameter), [1] format_checkpoint_name() (lightning.pytorch.callbacks.ModelCheckpoint method) forward() (lightning.pytorch.core.LightningModule method) forward_context() (lightning.pytorch.plugins.precision.DoublePrecision method) (lightning.pytorch.plugins.precision.FSDPPrecision method) (lightning.pytorch.plugins.precision.HalfPrecision method) (lightning.pytorch.plugins.precision.MixedPrecision method) frame (lightning.pytorch.core.mixins.HyperparametersMixin.save_hyperparameters parameter) (lightning.pytorch.core.module.LightningModule.save_hyperparameters parameter) (lightning.pytorch.utilities.parsing.collect_init_args parameter), [1] freeze() (lightning.pytorch.callbacks.BaseFinetuning static method) (lightning.pytorch.core.LightningModule method) freeze_before_training() (lightning.pytorch.callbacks.BackboneFinetuning method) (lightning.pytorch.callbacks.BaseFinetuning method) freeze_module() (lightning.pytorch.callbacks.BaseFinetuning static method) from_datasets() (lightning.pytorch.core.LightningDataModule class method) FSDPPrecision (class in lightning.pytorch.plugins.precision) FSDPStrategy (class in lightning.pytorch.strategies)

G

garbage_collection_cuda() (in module lightning.pytorch.utilities.memory) get_device_stats() (lightning.pytorch.accelerators.Accelerator method) (lightning.pytorch.accelerators.CPUAccelerator method) (lightning.pytorch.accelerators.CUDAAccelerator method) (lightning.pytorch.accelerators.XLAAccelerator method) get_init_args() (in module lightning.pytorch.utilities.parsing) get_metrics() (lightning.pytorch.callbacks.ProgressBar method) get_parallel_devices() (lightning.pytorch.accelerators.CPUAccelerator static method) (lightning.pytorch.accelerators.CUDAAccelerator static method) global_rank (lightning.pytorch.core.LightningModule property) global_rank() (lightning.pytorch.plugins.environments.ClusterEnvironment method) (lightning.pytorch.plugins.environments.KubeflowEnvironment method) (lightning.pytorch.plugins.environments.LightningEnvironment method) (lightning.pytorch.plugins.environments.LSFEnvironment method) (lightning.pytorch.plugins.environments.MPIEnvironment method) (lightning.pytorch.plugins.environments.SLURMEnvironment method) (lightning.pytorch.plugins.environments.TorchElasticEnvironment method) (lightning.pytorch.plugins.environments.XLAEnvironment method) global_step (lightning.pytorch.core.LightningModule property) (lightning.pytorch.trainer.trainer.Trainer property)

gradient_clip_algorithm (lightning.pytorch.core.LightningModule.clip_gradients parameter) (lightning.pytorch.core.LightningModule.configure_gradient_clipping parameter) (lightning.pytorch.core.module.LightningModule.configure_gradient_clipping parameter) (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] gradient_clip_val (lightning.pytorch.core.LightningModule.clip_gradients parameter) (lightning.pytorch.core.LightningModule.configure_gradient_clipping parameter) (lightning.pytorch.core.module.LightningModule.configure_gradient_clipping parameter) (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] GradientAccumulationScheduler (class in lightning.pytorch.callbacks) group (lightning.pytorch.core.LightningModule.all_gather parameter) (lightning.pytorch.core.module.LightningModule.all_gather parameter) (lightning.pytorch.strategies.DDPStrategy.reduce parameter) (lightning.pytorch.strategies.FSDPStrategy.reduce parameter) (lightning.pytorch.strategies.Strategy.all_gather parameter) (lightning.pytorch.strategies.Strategy.reduce parameter) (lightning.pytorch.strategies.XLAStrategy.all_gather parameter) (lightning.pytorch.strategies.XLAStrategy.reduce parameter) group_by_input_shapes (lightning.pytorch.profilers.PyTorchProfiler parameter), [1]

H

HalfPrecision (class in lightning.pytorch.plugins.precision) handles_gradient_accumulation (lightning.pytorch.strategies.DeepSpeedStrategy property) (lightning.pytorch.strategies.Strategy property) has_len_all_ranks() (in module lightning.pytorch.utilities.data) hparams (lightning.pytorch.core.mixins.HyperparametersMixin property)

hparams_file (lightning.pytorch.core.LightningDataModule.load_from_checkpoint parameter) (lightning.pytorch.core.LightningModule.load_from_checkpoint parameter) (lightning.pytorch.core.module.LightningModule.load_from_checkpoint parameter) hparams_initial (lightning.pytorch.core.mixins.HyperparametersMixin property) HyperparametersMixin (class in lightning.pytorch.core.mixins) hysteresis (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1]

I

id (lightning.pytorch.loggers.wandb.WandbLogger parameter), [1] (lightning.pytorch.loggers.WandbLogger parameter), [1] ignore (lightning.pytorch.core.mixins.HyperparametersMixin.save_hyperparameters parameter) (lightning.pytorch.core.module.LightningModule.save_hyperparameters parameter) ignore_modules (lightning.pytorch.plugins.precision.BitsandbytesPrecision parameter), [1], [2] in_dict (lightning.pytorch.utilities.memory.recursive_detach parameter), [1] include_cuda (lightning.pytorch.utilities.seed.isolate_rng parameter), [1] inference_mode (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] init_module() (lightning.pytorch.trainer.trainer.Trainer method) init_parser() (lightning.pytorch.cli.LightningCLI method) init_predict_tqdm() (lightning.pytorch.callbacks.TQDMProgressBar method) init_sanity_tqdm() (lightning.pytorch.callbacks.TQDMProgressBar method) init_test_tqdm() (lightning.pytorch.callbacks.TQDMProgressBar method) init_train_tqdm() (lightning.pytorch.callbacks.TQDMProgressBar method) init_val (lightning.pytorch.callbacks.BatchSizeFinder parameter), [1], [2] (lightning.pytorch.tuner.tuning.Tuner.scale_batch_size parameter) init_validation_tqdm() (lightning.pytorch.callbacks.TQDMProgressBar method) initial_denom_lr (lightning.pytorch.callbacks.BackboneFinetuning parameter), [1], [2] (lightning.pytorch.callbacks.BaseFinetuning.unfreeze_and_add_param_group parameter) initial_scale_power (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1]

input_array (lightning.pytorch.loggers.comet.CometLogger.log_graph parameter) (lightning.pytorch.loggers.CometLogger.log_graph parameter) (lightning.pytorch.loggers.tensorboard.TensorBoardLogger.log_graph parameter) (lightning.pytorch.loggers.TensorBoardLogger.log_graph parameter) input_sample (lightning.pytorch.core.LightningModule.to_onnx parameter) (lightning.pytorch.core.module.LightningModule.to_onnx parameter) inside (lightning.pytorch.utilities.parsing.collect_init_args parameter), [1] instantiate_classes() (lightning.pytorch.cli.LightningCLI method) instantiate_trainer() (lightning.pytorch.cli.LightningCLI method) interval (lightning.pytorch.callbacks.Timer parameter), [1], [2] is_available() (lightning.pytorch.accelerators.CPUAccelerator static method) (lightning.pytorch.accelerators.CUDAAccelerator static method) is_distributed (lightning.pytorch.strategies.DDPStrategy property) is_global_zero (lightning.pytorch.strategies.ParallelStrategy property) (lightning.pytorch.strategies.SingleDeviceStrategy property) (lightning.pytorch.strategies.Strategy property) (lightning.pytorch.trainer.trainer.Trainer property) is_last_batch (lightning.pytorch.trainer.trainer.Trainer property) is_picklable() (in module lightning.pytorch.utilities.parsing) isolate_rng() (in module lightning.pytorch.utilities.seed) iterables (lightning.pytorch.utilities.combined_loader.CombinedLoader parameter), [1] (lightning.pytorch.utilities.combined_loader.CombinedLoader property)

K

KubeflowEnvironment (class in lightning.pytorch.plugins.environments) kwargs (lightning.pytorch.cli.LightningCLI.instantiate_trainer parameter) (lightning.pytorch.core.optimizer.LightningOptimizer.step parameter) (lightning.pytorch.loggers.comet.CometLogger.log_hyperparams parameter) (lightning.pytorch.loggers.CometLogger.log_hyperparams parameter) (lightning.pytorch.loggers.csv_logs.CSVLogger.log_hyperparams parameter) (lightning.pytorch.loggers.CSVLogger.log_hyperparams parameter) (lightning.pytorch.loggers.logger.DummyLogger.log_hyperparams parameter) (lightning.pytorch.loggers.mlflow.MLFlowLogger.log_hyperparams parameter) (lightning.pytorch.loggers.MLFlowLogger.log_hyperparams parameter) (lightning.pytorch.loggers.wandb.WandbLogger.log_hyperparams parameter) (lightning.pytorch.loggers.WandbLogger.log_hyperparams parameter)

