Upgrade from 1.5 to the 2.0

Regular User

reg. user 1.5

If	Then	Ref
used trainer.fit(train_dataloaders=...)	use trainer.fit(dataloaders=...)	PR7431
used trainer.validate(val_dataloaders...)	use trainer.validate(dataloaders=...)	PR7431
passed num_nodes to DDPPlugin and DDPSpawnPlugin	remove them since these parameters are now passed from the Trainer	PR7026
passed sync_batchnorm to DDPPlugin and DDPSpawnPlugin	remove them since these parameters are now passed from the Trainer	PR7026
didn’t provide a monitor argument to the EarlyStopping callback and just relied on the default value	pass monitor as it is now a required argument	PR7907
used every_n_val_epochs in ModelCheckpoint	change the argument to every_n_epochs	PR8383
used Trainer’s flag reload_dataloaders_every_epoch	use pass reload_dataloaders_every_n_epochs	PR5043
used Trainer’s flag distributed_backend	use strategy	PR8575

reg. user 1.6

If	Then	Ref
used Trainer’s flag terminate_on_nan	set detect_anomaly instead, which enables detecting anomalies in the autograd engine	PR9175
used Trainer’s flag weights_summary	pass a ModelSummary callback with max_depth instead	PR9699
used Trainer’s flag checkpoint_callback	set enable_checkpointing. If you set enable_checkpointing=True, it configures a default ModelCheckpoint callback if none is provided lightning.pytorch.trainer.trainer.Trainer.callbacks.ModelCheckpoint	PR9754
used Trainer’s flag stochastic_weight_avg	add the StochasticWeightAveraging callback directly to the list of callbacks, so for example, Trainer(..., callbacks=[StochasticWeightAveraging(), ...])	PR8989
used Trainer’s flag flush_logs_every_n_steps	pass it to the logger init if it is supported for the particular logger	PR9366
used Trainer’s flag max_steps to the Trainer, max_steps=None won’t have any effect	turn off the limit by passing Trainer(max_steps=-1) which is the default	PR9460
used Trainer’s flag resume_from_checkpoint="..."	pass the same path to the fit function instead, trainer.fit(ckpt_path="...")	PR9693
used Trainer’s flag log_gpu_memory, gpu_metrics	use the DeviceStatsMonitor callback instead	PR9921
used Trainer’s flag progress_bar_refresh_rate	set the ProgressBar callback and set refresh_rate there, or pass enable_progress_bar=False to disable the progress bar	PR9616
called LightningModule.summarize()	use the utility function pl.utilities.model_summary.summarize(model)	PR8513
used the LightningModule.model_size property	use the utility function pl.utilities.memory.get_model_size_mb(model)	PR8495
relied on the on_train_dataloader() hooks in LightningModule and LightningDataModule	use train_dataloader	PR9098
relied on the on_val_dataloader() hooks in LightningModule and LightningDataModule	use val_dataloader	PR9098
relied on the on_test_dataloader() hooks in LightningModule and LightningDataModule	use test_dataloader	PR9098
relied on the on_predict_dataloader() hooks in LightningModule and LightningDataModule	use predict_dataloader	PR9098
implemented the on_keyboard_interrupt callback hook	implement the on_exception hook, and specify the exception type	PR9260
relied on the TestTubeLogger	Use another logger like TensorBoardLogger	PR9065
used the basic progress bar ProgressBar callback	use the TQDMProgressBar callback instead with the same arguments	PR10134
were using GPUStatsMonitor callbacks	use DeviceStatsMonitor callback instead	PR9924
were using XLAStatsMonitor callbacks	use DeviceStatsMonitor callback instead	PR9924

reg. user 1.7

If	Then	Ref
have wrapped your loggers with LoggerCollection	directly pass a list of loggers to the Trainer and access the list via the trainer.loggers attribute.	PR12147
used Trainer.lr_schedulers	access trainer.lr_scheduler_configs instead, which contains dataclasses instead of dictionaries.	PR11443
used neptune-client API in the NeptuneLogger	upgrade to the latest API	PR14727
used LightningDataModule.on_save hook	use LightningDataModule.on_save_checkpoint instead	PR11887
used LightningDataModule.on_load_checkpoint hook	use LightningDataModule.on_load_checkpoint hook instead	PR11887
used LightningModule.on_hpc_load hook	switch to general purpose hook LightningModule.on_load_checkpoint	PR14315
used LightningModule.on_hpc_save hook	switch to general purpose hook LightningModule.on_save_checkpoint	PR14315
used Trainer’s flag weights_save_path	use directly dirpath argument in the ModelCheckpoint callback.	PR14424
used Trainer’s property Trainer.weights_save_path is dropped		PR14424

