Index

Symbols

**datamodule_kwargs (lightning.pytorch.core.LightningDataModule.from_datasets parameter) **kwargs (lightning.pytorch.callbacks.LambdaCallback parameter), [1], [2] (lightning.pytorch.callbacks.ThroughputMonitor parameter), [1] (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.wandb.WandbLogger.log_audio parameter) (lightning.pytorch.loggers.wandb.WandbLogger.log_video parameter) (lightning.pytorch.loggers.WandbLogger parameter), [1] (lightning.pytorch.loggers.WandbLogger.log_audio parameter) (lightning.pytorch.loggers.WandbLogger.log_video 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.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) audios (lightning.pytorch.loggers.wandb.WandbLogger.log_audio parameter) (lightning.pytorch.loggers.WandbLogger.log_audio parameter) 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.ModelParallelStrategy 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_size_fn (lightning.pytorch.callbacks.ThroughputMonitor parameter), [1] 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.ModelParallelStrategy 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.ModelParallelStrategy.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) data_parallel_size (lightning.pytorch.strategies.ModelParallelStrategy parameter), [1] 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) device_mesh (lightning.pytorch.core.LightningModule property) (lightning.pytorch.strategies.FSDPStrategy parameter), [1] 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) dump_stats (lightning.pytorch.profilers.AdvancedProfiler parameter), [1] 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.ModelParallelStrategy.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.ModelParallelStrategy.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) forward_fn (lightning.pytorch.utilities.measure_flops parameter) 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.ModelParallelStrategy.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

key (lightning.pytorch.loggers.wandb.WandbLogger.log_audio parameter) (lightning.pytorch.loggers.wandb.WandbLogger.log_video parameter) (lightning.pytorch.loggers.WandbLogger.log_audio parameter) (lightning.pytorch.loggers.WandbLogger.log_video parameter) 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.callbacks.TQDMProgressBar parameter), [1], [2] length_fn (lightning.pytorch.callbacks.ThroughputMonitor parameter), [1] 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.ModelParallelStrategy method) (lightning.pytorch.strategies.Strategy method) lightning_restore_optimizer (lightning.pytorch.strategies.DeepSpeedStrategy property) (lightning.pytorch.strategies.FSDPStrategy property) (lightning.pytorch.strategies.ModelParallelStrategy 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_audio() (lightning.pytorch.loggers.wandb.WandbLogger method) (lightning.pytorch.loggers.WandbLogger 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) log_video() (lightning.pytorch.loggers.wandb.WandbLogger method) (lightning.pytorch.loggers.WandbLogger method) log_weight_decay (lightning.pytorch.callbacks.LearningRateMonitor parameter), [1], [2] 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_fn (lightning.pytorch.utilities.measure_flops 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) measure_flops() (in module lightning.pytorch.utilities) 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) (lightning.pytorch.utilities.measure_flops 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.ModelParallelStrategy 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) ModelParallelStrategy (class in lightning.pytorch.strategies) 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.ModelParallelStrategy.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.ModelParallelStrategy.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.core.LightningDataModule 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.ThroughputMonitor 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.ThroughputMonitor 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.ThroughputMonitor 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.ThroughputMonitor 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.SpikeDetection method) (lightning.pytorch.callbacks.ThroughputMonitor 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)

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.ThroughputMonitor 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.ThroughputMonitor 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.ThroughputMonitor 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.ThroughputMonitor 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.ModelParallelStrategy 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.ModelParallelStrategy 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.ModelParallelStrategy.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.ModelParallelStrategy 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.ModelParallelStrategy 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.ModelParallelStrategy 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_distributed_checkpoint (lightning.pytorch.strategies.ModelParallelStrategy parameter), [1] 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.callbacks.ThroughputMonitor 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.ModelParallelStrategy 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.DeepSpeedStrategy method) (lightning.pytorch.strategies.FSDPStrategy method) (lightning.pytorch.strategies.ModelParallelStrategy method) (lightning.pytorch.strategies.Strategy method) (lightning.pytorch.strategies.XLAStrategy method) setup_optimizers() (lightning.pytorch.strategies.DeepSpeedStrategy method) (lightning.pytorch.strategies.FSDPStrategy method) (lightning.pytorch.strategies.ModelParallelStrategy 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] SpikeDetection (class in lightning.pytorch.callbacks) src (lightning.pytorch.strategies.DDPStrategy.broadcast parameter) (lightning.pytorch.strategies.FSDPStrategy.broadcast parameter) (lightning.pytorch.strategies.ModelParallelStrategy.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_audio parameter) (lightning.pytorch.loggers.wandb.WandbLogger.log_metrics parameter) (lightning.pytorch.loggers.wandb.WandbLogger.log_video parameter) (lightning.pytorch.loggers.WandbLogger.log_audio parameter) (lightning.pytorch.loggers.WandbLogger.log_metrics parameter) (lightning.pytorch.loggers.WandbLogger.log_video 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.ModelParallelStrategy.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) strict_loading (lightning.pytorch.core.LightningModule property) 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] synchronous (lightning.pytorch.loggers.mlflow.MLFlowLogger parameter), [1] (lightning.pytorch.loggers.MLFlowLogger 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.ModelParallelStrategy 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.ModelParallelStrategy.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.ModelParallelStrategy method) (lightning.pytorch.strategies.Strategy method) tensor_parallel_size (lightning.pytorch.strategies.ModelParallelStrategy parameter), [1] 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] ThroughputMonitor (class in lightning.pytorch.callbacks) 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.ModelParallelStrategy.setup parameter) (lightning.pytorch.strategies.ModelParallelStrategy.setup_optimizers 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) videos (lightning.pytorch.loggers.wandb.WandbLogger.log_video parameter) (lightning.pytorch.loggers.WandbLogger.log_video parameter)

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]