L

lambda_func (lightning.pytorch.callbacks.BackboneFinetuning parameter), [1], [2] LambdaCallback (class in lightning.pytorch.callbacks) LayerSync (class in lightning.pytorch.plugins) LearningRateFinder (class in lightning.pytorch.callbacks) LearningRateMonitor (class in lightning.pytorch.callbacks) leave (lightning.pytorch.callbacks.RichProgressBar parameter), [1], [2] lightning.pytorch.loggers.comet module lightning.pytorch.loggers.csv_logs module lightning.pytorch.loggers.logger module lightning.pytorch.loggers.mlflow module lightning.pytorch.loggers.neptune module lightning.pytorch.loggers.tensorboard module lightning.pytorch.loggers.wandb module lightning.pytorch.utilities.combined_loader module lightning.pytorch.utilities.data module lightning.pytorch.utilities.deepspeed module lightning.pytorch.utilities.memory module lightning.pytorch.utilities.model_summary module lightning.pytorch.utilities.parsing module lightning.pytorch.utilities.rank_zero module lightning.pytorch.utilities.seed module lightning.pytorch.utilities.warnings module lightning_class (lightning.pytorch.cli.LightningArgumentParser.add_lightning_class_args parameter) lightning_getattr() (in module lightning.pytorch.utilities.parsing) lightning_hasattr() (in module lightning.pytorch.utilities.parsing) lightning_module (lightning.pytorch.cli.LightningCLI.configure_optimizers parameter) (lightning.pytorch.strategies.Strategy property) lightning_module_state_dict() (lightning.pytorch.strategies.FSDPStrategy method) (lightning.pytorch.strategies.Strategy method) lightning_restore_optimizer (lightning.pytorch.strategies.DeepSpeedStrategy property) (lightning.pytorch.strategies.FSDPStrategy property) (lightning.pytorch.strategies.Strategy property) lightning_setattr() (in module lightning.pytorch.utilities.parsing) LightningArgumentParser (class in lightning.pytorch.cli) LightningCLI (class in lightning.pytorch.cli) LightningDataModule (class in lightning.pytorch.core) LightningEnvironment (class in lightning.pytorch.plugins.environments) LightningModule (class in lightning.pytorch.core) LightningOptimizer (class in lightning.pytorch.core.optimizer) limit_predict_batches (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] limit_test_batches (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] limit_train_batches (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] limit_val_batches (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] limits (lightning.pytorch.utilities.combined_loader.CombinedLoader property) line_count_restriction (lightning.pytorch.profilers.AdvancedProfiler parameter), [1] link_optimizers_and_lr_schedulers() (lightning.pytorch.cli.LightningCLI static method) link_to (lightning.pytorch.cli.LightningArgumentParser.add_lr_scheduler_args parameter) (lightning.pytorch.cli.LightningArgumentParser.add_optimizer_args parameter) load_checkpoint() (lightning.pytorch.plugins.io.CheckpointIO method) (lightning.pytorch.plugins.io.TorchCheckpointIO method) load_from_checkpoint() (lightning.pytorch.core.LightningDataModule method) (lightning.pytorch.core.LightningModule method) load_full_weights (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] load_state_dict() (lightning.pytorch.callbacks.BackboneFinetuning method) (lightning.pytorch.callbacks.BaseFinetuning method) (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.EarlyStopping method) (lightning.pytorch.callbacks.ModelCheckpoint method) (lightning.pytorch.callbacks.StochasticWeightAveraging method) (lightning.pytorch.callbacks.Timer method) (lightning.pytorch.core.LightningDataModule method) (lightning.pytorch.plugins.precision.FSDPPrecision method) (lightning.pytorch.plugins.precision.MixedPrecision method) local_rank (lightning.pytorch.core.LightningModule property) local_rank() (lightning.pytorch.plugins.environments.ClusterEnvironment method) (lightning.pytorch.plugins.environments.KubeflowEnvironment method) (lightning.pytorch.plugins.environments.LightningEnvironment method) (lightning.pytorch.plugins.environments.LSFEnvironment method) (lightning.pytorch.plugins.environments.MPIEnvironment method) (lightning.pytorch.plugins.environments.SLURMEnvironment method) (lightning.pytorch.plugins.environments.TorchElasticEnvironment method) (lightning.pytorch.plugins.environments.XLAEnvironment method)

log() (lightning.pytorch.core.LightningModule method) log_dict() (lightning.pytorch.core.LightningModule method) log_dir (lightning.pytorch.loggers.csv_logs.CSVLogger property) (lightning.pytorch.loggers.csv_logs.ExperimentWriter parameter), [1] (lightning.pytorch.loggers.CSVLogger property) (lightning.pytorch.loggers.tensorboard.TensorBoardLogger property) (lightning.pytorch.loggers.TensorBoardLogger property) (lightning.pytorch.trainer.trainer.Trainer property) log_every_n_steps (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] log_graph (lightning.pytorch.loggers.tensorboard.TensorBoardLogger parameter), [1] (lightning.pytorch.loggers.TensorBoardLogger parameter), [1] log_graph() (lightning.pytorch.loggers.comet.CometLogger method) (lightning.pytorch.loggers.CometLogger method) (lightning.pytorch.loggers.tensorboard.TensorBoardLogger method) (lightning.pytorch.loggers.TensorBoardLogger method) log_hparams() (lightning.pytorch.loggers.csv_logs.ExperimentWriter method) log_hyperparams() (lightning.pytorch.loggers.comet.CometLogger method) (lightning.pytorch.loggers.CometLogger method) (lightning.pytorch.loggers.csv_logs.CSVLogger method) (lightning.pytorch.loggers.CSVLogger method) (lightning.pytorch.loggers.logger.DummyLogger method) (lightning.pytorch.loggers.mlflow.MLFlowLogger method) (lightning.pytorch.loggers.MLFlowLogger method) (lightning.pytorch.loggers.neptune.NeptuneLogger method) (lightning.pytorch.loggers.NeptuneLogger method) (lightning.pytorch.loggers.tensorboard.TensorBoardLogger method) (lightning.pytorch.loggers.TensorBoardLogger method) (lightning.pytorch.loggers.wandb.WandbLogger method) (lightning.pytorch.loggers.WandbLogger method) log_image() (lightning.pytorch.loggers.wandb.WandbLogger method) (lightning.pytorch.loggers.WandbLogger method) log_metrics() (lightning.pytorch.loggers.comet.CometLogger method) (lightning.pytorch.loggers.CometLogger method) (lightning.pytorch.loggers.logger.DummyLogger method) (lightning.pytorch.loggers.mlflow.MLFlowLogger method) (lightning.pytorch.loggers.MLFlowLogger method) (lightning.pytorch.loggers.neptune.NeptuneLogger method) (lightning.pytorch.loggers.NeptuneLogger method) (lightning.pytorch.loggers.wandb.WandbLogger method) (lightning.pytorch.loggers.WandbLogger method) log_model (lightning.pytorch.loggers.mlflow.MLFlowLogger parameter), [1] (lightning.pytorch.loggers.MLFlowLogger parameter), [1] (lightning.pytorch.loggers.wandb.WandbLogger parameter), [1] (lightning.pytorch.loggers.WandbLogger parameter), [1] log_model_checkpoints (lightning.pytorch.loggers.neptune.NeptuneLogger parameter), [1] (lightning.pytorch.loggers.NeptuneLogger parameter), [1] log_momentum (lightning.pytorch.callbacks.LearningRateMonitor parameter), [1], [2] log_rank_zero_only (lightning.pytorch.callbacks.EarlyStopping parameter), [1], [2] log_table() (lightning.pytorch.loggers.wandb.WandbLogger method) (lightning.pytorch.loggers.WandbLogger method) log_text() (lightning.pytorch.loggers.wandb.WandbLogger method) (lightning.pytorch.loggers.WandbLogger method) logged_metrics (lightning.pytorch.trainer.trainer.Trainer property) Logger (class in lightning.pytorch.loggers.logger) logger (lightning.pytorch.core.LightningModule property) (lightning.pytorch.core.LightningModule.log parameter) (lightning.pytorch.core.LightningModule.log_dict parameter) (lightning.pytorch.core.mixins.HyperparametersMixin.save_hyperparameters parameter) (lightning.pytorch.core.module.LightningModule.log parameter) (lightning.pytorch.core.module.LightningModule.log_dict parameter) (lightning.pytorch.core.module.LightningModule.save_hyperparameters parameter) (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] (lightning.pytorch.trainer.trainer.Trainer property) loggers (lightning.pytorch.core.LightningModule property) (lightning.pytorch.trainer.trainer.Trainer property) logging_batch_size_per_gpu (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] logging_interval (lightning.pytorch.callbacks.LearningRateMonitor parameter), [1], [2] logging_level (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] loss (lightning.pytorch.core.hooks.ModelHooks.on_before_backward parameter) (lightning.pytorch.core.LightningModule.backward parameter) (lightning.pytorch.core.LightningModule.manual_backward parameter) (lightning.pytorch.core.module.LightningModule.backward parameter) (lightning.pytorch.core.module.LightningModule.manual_backward parameter) (lightning.pytorch.core.module.LightningModule.on_before_backward parameter) loss_scale (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] loss_scale_window (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] lr (lightning.pytorch.callbacks.BaseFinetuning.unfreeze_and_add_param_group parameter) lr_find() (lightning.pytorch.tuner.tuning.Tuner method) lr_scheduler (lightning.pytorch.cli.LightningCLI.configure_optimizers parameter) lr_scheduler_class (lightning.pytorch.cli.LightningArgumentParser.add_lr_scheduler_args parameter) lr_scheduler_step() (lightning.pytorch.core.LightningModule method) lr_schedulers() (lightning.pytorch.core.LightningModule method) LSFEnvironment (class in lightning.pytorch.plugins.environments)