reg. user 1.8

If	Then	Ref
used seed_everything_default=None in LightningCLI	set seed_everything_default=False instead	PR12804
used Trainer.reset_train_val_dataloaders()	call Trainer.fit_loop.setup_data()	PR12184
imported pl.core.lightning	import pl.core.module instead	PR12740

reg. user 1.9

If	Then	Ref
used Python 3.7	upgrade to Python 3.8 or higher	PR16579
used PyTorch 1.10	upgrade to PyTorch 1.11 or higher	PR16492
used Trainer’s flag gpus	use devices with the same number	PR16171
used Trainer’s flag tpu_cores	use devices with the same number	PR16171
used Trainer’s flag ipus	use devices with the same number	PR16171
used Trainer’s flag num_processes	use devices with the same number	PR16171
used Trainer’s flag resume_from_checkpoint	pass the path to the Trainer.fit(ckpt_path="...") method,	PR10061
used Trainer’s flag auto_select_gpus	use devices="auto"	PR16184
called the pl.tuner.auto_gpu_select.pick_single_gpu function	use Trainer’s flag devices="auto"	PR16184
called the pl.tuner.auto_gpu_select.pick_multiple_gpus functions	use Trainer’s flag devices="auto"	PR16184
used Trainer’s flag accumulate_grad_batches with a scheduling dictionary value	use the GradientAccumulationScheduler callback and configure it	PR16729
imported profiles from pl.profiler	import from pl.profilers	PR16359
used Tuner as part of Trainer in any form	move to a standalone Tuner object or use particular callbacks LearningRateFinder and BatchSizeFinder	Batch Size Finder Learning Rate Finder
used Trainer’s flag auto_scale_batch_size	use BatchSizeFinder callback instead and the Trainer.tune() method was removed
used Trainer’s flag auto_lr_find	use callbacks LearningRateFinder callback instead and the Trainer.tune() method was removed

Advanced User

adv. user 1.5

If	Then	Ref
used self.log(sync_dist_op=...)	use self.log(reduce_fx=...) instead. Passing "mean" will still work, but it also takes a callable	PR7891
used the argument model from pytorch_lightning.utilities.model_helper.is_overridden	use instance instead	PR7918
returned values from training_step that had .grad defined (e.g., a loss) and expected .detach() to be called for you	call .detach() manually	PR7994
imported pl.utilities.distributed.rank_zero_warn	import pl.utilities.rank_zero.rank_zero_warn
relied on DataModule.has_prepared_data attribute	manage data lifecycle in customer methods	PR7657
relied on DataModule.has_setup_fit attribute	manage data lifecycle in customer methods	PR7657
relied on DataModule.has_setup_validate attribute	manage data lifecycle in customer methods	PR7657
relied on DataModule.has_setup_test attribute	manage data lifecycle in customer methods	PR7657
relied on DataModule.has_setup_predict attribute	manage data lifecycle in customer methods	PR7657
relied on DataModule.has_teardown_fit attribute	manage data lifecycle in customer methods	PR7657
relied on DataModule.has_teardown_validate attribute	manage data lifecycle in customer methods	PR7657
relied on DataModule.has_teardown_test attribute	manage data lifecycle in customer methods	PR7657
relied on DataModule.has_teardown_predict attribute	manage data lifecycle in customer methods	PR7657
used DDPPlugin.task_idx	use DDPStrategy.local_rank	PR8203
used Trainer.disable_validation	use the condition not Trainer.enable_validation	PR8291

adv. user 1.6

If	Then	Ref
passed prepare_data_per_node to the Trainer	set it as a property of DataHooks, accessible in the LightningModule and LightningDataModule instead	PR8958
used process_position flag	specify your ProgressBar callback and set it as process_position directly	PR9222
used distributed training attributes add_to_queue and get_from_queue in LightningModule	user the same methods in DDPStrategy(start_method='spawn')	PR9118
called LightningModule.get_progress_bar_dict	use the utility function pl.callbacks.progress.base.get_standard_metrics(module.trainer)	PR9118
used LightningModule.on_post_move_to_device	remove it as parameters tying happens automatically without the need of implementing your own logic	PR9525
relied on Trainer.progress_bar_dict	use ProgressBarBase.get_metrics	PR9118
used LightningDistributed	rely on the logic in DDPStrategy(start_method='...')	PR9691
used the Accelerator collective API Accelerator.barrier, Accelerator.broadcast, and Accelerator.all_gather	call Strategy collectives API directly, without going through Accelerator	PR9677
used pytorch_lightning.core.decorators.parameter_validation	rely on automatic parameters tying with pytorch_lightning.utilities.params_tying.set_shared_parameters	PR9525
used LearningRateMonitor.lr_sch_names	access them using LearningRateMonitor.lrs.keys() which will return the names of all the optimizers, even those without a scheduler.	PR10066
implemented DataModule train_transforms, val_transforms, test_transforms, size, dims	switch to LightningDataModule	PR8851