M

main_address (lightning.pytorch.plugins.environments.ClusterEnvironment property) (lightning.pytorch.plugins.environments.KubeflowEnvironment property) (lightning.pytorch.plugins.environments.LightningEnvironment property) (lightning.pytorch.plugins.environments.LSFEnvironment property) (lightning.pytorch.plugins.environments.MPIEnvironment property) (lightning.pytorch.plugins.environments.SLURMEnvironment property) (lightning.pytorch.plugins.environments.TorchElasticEnvironment property) (lightning.pytorch.plugins.environments.XLAEnvironment property) main_port (lightning.pytorch.plugins.environments.ClusterEnvironment property) (lightning.pytorch.plugins.environments.KubeflowEnvironment property) (lightning.pytorch.plugins.environments.LightningEnvironment property) (lightning.pytorch.plugins.environments.LSFEnvironment property) (lightning.pytorch.plugins.environments.MPIEnvironment property) (lightning.pytorch.plugins.environments.SLURMEnvironment property) (lightning.pytorch.plugins.environments.TorchElasticEnvironment property) (lightning.pytorch.plugins.environments.XLAEnvironment property) make_pruning_permanent (lightning.pytorch.callbacks.ModelPruning parameter), [1], [2] make_pruning_permanent() (lightning.pytorch.callbacks.ModelPruning method) make_trainable() (lightning.pytorch.callbacks.BaseFinetuning static method) manual_backward() (lightning.pytorch.core.LightningModule method) map_location (lightning.pytorch.core.LightningDataModule.load_from_checkpoint parameter) (lightning.pytorch.core.LightningModule.load_from_checkpoint parameter) (lightning.pytorch.core.module.LightningModule.load_from_checkpoint parameter) (lightning.pytorch.plugins.io.CheckpointIO.load_checkpoint parameter) (lightning.pytorch.plugins.io.TorchCheckpointIO.load_checkpoint parameter) max_depth (lightning.pytorch.callbacks.ModelSummary parameter), [1], [2] (lightning.pytorch.callbacks.RichModelSummary parameter), [1], [2] max_epochs (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] max_in_cpu (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] max_lr (lightning.pytorch.callbacks.LearningRateFinder parameter), [1], [2] (lightning.pytorch.tuner.tuning.Tuner.lr_find parameter) max_steps (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] max_time (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] max_trials (lightning.pytorch.callbacks.BatchSizeFinder parameter), [1], [2] (lightning.pytorch.tuner.tuning.Tuner.scale_batch_size parameter) merge_dicts() (in module lightning.pytorch.loggers.logger) method (lightning.pytorch.core.LightningModule.to_torchscript parameter) (lightning.pytorch.core.module.LightningModule.to_torchscript parameter) (lightning.pytorch.tuner.tuning.Tuner.lr_find parameter) (lightning.pytorch.tuner.tuning.Tuner.scale_batch_size parameter) metric (lightning.pytorch.core.LightningModule.lr_scheduler_step parameter) metric_attribute (lightning.pytorch.core.LightningModule.log parameter) (lightning.pytorch.core.module.LightningModule.log parameter) metrics (lightning.pytorch.loggers.comet.CometLogger.log_metrics parameter) (lightning.pytorch.loggers.CometLogger.log_metrics parameter) (lightning.pytorch.loggers.logger.DummyLogger.log_metrics parameter) (lightning.pytorch.loggers.mlflow.MLFlowLogger.log_metrics parameter) (lightning.pytorch.loggers.MLFlowLogger.log_metrics parameter) (lightning.pytorch.loggers.neptune.NeptuneLogger.log_metrics parameter) (lightning.pytorch.loggers.NeptuneLogger.log_metrics parameter) (lightning.pytorch.loggers.tensorboard.TensorBoardLogger.log_hyperparams parameter) (lightning.pytorch.loggers.TensorBoardLogger.log_hyperparams parameter) (lightning.pytorch.loggers.wandb.WandbLogger.log_metrics parameter) (lightning.pytorch.loggers.WandbLogger.log_metrics parameter) min_delta (lightning.pytorch.callbacks.EarlyStopping parameter), [1], [2] min_epochs (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] min_loss_scale (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] min_lr (lightning.pytorch.callbacks.LearningRateFinder parameter), [1], [2] (lightning.pytorch.tuner.tuning.Tuner.lr_find parameter) min_steps (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] mixed_precision (lightning.pytorch.strategies.FSDPStrategy parameter), [1] MixedPrecision (class in lightning.pytorch.plugins.precision) MLFlowLogger (class in lightning.pytorch.loggers) (class in lightning.pytorch.loggers.mlflow) mode (lightning.pytorch.callbacks.BatchSizeFinder parameter), [1], [2] (lightning.pytorch.callbacks.EarlyStopping parameter), [1], [2] (lightning.pytorch.callbacks.LearningRateFinder parameter), [1], [2] (lightning.pytorch.callbacks.ModelCheckpoint parameter), [1], [2] (lightning.pytorch.plugins.precision.BitsandbytesPrecision parameter), [1], [2] (lightning.pytorch.tuner.tuning.Tuner.lr_find parameter) (lightning.pytorch.tuner.tuning.Tuner.scale_batch_size parameter) (lightning.pytorch.utilities.combined_loader.CombinedLoader parameter), [1]

model (lightning.pytorch.loggers.comet.CometLogger.log_graph parameter) (lightning.pytorch.loggers.CometLogger.log_graph parameter) (lightning.pytorch.loggers.tensorboard.TensorBoardLogger.log_graph parameter) (lightning.pytorch.loggers.TensorBoardLogger.log_graph parameter) (lightning.pytorch.plugins.precision.DeepSpeedPrecision.backward parameter) (lightning.pytorch.plugins.precision.Precision.backward parameter) (lightning.pytorch.plugins.TorchSyncBatchNorm.apply parameter) (lightning.pytorch.plugins.TorchSyncBatchNorm.revert parameter) (lightning.pytorch.strategies.DDPStrategy.optimizer_step parameter) (lightning.pytorch.strategies.Strategy property) (lightning.pytorch.strategies.Strategy.optimizer_step parameter) (lightning.pytorch.trainer.Trainer.fit parameter) (lightning.pytorch.trainer.Trainer.predict parameter) (lightning.pytorch.trainer.Trainer.test parameter), [1] (lightning.pytorch.trainer.trainer.Trainer property) (lightning.pytorch.trainer.trainer.Trainer.fit parameter) (lightning.pytorch.trainer.trainer.Trainer.predict parameter) (lightning.pytorch.trainer.trainer.Trainer.test parameter) (lightning.pytorch.trainer.trainer.Trainer.validate parameter) (lightning.pytorch.trainer.Trainer.validate parameter), [1] (lightning.pytorch.tuner.tuning.Tuner.lr_find parameter) (lightning.pytorch.tuner.tuning.Tuner.scale_batch_size parameter) model_class (lightning.pytorch.cli.LightningCLI parameter), [1] model_sharded_context() (lightning.pytorch.strategies.DeepSpeedStrategy method) (lightning.pytorch.strategies.FSDPStrategy method) (lightning.pytorch.strategies.Strategy method) model_to_device() (lightning.pytorch.strategies.DDPStrategy method) (lightning.pytorch.strategies.FSDPStrategy method) (lightning.pytorch.strategies.SingleDeviceStrategy method) (lightning.pytorch.strategies.Strategy method) (lightning.pytorch.strategies.XLAStrategy method) ModelCheckpoint (class in lightning.pytorch.callbacks) ModelHooks (class in lightning.pytorch.core.hooks) ModelPruning (class in lightning.pytorch.callbacks) ModelSummary (class in lightning.pytorch.callbacks) module lightning.pytorch.loggers.comet lightning.pytorch.loggers.csv_logs lightning.pytorch.loggers.logger lightning.pytorch.loggers.mlflow lightning.pytorch.loggers.neptune lightning.pytorch.loggers.tensorboard lightning.pytorch.loggers.wandb lightning.pytorch.utilities.combined_loader lightning.pytorch.utilities.data lightning.pytorch.utilities.deepspeed lightning.pytorch.utilities.memory lightning.pytorch.utilities.model_summary lightning.pytorch.utilities.parsing lightning.pytorch.utilities.rank_zero lightning.pytorch.utilities.seed lightning.pytorch.utilities.warnings module (lightning.pytorch.callbacks.BaseFinetuning.freeze_module parameter) (lightning.pytorch.plugins.precision.FSDPPrecision.pre_backward parameter) (lightning.pytorch.plugins.precision.MixedPrecision.pre_backward parameter) (lightning.pytorch.plugins.precision.Precision.post_backward parameter) (lightning.pytorch.plugins.precision.Precision.pre_backward parameter) module_init_context() (lightning.pytorch.plugins.precision.DeepSpeedPrecision method) (lightning.pytorch.plugins.precision.DoublePrecision method) (lightning.pytorch.plugins.precision.FSDPPrecision method) (lightning.pytorch.plugins.precision.HalfPrecision method) modules (lightning.pytorch.callbacks.BaseFinetuning.filter_params parameter) (lightning.pytorch.callbacks.BaseFinetuning.flatten_modules parameter) (lightning.pytorch.callbacks.BaseFinetuning.freeze parameter) (lightning.pytorch.callbacks.BaseFinetuning.make_trainable parameter) (lightning.pytorch.callbacks.BaseFinetuning.unfreeze_and_add_param_group parameter) monitor (lightning.pytorch.callbacks.EarlyStopping parameter), [1], [2] (lightning.pytorch.callbacks.ModelCheckpoint parameter), [1], [2] MPIEnvironment (class in lightning.pytorch.plugins.environments) multifile (lightning.pytorch.cli.SaveConfigCallback parameter), [1]

N

name (lightning.pytorch.core.LightningModule.log parameter) (lightning.pytorch.core.module.LightningModule.log parameter) (lightning.pytorch.loggers.comet.CometLogger property) (lightning.pytorch.loggers.CometLogger property) (lightning.pytorch.loggers.csv_logs.CSVLogger parameter), [1] (lightning.pytorch.loggers.CSVLogger parameter), [1] (lightning.pytorch.loggers.logger.DummyLogger property) (lightning.pytorch.loggers.mlflow.MLFlowLogger property) (lightning.pytorch.loggers.MLFlowLogger property) (lightning.pytorch.loggers.neptune.NeptuneLogger parameter), [1] (lightning.pytorch.loggers.neptune.NeptuneLogger property) (lightning.pytorch.loggers.NeptuneLogger parameter), [1] (lightning.pytorch.loggers.NeptuneLogger property) (lightning.pytorch.loggers.tensorboard.TensorBoardLogger parameter), [1] (lightning.pytorch.loggers.TensorBoardLogger parameter), [1] (lightning.pytorch.loggers.wandb.WandbLogger parameter), [1] (lightning.pytorch.loggers.wandb.WandbLogger property) (lightning.pytorch.loggers.WandbLogger parameter), [1] (lightning.pytorch.loggers.WandbLogger property) (lightning.pytorch.strategies.DDPStrategy.barrier parameter) (lightning.pytorch.strategies.FSDPStrategy.barrier parameter) (lightning.pytorch.strategies.SingleDeviceStrategy.barrier parameter) (lightning.pytorch.strategies.Strategy.barrier parameter) (lightning.pytorch.strategies.XLAStrategy.barrier parameter) NeptuneLogger (class in lightning.pytorch.loggers) (class in lightning.pytorch.loggers.neptune)

nested_key (lightning.pytorch.cli.LightningArgumentParser.add_lightning_class_args parameter) (lightning.pytorch.cli.LightningArgumentParser.add_lr_scheduler_args parameter) (lightning.pytorch.cli.LightningArgumentParser.add_optimizer_args parameter) node_rank() (lightning.pytorch.plugins.environments.ClusterEnvironment method) (lightning.pytorch.plugins.environments.KubeflowEnvironment method) (lightning.pytorch.plugins.environments.LightningEnvironment method) (lightning.pytorch.plugins.environments.LSFEnvironment method) (lightning.pytorch.plugins.environments.MPIEnvironment method) (lightning.pytorch.plugins.environments.SLURMEnvironment method) (lightning.pytorch.plugins.environments.TorchElasticEnvironment method) (lightning.pytorch.plugins.environments.XLAEnvironment method) num_devices (lightning.pytorch.trainer.trainer.Trainer property) num_nodes (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] num_predict_batches (lightning.pytorch.trainer.trainer.Trainer property) num_sanity_val_batches (lightning.pytorch.trainer.trainer.Trainer property) num_sanity_val_steps (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] num_test_batches (lightning.pytorch.trainer.trainer.Trainer property) num_training (lightning.pytorch.tuner.tuning.Tuner.lr_find parameter) num_training_batches (lightning.pytorch.trainer.trainer.Trainer property) num_training_steps (lightning.pytorch.callbacks.LearningRateFinder parameter), [1], [2] num_val_batches (lightning.pytorch.trainer.trainer.Trainer property) num_workers (lightning.pytorch.core.LightningDataModule.from_datasets parameter) nvme_path (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1]