adv. user 1.7

If	Then	Ref
used DDP2Strategy	switch to DDPStrategy	PR14026
used Trainer.training_type_plugin property	now use Trainer.strategy and update the references	PR11141
used any TrainingTypePluginsn	rename them to Strategy	PR11120
used DistributedType	rely on protected _StrategyType	PR10505
used DeviceType	rely on protected _AcceleratorType	PR10503
used pl.utiltiies.meta functions	switch to built-in https://github.com/pytorch/torchdistx support	PR13868
have implemented Callback.on_configure_sharded_model hook	move your implementation to Callback.setup	PR14834
have implemented the Callback.on_before_accelerator_backend_setup hook	move your implementation to Callback.setup	PR14834
have implemented the Callback.on_batch_start hook	move your implementation to Callback.on_train_batch_start	PR14834
have implemented the Callback.on_batch_end hook	move your implementation to Callback.on_train_batch_end	PR14834
have implemented the Callback.on_epoch_start hook	move your implementation to Callback.on_train_epoch_start , to Callback.on_validation_epoch_start , to Callback.on_test_epoch_start	PR14834
have implemented the Callback.on_pretrain_routine_{start,end} hook	move your implementation to Callback.on_fit_start	PR14834
used Callback.on_init_start hook	use Callback.on_train_start instead	PR10940
used Callback.on_init_end hook	use Callback.on_train_start instead	PR10940
used Trainer’s attribute Trainer.num_processes	it was replaced by Trainer.num_devices	PR12388
used Trainer’s attribute Trainer.gpus	it was replaced by Trainer.num_devices	PR12436
used Trainer’s attribute Trainer.num_gpus	use Trainer.num_devices instead	PR12384
used Trainer’s attribute Trainer.ipus	use Trainer.num_devices instead	PR12386
used Trainer’s attribute Trainer.tpu_cores	use Trainer.num_devices instead	PR12437
used Trainer.num_processes attribute	switch to using Trainer.num_devices	PR12388
used LightningIPUModule	it was removed	PR14830
logged with LightningLoggerBase.agg_and_log_metrics	switch to LightningLoggerBase.log_metrics	PR11832
used agg_key_funcs parameter from LightningLoggerBase	log metrics explicitly	PR11871
used agg_default_func parameters in LightningLoggerBase	log metrics explicitly	PR11871
used Trainer.validated_ckpt_path attribute	rely on generic read-only property Trainer.ckpt_path which is set when checkpoints are loaded via Trainer.validate(ckpt_path=...)	PR11696
used Trainer.tested_ckpt_path attribute	rely on generic read-only property Trainer.ckpt_path which is set when checkpoints are loaded via Trainer.test(ckpt_path=...)	PR11696
used Trainer.predicted_ckpt_path attribute	rely on generic read-only property Trainer.ckpt_path, which is set when checkpoints are loaded via Trainer.predict(ckpt_path=...)	PR11696
rely on the returned dictionary from Callback.on_save_checkpoint	call directly Callback.state_dict instead	PR11887

adv. user 1.8

If	Then	Ref
imported pl.callbacks.base	import pl.callbacks.callback	PR13031
imported pl.loops.base	import pl.loops.loop instead	PR13043
imported pl.utilities.cli	import pl.cli instead	PR13767
imported profiler classes from pl.profiler.*	import pl.profilers instead	PR12308
used pl.accelerators.GPUAccelerator	use pl.accelerators.CUDAAccelerator	PR13636
used LightningDeepSpeedModule	use strategy="deepspeed" or strategy=DeepSpeedStrategy(...)	DeepSpeedStrategy
used the with init_meta_context() context manager from import pl.utilities.meta	switch to deepspeed-zero-stage-3	DeepSpeed ZeRO Stage 3
used the Lightning Hydra multi-run integration	removed support for it as it caused issues with processes hanging	PR15689
used pl.utilities.memory.get_gpu_memory_map	use pl.accelerators.cuda.get_nvidia_gpu_stats	PR9921