O

obj (lightning.pytorch.strategies.DDPStrategy.broadcast parameter) (lightning.pytorch.strategies.FSDPStrategy.broadcast parameter) (lightning.pytorch.strategies.SingleDeviceStrategy.broadcast parameter) (lightning.pytorch.strategies.Strategy.broadcast parameter) (lightning.pytorch.strategies.XLAStrategy.broadcast parameter) offline (lightning.pytorch.loggers.comet.CometLogger parameter), [1] (lightning.pytorch.loggers.CometLogger parameter), [1] (lightning.pytorch.loggers.wandb.WandbLogger parameter), [1] (lightning.pytorch.loggers.WandbLogger parameter), [1] offload_optimizer (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] offload_optimizer_device (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] offload_parameters (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] offload_params_device (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] on_after_backward() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.core.hooks.ModelHooks method) on_after_batch_transfer() (lightning.pytorch.core.hooks.DataHooks method) on_before_backward() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.core.hooks.ModelHooks method) on_before_batch_transfer() (lightning.pytorch.core.hooks.DataHooks method) on_before_optimizer_step() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.core.hooks.ModelHooks method) on_before_zero_grad() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.core.hooks.ModelHooks method) on_epoch (lightning.pytorch.core.LightningModule.log parameter) (lightning.pytorch.core.LightningModule.log_dict parameter) (lightning.pytorch.core.module.LightningModule.log parameter) (lightning.pytorch.core.module.LightningModule.log_dict parameter) on_exception() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.OnExceptionCheckpoint method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.strategies.DDPStrategy method) (lightning.pytorch.strategies.Strategy method) on_fit_end() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.core.hooks.ModelHooks method) on_fit_start() (lightning.pytorch.callbacks.BackboneFinetuning method) (lightning.pytorch.callbacks.BaseFinetuning method) (lightning.pytorch.callbacks.BatchSizeFinder method) (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.LearningRateFinder method) (lightning.pytorch.callbacks.ModelSummary method) (lightning.pytorch.callbacks.StochasticWeightAveraging method) (lightning.pytorch.callbacks.Timer method) (lightning.pytorch.core.hooks.ModelHooks method) on_gpu (lightning.pytorch.core.LightningModule property) on_load_checkpoint() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.core.hooks.CheckpointHooks method) on_predict_batch_end() (lightning.pytorch.callbacks.BasePredictionWriter method) (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) on_predict_batch_start() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) on_predict_end() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) (lightning.pytorch.strategies.Strategy method) on_predict_epoch_end() (lightning.pytorch.callbacks.BasePredictionWriter method) (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.core.hooks.ModelHooks method) on_predict_epoch_start() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.core.hooks.ModelHooks method) on_predict_model_eval() (lightning.pytorch.core.hooks.ModelHooks method) on_predict_start() (lightning.pytorch.callbacks.BatchSizeFinder method) (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) (lightning.pytorch.strategies.Strategy method) on_sanity_check_end() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.TQDMProgressBar method) on_sanity_check_start() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.TQDMProgressBar method) on_save_checkpoint() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.ModelPruning method) (lightning.pytorch.core.hooks.CheckpointHooks method) on_step (lightning.pytorch.core.LightningModule.log parameter) (lightning.pytorch.core.LightningModule.log_dict parameter) (lightning.pytorch.core.module.LightningModule.log parameter) (lightning.pytorch.core.module.LightningModule.log_dict parameter) on_test_batch_end() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.DeviceStatsMonitor method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) on_test_batch_start() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.DeviceStatsMonitor method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) on_test_end() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.Timer method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) (lightning.pytorch.strategies.Strategy method) on_test_epoch_end() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.core.hooks.ModelHooks method) on_test_epoch_start() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.core.hooks.ModelHooks method) on_test_model_eval() (lightning.pytorch.core.hooks.ModelHooks method) on_test_model_train() (lightning.pytorch.core.hooks.ModelHooks method) on_test_start() (lightning.pytorch.callbacks.BatchSizeFinder method) (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.Timer method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) (lightning.pytorch.strategies.Strategy method) on_train_batch_end() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.DeviceStatsMonitor method) (lightning.pytorch.callbacks.ModelCheckpoint method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.Timer method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) on_train_batch_start() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.DeviceStatsMonitor method) (lightning.pytorch.callbacks.LearningRateMonitor method) (lightning.pytorch.core.hooks.ModelHooks method) (lightning.pytorch.strategies.Strategy method) (lightning.pytorch.strategies.XLAStrategy method)

on_train_end() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.ModelPruning method) (lightning.pytorch.callbacks.StochasticWeightAveraging method) (lightning.pytorch.callbacks.Timer method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) (lightning.pytorch.strategies.Strategy method) on_train_epoch_end() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.EarlyStopping method) (lightning.pytorch.callbacks.ModelCheckpoint method) (lightning.pytorch.callbacks.ModelPruning method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.StochasticWeightAveraging method) (lightning.pytorch.callbacks.Timer method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) on_train_epoch_start() (lightning.pytorch.callbacks.BaseFinetuning method) (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.GradientAccumulationScheduler method) (lightning.pytorch.callbacks.LearningRateMonitor method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.StochasticWeightAveraging method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) on_train_start() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.GradientAccumulationScheduler method) (lightning.pytorch.callbacks.LearningRateMonitor method) (lightning.pytorch.callbacks.ModelCheckpoint method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.Timer method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) (lightning.pytorch.strategies.Strategy method) on_validation_batch_end() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.DeviceStatsMonitor method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) on_validation_batch_start() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.DeviceStatsMonitor method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) on_validation_end() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.EarlyStopping method) (lightning.pytorch.callbacks.ModelCheckpoint method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.Timer method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) (lightning.pytorch.strategies.Strategy method) on_validation_epoch_end() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.ModelPruning method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) on_validation_epoch_start() (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.core.hooks.ModelHooks method) on_validation_model_eval() (lightning.pytorch.core.hooks.ModelHooks method) on_validation_model_train() (lightning.pytorch.core.hooks.ModelHooks method) on_validation_model_zero_grad() (lightning.pytorch.core.hooks.ModelHooks method) on_validation_start() (lightning.pytorch.callbacks.BatchSizeFinder method) (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.callbacks.Timer method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.hooks.ModelHooks method) (lightning.pytorch.strategies.Strategy method) OnExceptionCheckpoint (class in lightning.pytorch.callbacks) optimizer (lightning.pytorch.callbacks.BaseFinetuning.filter_on_optimizer parameter) (lightning.pytorch.callbacks.BaseFinetuning.unfreeze_and_add_param_group parameter) (lightning.pytorch.cli.LightningCLI.configure_optimizers parameter) (lightning.pytorch.core.hooks.ModelHooks.on_before_optimizer_step parameter) (lightning.pytorch.core.hooks.ModelHooks.on_before_zero_grad parameter) (lightning.pytorch.core.LightningModule.clip_gradients parameter) (lightning.pytorch.core.LightningModule.configure_gradient_clipping parameter) (lightning.pytorch.core.LightningModule.optimizer_step parameter) (lightning.pytorch.core.LightningModule.optimizer_zero_grad parameter) (lightning.pytorch.core.LightningModule.toggle_optimizer parameter) (lightning.pytorch.core.LightningModule.untoggle_optimizer parameter) (lightning.pytorch.core.module.LightningModule.configure_gradient_clipping parameter) (lightning.pytorch.core.module.LightningModule.on_before_optimizer_step parameter) (lightning.pytorch.core.module.LightningModule.on_before_zero_grad parameter) (lightning.pytorch.core.module.LightningModule.optimizer_step parameter) (lightning.pytorch.core.module.LightningModule.optimizer_zero_grad parameter) (lightning.pytorch.core.module.LightningModule.toggle_optimizer parameter) (lightning.pytorch.core.module.LightningModule.untoggle_optimizer parameter) (lightning.pytorch.plugins.precision.DeepSpeedPrecision.backward parameter) (lightning.pytorch.plugins.precision.Precision.backward parameter) (lightning.pytorch.strategies.DDPStrategy.optimizer_step parameter) (lightning.pytorch.strategies.Strategy.backward parameter) (lightning.pytorch.strategies.Strategy.optimizer_step parameter) optimizer_buffer_count (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] optimizer_class (lightning.pytorch.cli.LightningArgumentParser.add_optimizer_args parameter) optimizer_closure (lightning.pytorch.core.LightningModule.optimizer_step parameter) (lightning.pytorch.core.module.LightningModule.optimizer_step parameter) optimizer_state() (lightning.pytorch.strategies.FSDPStrategy method) (lightning.pytorch.strategies.Strategy method) optimizer_step() (lightning.pytorch.core.LightningModule method) (lightning.pytorch.plugins.precision.DeepSpeedPrecision method) (lightning.pytorch.plugins.precision.FSDPPrecision method) (lightning.pytorch.plugins.precision.MixedPrecision method) (lightning.pytorch.plugins.precision.Precision method) (lightning.pytorch.plugins.precision.XLAPrecision method) (lightning.pytorch.strategies.DDPStrategy method) (lightning.pytorch.strategies.Strategy method) optimizer_zero_grad() (lightning.pytorch.core.LightningModule method) optimizers() (lightning.pytorch.core.LightningModule method) output_file (lightning.pytorch.utilities.deepspeed.convert_zero_checkpoint_to_fp32_state_dict parameter), [1] outputs (lightning.pytorch.core.hooks.ModelHooks.on_predict_batch_end parameter) (lightning.pytorch.core.hooks.ModelHooks.on_test_batch_end parameter) (lightning.pytorch.core.hooks.ModelHooks.on_train_batch_end parameter) (lightning.pytorch.core.hooks.ModelHooks.on_validation_batch_end parameter) (lightning.pytorch.core.module.LightningModule.on_predict_batch_end parameter) (lightning.pytorch.core.module.LightningModule.on_test_batch_end parameter) (lightning.pytorch.core.module.LightningModule.on_train_batch_end parameter) (lightning.pytorch.core.module.LightningModule.on_validation_batch_end parameter) overfit_batches (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] overlap_comm (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] overlap_events (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] overwrite (lightning.pytorch.cli.SaveConfigCallback parameter), [1]

P

ParallelStrategy (class in lightning.pytorch.strategies) parameter_names (lightning.pytorch.callbacks.ModelPruning parameter), [1], [2] parameters_to_prune (lightning.pytorch.callbacks.ModelPruning parameter), [1], [2] params (lightning.pytorch.callbacks.BaseFinetuning.filter_on_optimizer parameter) (lightning.pytorch.loggers.comet.CometLogger.log_hyperparams parameter) (lightning.pytorch.loggers.CometLogger.log_hyperparams parameter) (lightning.pytorch.loggers.csv_logs.CSVLogger.log_hyperparams parameter) (lightning.pytorch.loggers.CSVLogger.log_hyperparams parameter) (lightning.pytorch.loggers.logger.DummyLogger.log_hyperparams parameter) (lightning.pytorch.loggers.mlflow.MLFlowLogger.log_hyperparams parameter) (lightning.pytorch.loggers.MLFlowLogger.log_hyperparams parameter) (lightning.pytorch.loggers.neptune.NeptuneLogger.log_hyperparams parameter) (lightning.pytorch.loggers.NeptuneLogger.log_hyperparams parameter) (lightning.pytorch.loggers.tensorboard.TensorBoardLogger.log_hyperparams parameter) (lightning.pytorch.loggers.TensorBoardLogger.log_hyperparams parameter) (lightning.pytorch.loggers.wandb.WandbLogger.log_hyperparams parameter) (lightning.pytorch.loggers.WandbLogger.log_hyperparams parameter) params_buffer_count (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] params_buffer_size (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] parse_arguments() (lightning.pytorch.cli.LightningCLI method) parse_class_init_keys() (in module lightning.pytorch.utilities.parsing) parse_devices() (lightning.pytorch.accelerators.CPUAccelerator static method) (lightning.pytorch.accelerators.CUDAAccelerator static method) parser (lightning.pytorch.cli.LightningCLI.add_arguments_to_parser parameter) (lightning.pytorch.cli.SaveConfigCallback parameter), [1] parser_kwargs (lightning.pytorch.cli.LightningCLI parameter), [1] partition_activations (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] PassThroughProfiler (class in lightning.pytorch.profilers) path (lightning.pytorch.plugins.io.CheckpointIO.load_checkpoint parameter) (lightning.pytorch.plugins.io.CheckpointIO.remove_checkpoint parameter) (lightning.pytorch.plugins.io.CheckpointIO.save_checkpoint parameter) (lightning.pytorch.plugins.io.TorchCheckpointIO.load_checkpoint parameter) (lightning.pytorch.plugins.io.TorchCheckpointIO.remove_checkpoint parameter) (lightning.pytorch.plugins.io.TorchCheckpointIO.save_checkpoint parameter) (lightning.pytorch.plugins.io.XLACheckpointIO.save_checkpoint parameter) path_args (lightning.pytorch.utilities.parsing.collect_init_args parameter), [1] patience (lightning.pytorch.callbacks.EarlyStopping parameter), [1], [2] pin_memory (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] pl_module (lightning.pytorch.callbacks.Callback.on_load_checkpoint parameter), [1] (lightning.pytorch.callbacks.Callback.on_save_checkpoint parameter), [1] (lightning.pytorch.callbacks.ModelPruning.on_save_checkpoint parameter) plugins (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] port (lightning.pytorch.profilers.XLAProfiler parameter), [1] post_backward() (lightning.pytorch.plugins.precision.Precision method) (lightning.pytorch.strategies.Strategy method) post_training_step() (lightning.pytorch.strategies.Strategy method) pre_backward() (lightning.pytorch.plugins.precision.FSDPPrecision method) (lightning.pytorch.plugins.precision.MixedPrecision method) (lightning.pytorch.plugins.precision.Precision method) (lightning.pytorch.strategies.DDPStrategy method) (lightning.pytorch.strategies.Strategy method) Precision (class in lightning.pytorch.plugins.precision) precision (lightning.pytorch.plugins.precision.DeepSpeedPrecision parameter), [1], [2] (lightning.pytorch.plugins.precision.FSDPPrecision parameter), [1], [2] (lightning.pytorch.plugins.precision.HalfPrecision parameter), [1], [2] (lightning.pytorch.plugins.precision.MixedPrecision parameter), [1], [2] (lightning.pytorch.plugins.precision.XLAPrecision parameter), [1], [2] (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1]

predict() (lightning.pytorch.trainer.trainer.Trainer method) predict_dataloader() (lightning.pytorch.core.hooks.DataHooks method) predict_dataloaders (lightning.pytorch.trainer.trainer.Trainer property) predict_dataset (lightning.pytorch.core.LightningDataModule.from_datasets parameter) predict_step() (lightning.pytorch.core.LightningModule method) (lightning.pytorch.strategies.Strategy method) predict_step_context() (lightning.pytorch.plugins.precision.Precision method) prefix (lightning.pytorch.loggers.comet.CometLogger parameter), [1] (lightning.pytorch.loggers.CometLogger parameter), [1] (lightning.pytorch.loggers.csv_logs.CSVLogger parameter), [1] (lightning.pytorch.loggers.CSVLogger parameter), [1] (lightning.pytorch.loggers.mlflow.MLFlowLogger parameter), [1] (lightning.pytorch.loggers.MLFlowLogger parameter), [1] (lightning.pytorch.loggers.neptune.NeptuneLogger parameter), [1] (lightning.pytorch.loggers.NeptuneLogger parameter), [1] (lightning.pytorch.loggers.tensorboard.TensorBoardLogger parameter), [1] (lightning.pytorch.loggers.TensorBoardLogger parameter), [1] (lightning.pytorch.loggers.wandb.WandbLogger parameter), [1] (lightning.pytorch.loggers.WandbLogger parameter), [1] prepare_data() (lightning.pytorch.core.hooks.DataHooks method) prepare_data_per_node (lightning.pytorch.core.hooks.DataHooks attribute) (lightning.pytorch.core.LightningDataModule attribute) print() (lightning.pytorch.callbacks.ProgressBar method) (lightning.pytorch.callbacks.TQDMProgressBar method) (lightning.pytorch.core.LightningModule method) (lightning.pytorch.trainer.trainer.Trainer method) process_dataloader() (lightning.pytorch.strategies.Strategy method) (lightning.pytorch.strategies.XLAStrategy method) process_position (lightning.pytorch.callbacks.TQDMProgressBar parameter), [1], [2] profile() (lightning.pytorch.profilers.Profiler method) Profiler (class in lightning.pytorch.profilers) profiler (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] prog_bar (lightning.pytorch.core.LightningModule.log parameter) (lightning.pytorch.core.LightningModule.log_dict parameter) (lightning.pytorch.core.module.LightningModule.log parameter) (lightning.pytorch.core.module.LightningModule.log_dict parameter) progress_bar_callback (lightning.pytorch.trainer.trainer.Trainer property) progress_bar_metrics (lightning.pytorch.trainer.trainer.Trainer property) ProgressBar (class in lightning.pytorch.callbacks) project (lightning.pytorch.loggers.neptune.NeptuneLogger parameter), [1] (lightning.pytorch.loggers.NeptuneLogger parameter), [1] (lightning.pytorch.loggers.wandb.WandbLogger parameter), [1] (lightning.pytorch.loggers.WandbLogger parameter), [1] project_name (lightning.pytorch.loggers.comet.CometLogger parameter), [1] (lightning.pytorch.loggers.CometLogger parameter), [1] prune_on_train_epoch_end (lightning.pytorch.callbacks.ModelPruning parameter), [1], [2] pruning_dim (lightning.pytorch.callbacks.ModelPruning parameter), [1], [2] pruning_fn (lightning.pytorch.callbacks.ModelPruning parameter), [1], [2] pruning_norm (lightning.pytorch.callbacks.ModelPruning parameter), [1], [2] PyTorchProfiler (class in lightning.pytorch.profilers)

Q

queue_depth (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1]

R

rank_zero_only (lightning.pytorch.core.LightningModule.log parameter) (lightning.pytorch.core.LightningModule.log_dict parameter) (lightning.pytorch.core.module.LightningModule.log parameter) (lightning.pytorch.core.module.LightningModule.log_dict parameter) received_sigterm (lightning.pytorch.trainer.trainer.Trainer property) recipe (lightning.pytorch.plugins.precision.TransformerEnginePrecision parameter), [1], [2] record_module_names (lightning.pytorch.profilers.PyTorchProfiler parameter), [1] recursive_detach() (in module lightning.pytorch.utilities.memory) reduce() (lightning.pytorch.strategies.DDPStrategy method) (lightning.pytorch.strategies.FSDPStrategy method) (lightning.pytorch.strategies.SingleDeviceStrategy method) (lightning.pytorch.strategies.Strategy method) (lightning.pytorch.strategies.XLAStrategy method) reduce_boolean_decision() (lightning.pytorch.strategies.ParallelStrategy method) (lightning.pytorch.strategies.Strategy method) reduce_bucket_size (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] reduce_fx (lightning.pytorch.core.LightningModule.log parameter) (lightning.pytorch.core.LightningModule.log_dict parameter) (lightning.pytorch.core.module.LightningModule.log parameter) (lightning.pytorch.core.module.LightningModule.log_dict parameter) reduce_op (lightning.pytorch.strategies.DDPStrategy.reduce parameter) (lightning.pytorch.strategies.FSDPStrategy.reduce parameter) (lightning.pytorch.strategies.Strategy.reduce parameter) (lightning.pytorch.strategies.XLAStrategy.reduce parameter) reduce_scatter (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] refresh_rate (lightning.pytorch.callbacks.RichProgressBar parameter), [1], [2] (lightning.pytorch.callbacks.TQDMProgressBar parameter), [1], [2] reload_dataloaders_every_n_epochs (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] remote_device (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] remove_checkpoint() (lightning.pytorch.plugins.io.CheckpointIO method) (lightning.pytorch.plugins.io.TorchCheckpointIO method) (lightning.pytorch.strategies.Strategy method) (lightning.pytorch.strategies.XLAStrategy method) replace_layers (lightning.pytorch.plugins.precision.TransformerEnginePrecision parameter), [1], [2] requeue_signal (lightning.pytorch.plugins.environments.SLURMEnvironment parameter), [1], [2] required (lightning.pytorch.cli.LightningArgumentParser.add_lightning_class_args parameter)

requires_grad (lightning.pytorch.callbacks.BaseFinetuning.filter_params parameter) resample_parameters (lightning.pytorch.callbacks.ModelPruning parameter), [1], [2] reset() (lightning.pytorch.utilities.combined_loader.CombinedLoader method) reset_batch_norm_and_save_state() (lightning.pytorch.callbacks.StochasticWeightAveraging method) reset_momenta() (lightning.pytorch.callbacks.StochasticWeightAveraging method) resolve_root_node_address() (lightning.pytorch.plugins.environments.SLURMEnvironment static method) rest_api_key (lightning.pytorch.loggers.comet.CometLogger parameter), [1] (lightning.pytorch.loggers.CometLogger parameter), [1] restore_checkpoint_after_setup (lightning.pytorch.strategies.DeepSpeedStrategy property) (lightning.pytorch.strategies.FSDPStrategy property) (lightning.pytorch.strategies.Strategy property) return_predictions (lightning.pytorch.trainer.Trainer.predict parameter) (lightning.pytorch.trainer.trainer.Trainer.predict parameter) revert() (lightning.pytorch.plugins.LayerSync method) (lightning.pytorch.plugins.TorchSyncBatchNorm method) RichModelSummary (class in lightning.pytorch.callbacks) RichProgressBar (class in lightning.pytorch.callbacks) root_device (lightning.pytorch.strategies.DDPStrategy property) (lightning.pytorch.strategies.FSDPStrategy property) (lightning.pytorch.strategies.ParallelStrategy property) (lightning.pytorch.strategies.SingleDeviceStrategy property) (lightning.pytorch.strategies.Strategy property) (lightning.pytorch.strategies.XLAStrategy property) root_dir (lightning.pytorch.loggers.csv_logs.CSVLogger property) (lightning.pytorch.loggers.CSVLogger property) (lightning.pytorch.loggers.tensorboard.TensorBoardLogger property) (lightning.pytorch.loggers.TensorBoardLogger property) rounding (lightning.pytorch.callbacks.BackboneFinetuning parameter), [1], [2] row_limit (lightning.pytorch.profilers.PyTorchProfiler parameter), [1] run (lightning.pytorch.cli.LightningCLI parameter), [1] (lightning.pytorch.loggers.neptune.NeptuneLogger parameter), [1] (lightning.pytorch.loggers.NeptuneLogger parameter), [1] run_id (lightning.pytorch.loggers.mlflow.MLFlowLogger parameter), [1] (lightning.pytorch.loggers.mlflow.MLFlowLogger property) (lightning.pytorch.loggers.MLFlowLogger parameter), [1] (lightning.pytorch.loggers.MLFlowLogger property) run_name (lightning.pytorch.loggers.mlflow.MLFlowLogger parameter), [1] (lightning.pytorch.loggers.MLFlowLogger parameter), [1]

S

sampler (lightning.pytorch.utilities.combined_loader.CombinedLoader property) sanitize_parameters_to_prune() (lightning.pytorch.callbacks.ModelPruning static method) sanity_checking (lightning.pytorch.trainer.trainer.Trainer property) save() (lightning.pytorch.loggers.csv_logs.ExperimentWriter method) (lightning.pytorch.loggers.tensorboard.TensorBoardLogger method) (lightning.pytorch.loggers.TensorBoardLogger method) save_checkpoint() (lightning.pytorch.plugins.io.AsyncCheckpointIO method) (lightning.pytorch.plugins.io.CheckpointIO method) (lightning.pytorch.plugins.io.TorchCheckpointIO method) (lightning.pytorch.plugins.io.XLACheckpointIO method) (lightning.pytorch.strategies.DeepSpeedStrategy method) (lightning.pytorch.strategies.FSDPStrategy method) (lightning.pytorch.strategies.Strategy method) (lightning.pytorch.strategies.XLAStrategy method) (lightning.pytorch.trainer.trainer.Trainer method) save_config() (lightning.pytorch.cli.SaveConfigCallback method) save_config_callback (lightning.pytorch.cli.LightningCLI parameter), [1] save_config_kwargs (lightning.pytorch.cli.LightningCLI parameter), [1] save_dir (lightning.pytorch.loggers.comet.CometLogger parameter), [1] (lightning.pytorch.loggers.comet.CometLogger property) (lightning.pytorch.loggers.CometLogger parameter), [1] (lightning.pytorch.loggers.CometLogger property) (lightning.pytorch.loggers.csv_logs.CSVLogger parameter), [1] (lightning.pytorch.loggers.csv_logs.CSVLogger property) (lightning.pytorch.loggers.CSVLogger parameter), [1] (lightning.pytorch.loggers.CSVLogger property) (lightning.pytorch.loggers.logger.Logger property) (lightning.pytorch.loggers.mlflow.MLFlowLogger parameter), [1] (lightning.pytorch.loggers.mlflow.MLFlowLogger property) (lightning.pytorch.loggers.MLFlowLogger parameter), [1] (lightning.pytorch.loggers.MLFlowLogger property) (lightning.pytorch.loggers.neptune.NeptuneLogger property) (lightning.pytorch.loggers.NeptuneLogger property) (lightning.pytorch.loggers.tensorboard.TensorBoardLogger parameter), [1] (lightning.pytorch.loggers.tensorboard.TensorBoardLogger property) (lightning.pytorch.loggers.TensorBoardLogger parameter), [1] (lightning.pytorch.loggers.TensorBoardLogger property) (lightning.pytorch.loggers.wandb.WandbLogger parameter), [1] (lightning.pytorch.loggers.wandb.WandbLogger property) (lightning.pytorch.loggers.wandb.WandbLogger.download_artifact parameter) (lightning.pytorch.loggers.WandbLogger parameter), [1] (lightning.pytorch.loggers.WandbLogger property) (lightning.pytorch.loggers.WandbLogger.download_artifact parameter) save_hyperparameters() (in module lightning.pytorch.utilities.parsing) (lightning.pytorch.core.mixins.HyperparametersMixin method) save_last (lightning.pytorch.callbacks.ModelCheckpoint parameter), [1], [2] save_on_train_epoch_end (lightning.pytorch.callbacks.ModelCheckpoint parameter), [1], [2] save_to_log_dir (lightning.pytorch.cli.SaveConfigCallback parameter), [1] save_top_k (lightning.pytorch.callbacks.ModelCheckpoint parameter), [1], [2] save_weights_only (lightning.pytorch.callbacks.ModelCheckpoint parameter), [1], [2] SaveConfigCallback (class in lightning.pytorch.cli) scale_batch_size() (lightning.pytorch.tuner.tuning.Tuner method) scaler (lightning.pytorch.plugins.precision.FSDPPrecision parameter), [1], [2] (lightning.pytorch.plugins.precision.MixedPrecision parameter), [1], [2] scheduler (lightning.pytorch.core.LightningModule.lr_scheduler_step parameter) scheduling (lightning.pytorch.callbacks.GradientAccumulationScheduler parameter), [1], [2] seed_everything_default (lightning.pytorch.cli.LightningCLI parameter), [1] setup() (lightning.pytorch.accelerators.Accelerator method) (lightning.pytorch.accelerators.CUDAAccelerator method) (lightning.pytorch.callbacks.BaseFinetuning method) (lightning.pytorch.callbacks.BasePredictionWriter method) (lightning.pytorch.callbacks.BatchSizeFinder method) (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.DeviceStatsMonitor method) (lightning.pytorch.callbacks.EarlyStopping method) (lightning.pytorch.callbacks.ModelCheckpoint method) (lightning.pytorch.callbacks.ModelPruning method) (lightning.pytorch.callbacks.ProgressBar method) (lightning.pytorch.callbacks.StochasticWeightAveraging method) (lightning.pytorch.cli.SaveConfigCallback method) (lightning.pytorch.core.hooks.DataHooks method) (lightning.pytorch.profilers.Profiler method) (lightning.pytorch.strategies.DDPStrategy method) (lightning.pytorch.strategies.DeepSpeedStrategy method) (lightning.pytorch.strategies.FSDPStrategy method) (lightning.pytorch.strategies.SingleDeviceStrategy method) (lightning.pytorch.strategies.SingleDeviceXLAStrategy method) (lightning.pytorch.strategies.Strategy method) (lightning.pytorch.strategies.XLAStrategy method) setup_device() (lightning.pytorch.accelerators.CPUAccelerator method) (lightning.pytorch.accelerators.CUDAAccelerator method) setup_environment() (lightning.pytorch.strategies.DDPStrategy method) (lightning.pytorch.strategies.FSDPStrategy method) (lightning.pytorch.strategies.Strategy method) setup_optimizers() (lightning.pytorch.strategies.DeepSpeedStrategy method) (lightning.pytorch.strategies.FSDPStrategy method) (lightning.pytorch.strategies.Strategy method) setup_parser() (lightning.pytorch.cli.LightningCLI method) setup_precision_plugin() (lightning.pytorch.strategies.Strategy method) sharding_strategy (lightning.pytorch.strategies.FSDPStrategy parameter), [1] should_align (lightning.pytorch.callbacks.BackboneFinetuning parameter), [1], [2] SimpleProfiler (class in lightning.pytorch.profilers) single_submit (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] SingleDeviceStrategy (class in lightning.pytorch.strategies) SingleDeviceXLAStrategy (class in lightning.pytorch.strategies) SLURMEnvironment (class in lightning.pytorch.plugins.environments) sort_by_key (lightning.pytorch.profilers.PyTorchProfiler parameter), [1] src (lightning.pytorch.strategies.DDPStrategy.broadcast parameter) (lightning.pytorch.strategies.FSDPStrategy.broadcast parameter) (lightning.pytorch.strategies.SingleDeviceStrategy.broadcast parameter) (lightning.pytorch.strategies.Strategy.broadcast parameter) (lightning.pytorch.strategies.XLAStrategy.broadcast parameter) stage (lightning.pytorch.core.datamodule.LightningDataModule.teardown parameter) (lightning.pytorch.core.hooks.DataHooks.setup parameter) (lightning.pytorch.core.hooks.DataHooks.teardown parameter) (lightning.pytorch.core.module.LightningModule.setup parameter) (lightning.pytorch.core.module.LightningModule.teardown parameter) (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1]

start() (lightning.pytorch.profilers.AdvancedProfiler method) (lightning.pytorch.profilers.PassThroughProfiler method) (lightning.pytorch.profilers.Profiler method) (lightning.pytorch.profilers.PyTorchProfiler method) (lightning.pytorch.profilers.SimpleProfiler method) (lightning.pytorch.profilers.XLAProfiler method) start_time() (lightning.pytorch.callbacks.Timer method) state_dict (lightning.pytorch.callbacks.BackboneFinetuning.load_state_dict parameter) (lightning.pytorch.callbacks.BaseFinetuning.load_state_dict parameter) (lightning.pytorch.callbacks.Callback.load_state_dict parameter), [1] (lightning.pytorch.callbacks.EarlyStopping.load_state_dict parameter) (lightning.pytorch.callbacks.ModelCheckpoint.load_state_dict parameter) (lightning.pytorch.callbacks.StochasticWeightAveraging.load_state_dict parameter) (lightning.pytorch.callbacks.Timer.load_state_dict parameter) (lightning.pytorch.core.datamodule.LightningDataModule.load_state_dict parameter) (lightning.pytorch.core.LightningDataModule.load_state_dict parameter) (lightning.pytorch.plugins.precision.FSDPPrecision.load_state_dict parameter) (lightning.pytorch.plugins.precision.MixedPrecision.load_state_dict parameter) state_dict() (lightning.pytorch.callbacks.BackboneFinetuning method) (lightning.pytorch.callbacks.BaseFinetuning method) (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.EarlyStopping method) (lightning.pytorch.callbacks.ModelCheckpoint method) (lightning.pytorch.callbacks.StochasticWeightAveraging method) (lightning.pytorch.callbacks.Timer method) (lightning.pytorch.core.LightningDataModule method) (lightning.pytorch.plugins.precision.FSDPPrecision method) (lightning.pytorch.plugins.precision.MixedPrecision method) state_dict_type (lightning.pytorch.strategies.FSDPStrategy parameter), [1] state_key (lightning.pytorch.callbacks.Callback property) (lightning.pytorch.callbacks.EarlyStopping property) (lightning.pytorch.callbacks.ModelCheckpoint property) status (lightning.pytorch.loggers.mlflow.MLFlowLogger.finalize parameter) (lightning.pytorch.loggers.MLFlowLogger.finalize parameter) (lightning.pytorch.loggers.neptune.NeptuneLogger.finalize parameter) (lightning.pytorch.loggers.NeptuneLogger.finalize parameter) (lightning.pytorch.loggers.tensorboard.TensorBoardLogger.finalize parameter) (lightning.pytorch.loggers.TensorBoardLogger.finalize parameter) (lightning.pytorch.loggers.wandb.WandbLogger.finalize parameter) (lightning.pytorch.loggers.WandbLogger.finalize parameter) step (lightning.pytorch.loggers.comet.CometLogger.log_metrics parameter) (lightning.pytorch.loggers.CometLogger.log_metrics parameter) (lightning.pytorch.loggers.logger.DummyLogger.log_metrics parameter) (lightning.pytorch.loggers.mlflow.MLFlowLogger.log_metrics parameter) (lightning.pytorch.loggers.MLFlowLogger.log_metrics parameter) (lightning.pytorch.loggers.neptune.NeptuneLogger.log_metrics parameter) (lightning.pytorch.loggers.NeptuneLogger.log_metrics parameter) (lightning.pytorch.loggers.wandb.WandbLogger.log_metrics parameter) (lightning.pytorch.loggers.WandbLogger.log_metrics parameter) step() (lightning.pytorch.core.optimizer.LightningOptimizer method) steps_per_trial (lightning.pytorch.callbacks.BatchSizeFinder parameter), [1], [2] (lightning.pytorch.tuner.tuning.Tuner.scale_batch_size parameter) StochasticWeightAveraging (class in lightning.pytorch.callbacks) stop() (lightning.pytorch.profilers.AdvancedProfiler method) (lightning.pytorch.profilers.PassThroughProfiler method) (lightning.pytorch.profilers.Profiler method) (lightning.pytorch.profilers.PyTorchProfiler method) (lightning.pytorch.profilers.SimpleProfiler method) (lightning.pytorch.profilers.XLAProfiler method) stopping_threshold (lightning.pytorch.callbacks.EarlyStopping parameter), [1], [2] storage_options (lightning.pytorch.plugins.io.CheckpointIO.save_checkpoint parameter) (lightning.pytorch.plugins.io.TorchCheckpointIO.save_checkpoint parameter) (lightning.pytorch.plugins.io.XLACheckpointIO.save_checkpoint parameter) (lightning.pytorch.strategies.DeepSpeedStrategy.save_checkpoint parameter) (lightning.pytorch.strategies.FSDPStrategy.save_checkpoint parameter) (lightning.pytorch.strategies.Strategy.save_checkpoint parameter) (lightning.pytorch.strategies.XLAStrategy.save_checkpoint parameter) (lightning.pytorch.trainer.trainer.Trainer.save_checkpoint parameter) Strategy (class in lightning.pytorch.strategies) strategy (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] strict (lightning.pytorch.callbacks.EarlyStopping parameter), [1], [2] (lightning.pytorch.core.LightningModule.load_from_checkpoint parameter) (lightning.pytorch.core.module.LightningModule.load_from_checkpoint parameter) sub_dir (lightning.pytorch.loggers.tensorboard.TensorBoardLogger parameter), [1] (lightning.pytorch.loggers.TensorBoardLogger parameter), [1] sub_group_size (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1] subclass_mode (lightning.pytorch.cli.LightningArgumentParser.add_lightning_class_args parameter) subclass_mode_data (lightning.pytorch.cli.LightningCLI parameter), [1] subclass_mode_model (lightning.pytorch.cli.LightningCLI parameter), [1] subcommands() (lightning.pytorch.cli.LightningCLI static method) swa_epoch_start (lightning.pytorch.callbacks.StochasticWeightAveraging parameter), [1], [2] swa_lrs (lightning.pytorch.callbacks.StochasticWeightAveraging parameter), [1], [2] sync_batchnorm (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] sync_dist (lightning.pytorch.core.LightningModule.log parameter) (lightning.pytorch.core.LightningModule.log_dict parameter) (lightning.pytorch.core.module.LightningModule.log parameter) (lightning.pytorch.core.module.LightningModule.log_dict parameter) sync_dist_group (lightning.pytorch.core.LightningModule.log parameter) (lightning.pytorch.core.LightningModule.log_dict parameter) (lightning.pytorch.core.module.LightningModule.log parameter) (lightning.pytorch.core.module.LightningModule.log_dict parameter) sync_grads (lightning.pytorch.core.LightningModule.all_gather parameter) (lightning.pytorch.core.module.LightningModule.all_gather parameter) (lightning.pytorch.strategies.Strategy.all_gather parameter) (lightning.pytorch.strategies.XLAStrategy.all_gather parameter) synchronize_checkpoint_boundary (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1]

T

table_kwargs (lightning.pytorch.profilers.PyTorchProfiler parameter), [1] tag (lightning.pytorch.utilities.deepspeed.convert_zero_checkpoint_to_fp32_state_dict parameter), [1] tags (lightning.pytorch.loggers.mlflow.MLFlowLogger parameter), [1] (lightning.pytorch.loggers.MLFlowLogger parameter), [1] teardown() (lightning.pytorch.accelerators.CPUAccelerator method) (lightning.pytorch.accelerators.CUDAAccelerator method) (lightning.pytorch.callbacks.Callback method) (lightning.pytorch.callbacks.OnExceptionCheckpoint method) (lightning.pytorch.callbacks.RichProgressBar method) (lightning.pytorch.core.hooks.DataHooks method) (lightning.pytorch.plugins.environments.ClusterEnvironment method) (lightning.pytorch.plugins.environments.LightningEnvironment method) (lightning.pytorch.plugins.io.AsyncCheckpointIO method) (lightning.pytorch.plugins.io.CheckpointIO method) (lightning.pytorch.plugins.precision.XLAPrecision method) (lightning.pytorch.profilers.AdvancedProfiler method) (lightning.pytorch.profilers.Profiler method) (lightning.pytorch.profilers.PyTorchProfiler method) (lightning.pytorch.strategies.DDPStrategy method) (lightning.pytorch.strategies.FSDPStrategy method) (lightning.pytorch.strategies.ParallelStrategy method) (lightning.pytorch.strategies.SingleDeviceXLAStrategy method) (lightning.pytorch.strategies.Strategy method) (lightning.pytorch.strategies.XLAStrategy method) tensor (lightning.pytorch.plugins.precision.DeepSpeedPrecision.backward parameter) (lightning.pytorch.plugins.precision.FSDPPrecision.pre_backward parameter) (lightning.pytorch.plugins.precision.MixedPrecision.pre_backward parameter) (lightning.pytorch.plugins.precision.Precision.backward parameter) (lightning.pytorch.plugins.precision.Precision.post_backward parameter) (lightning.pytorch.plugins.precision.Precision.pre_backward parameter) (lightning.pytorch.strategies.DDPStrategy.reduce parameter) (lightning.pytorch.strategies.FSDPStrategy.reduce parameter) (lightning.pytorch.strategies.SingleDeviceStrategy.reduce parameter) (lightning.pytorch.strategies.Strategy.all_gather parameter) (lightning.pytorch.strategies.Strategy.reduce parameter) (lightning.pytorch.strategies.XLAStrategy.all_gather parameter) (lightning.pytorch.strategies.XLAStrategy.reduce parameter) tensor_init_context() (lightning.pytorch.plugins.precision.DeepSpeedPrecision method) (lightning.pytorch.plugins.precision.DoublePrecision method) (lightning.pytorch.plugins.precision.FSDPPrecision method) (lightning.pytorch.plugins.precision.HalfPrecision method) (lightning.pytorch.strategies.DeepSpeedStrategy method) (lightning.pytorch.strategies.FSDPStrategy method) (lightning.pytorch.strategies.Strategy method) TensorBoardLogger (class in lightning.pytorch.loggers) (class in lightning.pytorch.loggers.tensorboard) test() (lightning.pytorch.trainer.trainer.Trainer method) test_dataloader() (lightning.pytorch.core.hooks.DataHooks method) test_dataloaders (lightning.pytorch.trainer.trainer.Trainer property) test_dataset (lightning.pytorch.core.LightningDataModule.from_datasets parameter) test_step() (lightning.pytorch.core.LightningModule method) (lightning.pytorch.strategies.Strategy method) test_step_context() (lightning.pytorch.plugins.precision.Precision method) theme (lightning.pytorch.callbacks.RichProgressBar parameter), [1], [2] thread_count (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1]

time_elapsed() (lightning.pytorch.callbacks.Timer method) time_remaining() (lightning.pytorch.callbacks.Timer method) Timer (class in lightning.pytorch.callbacks) to_cpu (lightning.pytorch.utilities.memory.recursive_detach parameter), [1] to_onnx() (lightning.pytorch.core.LightningModule method) to_torchscript() (lightning.pytorch.core.LightningModule method) to_yaml() (lightning.pytorch.callbacks.ModelCheckpoint method) toggle_model() (lightning.pytorch.core.optimizer.LightningOptimizer method) toggle_optimizer() (lightning.pytorch.core.LightningModule method) TorchCheckpointIO (class in lightning.pytorch.plugins.io) TorchElasticEnvironment (class in lightning.pytorch.plugins.environments) TorchSyncBatchNorm (class in lightning.pytorch.plugins) total_predict_batches_current_dataloader (lightning.pytorch.callbacks.ProgressBar property) total_test_batches_current_dataloader (lightning.pytorch.callbacks.ProgressBar property) total_train_batches (lightning.pytorch.callbacks.ProgressBar property) total_val_batches (lightning.pytorch.callbacks.ProgressBar property) total_val_batches_current_dataloader (lightning.pytorch.callbacks.ProgressBar property) TQDMProgressBar (class in lightning.pytorch.callbacks) tracking_uri (lightning.pytorch.loggers.mlflow.MLFlowLogger parameter), [1] (lightning.pytorch.loggers.MLFlowLogger parameter), [1] train_bn (lightning.pytorch.callbacks.BackboneFinetuning parameter), [1], [2] (lightning.pytorch.callbacks.BaseFinetuning.filter_params parameter) (lightning.pytorch.callbacks.BaseFinetuning.freeze parameter) (lightning.pytorch.callbacks.BaseFinetuning.unfreeze_and_add_param_group parameter) train_dataloader (lightning.pytorch.trainer.trainer.Trainer property) train_dataloader() (lightning.pytorch.core.hooks.DataHooks method) train_dataloaders (lightning.pytorch.trainer.Trainer.fit parameter) (lightning.pytorch.trainer.trainer.Trainer.fit parameter) (lightning.pytorch.tuner.tuning.Tuner.lr_find parameter) (lightning.pytorch.tuner.tuning.Tuner.scale_batch_size parameter) train_dataset (lightning.pytorch.core.LightningDataModule.from_datasets parameter) train_step_context() (lightning.pytorch.plugins.precision.Precision method) train_time_interval (lightning.pytorch.callbacks.ModelCheckpoint parameter), [1], [2] Trainer (class in lightning.pytorch.trainer.trainer) trainer (lightning.pytorch.accelerators.Accelerator.setup parameter) (lightning.pytorch.accelerators.CUDAAccelerator.setup parameter) (lightning.pytorch.callbacks.Callback.on_load_checkpoint parameter), [1] (lightning.pytorch.callbacks.Callback.on_save_checkpoint parameter), [1] (lightning.pytorch.callbacks.ModelPruning.on_save_checkpoint parameter) (lightning.pytorch.strategies.DDPStrategy.setup parameter) (lightning.pytorch.strategies.DeepSpeedStrategy.setup parameter) (lightning.pytorch.strategies.DeepSpeedStrategy.setup_optimizers parameter) (lightning.pytorch.strategies.FSDPStrategy.setup parameter) (lightning.pytorch.strategies.FSDPStrategy.setup_optimizers parameter) (lightning.pytorch.strategies.SingleDeviceStrategy.setup parameter) (lightning.pytorch.strategies.SingleDeviceXLAStrategy.setup parameter) (lightning.pytorch.strategies.Strategy.setup parameter) (lightning.pytorch.strategies.Strategy.setup_optimizers parameter) (lightning.pytorch.strategies.XLAStrategy.setup parameter) trainer_class (lightning.pytorch.cli.LightningCLI parameter), [1] trainer_defaults (lightning.pytorch.cli.LightningCLI parameter), [1] training_step() (lightning.pytorch.core.LightningModule method) (lightning.pytorch.strategies.Strategy method) transfer_batch_to_device() (lightning.pytorch.core.hooks.DataHooks method) TransformerEnginePrecision (class in lightning.pytorch.plugins.precision) Tuner (class in lightning.pytorch.tuner.tuning)

U

unfreeze() (lightning.pytorch.core.LightningModule method) unfreeze_and_add_param_group() (lightning.pytorch.callbacks.BaseFinetuning static method) unfreeze_backbone_at_epoch (lightning.pytorch.callbacks.BackboneFinetuning parameter), [1], [2] untoggle_optimizer() (lightning.pytorch.core.LightningModule method) update_attr (lightning.pytorch.callbacks.LearningRateFinder parameter), [1], [2] (lightning.pytorch.tuner.tuning.Tuner.lr_find parameter) update_parameters() (lightning.pytorch.callbacks.StochasticWeightAveraging static method) use_artifact (lightning.pytorch.loggers.wandb.WandbLogger.download_artifact parameter) (lightning.pytorch.loggers.WandbLogger.download_artifact parameter)

use_artifact() (lightning.pytorch.loggers.wandb.WandbLogger method) (lightning.pytorch.loggers.WandbLogger method) use_distributed_sampler (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] use_global_unstructured (lightning.pytorch.callbacks.ModelPruning parameter), [1], [2] use_lottery_ticket_hypothesis (lightning.pytorch.callbacks.ModelPruning parameter), [1], [2] use_pl_optimizer (lightning.pytorch.core.LightningModule.optimizers parameter) (lightning.pytorch.core.module.LightningModule.optimizers parameter)

V

val_check_interval (lightning.pytorch.trainer.Trainer parameter) (lightning.pytorch.trainer.trainer.Trainer parameter), [1] val_dataloader() (lightning.pytorch.core.hooks.DataHooks method) val_dataloaders (lightning.pytorch.trainer.Trainer.fit parameter) (lightning.pytorch.trainer.trainer.Trainer property) (lightning.pytorch.trainer.trainer.Trainer.fit parameter) (lightning.pytorch.tuner.tuning.Tuner.lr_find parameter) (lightning.pytorch.tuner.tuning.Tuner.scale_batch_size parameter) val_dataset (lightning.pytorch.core.LightningDataModule.from_datasets parameter) val_step_context() (lightning.pytorch.plugins.precision.Precision method) validate() (lightning.pytorch.trainer.trainer.Trainer method) validate_settings() (lightning.pytorch.plugins.environments.ClusterEnvironment method) (lightning.pytorch.plugins.environments.SLURMEnvironment method) (lightning.pytorch.plugins.environments.TorchElasticEnvironment method) validation_step() (lightning.pytorch.core.LightningModule method) (lightning.pytorch.strategies.Strategy method) value (lightning.pytorch.core.LightningModule.log parameter) (lightning.pytorch.core.module.LightningModule.log parameter) verbose (lightning.pytorch.callbacks.BackboneFinetuning parameter), [1], [2] (lightning.pytorch.callbacks.EarlyStopping parameter), [1], [2] (lightning.pytorch.callbacks.ModelCheckpoint parameter), [1], [2] (lightning.pytorch.callbacks.ModelPruning parameter), [1], [2] (lightning.pytorch.callbacks.Timer parameter), [1], [2] (lightning.pytorch.trainer.Trainer.test parameter), [1] (lightning.pytorch.trainer.trainer.Trainer.test parameter) (lightning.pytorch.trainer.trainer.Trainer.validate parameter) (lightning.pytorch.trainer.Trainer.validate parameter), [1]

version (lightning.pytorch.loggers.comet.CometLogger property) (lightning.pytorch.loggers.CometLogger property) (lightning.pytorch.loggers.csv_logs.CSVLogger parameter), [1] (lightning.pytorch.loggers.CSVLogger parameter), [1] (lightning.pytorch.loggers.logger.DummyLogger property) (lightning.pytorch.loggers.mlflow.MLFlowLogger property) (lightning.pytorch.loggers.MLFlowLogger property) (lightning.pytorch.loggers.neptune.NeptuneLogger property) (lightning.pytorch.loggers.NeptuneLogger property) (lightning.pytorch.loggers.tensorboard.TensorBoardLogger parameter), [1] (lightning.pytorch.loggers.TensorBoardLogger parameter), [1] (lightning.pytorch.loggers.wandb.WandbLogger parameter), [1] (lightning.pytorch.loggers.wandb.WandbLogger property) (lightning.pytorch.loggers.WandbLogger parameter), [1] (lightning.pytorch.loggers.WandbLogger property)

W

WandbLogger (class in lightning.pytorch.loggers) (class in lightning.pytorch.loggers.wandb) weights_only (lightning.pytorch.trainer.trainer.Trainer.save_checkpoint parameter) world_size() (lightning.pytorch.plugins.environments.ClusterEnvironment method) (lightning.pytorch.plugins.environments.KubeflowEnvironment method) (lightning.pytorch.plugins.environments.LightningEnvironment method) (lightning.pytorch.plugins.environments.LSFEnvironment method) (lightning.pytorch.plugins.environments.MPIEnvironment method) (lightning.pytorch.plugins.environments.SLURMEnvironment method) (lightning.pytorch.plugins.environments.TorchElasticEnvironment method) (lightning.pytorch.plugins.environments.XLAEnvironment method)

write_interval (lightning.pytorch.callbacks.BasePredictionWriter parameter), [1], [2] write_on_batch_end() (lightning.pytorch.callbacks.BasePredictionWriter method) write_on_epoch_end() (lightning.pytorch.callbacks.BasePredictionWriter method)

X

XLAAccelerator (class in lightning.pytorch.accelerators) XLACheckpointIO (class in lightning.pytorch.plugins.io) XLAEnvironment (class in lightning.pytorch.plugins.environments)

XLAPrecision (class in lightning.pytorch.plugins.precision) XLAProfiler (class in lightning.pytorch.profilers) XLAStrategy (class in lightning.pytorch.strategies)

Z

zero_allow_untested_optimizer (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1]

zero_optimization (lightning.pytorch.strategies.DeepSpeedStrategy parameter), [1]