adv. user 1.9

If	Then	Ref
used the pl.lite module	switch to lightning_fabric	PR15953
used Trainer’s flag strategy='dp'	use DDP with strategy='ddp' or DeepSpeed instead	PR16748
implemented LightningModule.training_epoch_end hooks	port your logic to LightningModule.on_train_epoch_end hook	PR16520
implemented LightningModule.validation_epoch_end hook	port your logic to LightningModule.on_validation_epoch_end hook	PR16520
implemented LightningModule.test_epoch_end hooks	port your logic to LightningModule.on_test_epoch_end hook	PR16520
used Trainer’s flag multiple_trainloader_mode	switch to CombinedLoader(..., mode=...) and set mode directly now	PR16800
used Trainer’s flag move_metrics_to_cpu	implement particular offload logic in your custom metric or turn it on in torchmetrics	PR16358
used Trainer’s flag track_grad_norm	overwrite on_before_optimizer_step hook and pass the argument directly and LightningModule.log_grad_norm() hook	PR16745 PR16745
used Trainer’s flag replace_sampler_ddp	use use_distributed_sampler; the sampler gets created not only for the DDP strategies
relied on the on_tpu argument in LightningModule.optimizer_step hook	switch to manual optimization	PR16537 Manual Optimization
relied on the using_lbfgs argument in LightningModule.optimizer_step hook	switch to manual optimization	PR16538 Manual Optimization
were using nvidia/apex in any form	switch to PyTorch native mixed precision torch.amp instead	PR16039 Precision
used Trainer’s flag using_native_amp	use PyTorch native mixed precision	PR16039 Precision
used Trainer’s flag amp_backend	use PyTorch native mixed precision	PR16039 Precision
used Trainer’s flag amp_level	use PyTorch native mixed precision	PR16039 Precision
used Trainer’s attribute using_native_amp	use PyTorch native mixed precision	PR16039 Precision
used Trainer’s attribute amp_backend	use PyTorch native mixed precision	PR16039 Precision
used Trainer’s attribute amp_level	use PyTorch native mixed precision	PR16039 Precision
use the FairScale integration	consider using PyTorch’s native FSDP implementation or outsourced implementation into own project	lightning-Fairscale
used pl.overrides.fairscale.LightningShardedDataParallel	use native FSDP instead	PR16400 FSDP
used pl.plugins.precision.fully_sharded_native_amp.FullyShardedNativeMixedPrecisionPlugin	use native FSDP instead	PR16400 FSDP
used pl.plugins.precision.sharded_native_amp.ShardedNativeMixedPrecisionPlugin	use native FSDP instead	PR16400 FSDP
used pl.strategies.fully_sharded.DDPFullyShardedStrategy	use native FSDP instead	PR16400 FSDP
used pl.strategies.sharded.DDPShardedStrategy	use native FSDP instead	PR16400 FSDP
used pl.strategies.sharded_spawn.DDPSpawnShardedStrategy	use native FSDP instead	PR16400 FSDP
used save_config_overwrite parameters in LightningCLI	pass this option and via dictionary of save_config_kwargs parameter	PR14998
used save_config_multifile parameters in LightningCLI	pass this option and via dictionary of save_config_kwargs parameter	PR14998
have customized loops Loop.replace()	implement your training loop with Fabric.	PR14998 Fabric
have customized loops Loop.run()	implement your training loop with Fabric.	PR14998 Fabric
have customized loops Loop.connect()	implement your training loop with Fabric.	PR14998 Fabric
used the Trainer’s trainer.fit_loop property	implement your training loop with Fabric	PR14998 Fabric
used the Trainer’s trainer.validate_loop property	implement your training loop with Fabric	PR14998 Fabric
used the Trainer’s trainer.test_loop property	implement your training loop with Fabric	PR14998 Fabric
used the Trainer’s trainer.predict_loop property	implement your training loop with Fabric	PR14998 Fabric
used the Trainer.loop and fetching classes	being marked as protected
used opt_idx argument in BaseFinetuning.finetune_function	use manual optimization	PR16539
used opt_idx argument in Callback.on_before_optimizer_step	use manual optimization	PR16539 Manual Optimization
used optimizer_idx as an optional argument in LightningModule.training_step	use manual optimization	PR16539 Manual Optimization
used optimizer_idx argument in LightningModule.on_before_optimizer_step	use manual optimization	PR16539 Manual Optimization
used optimizer_idx argument in LightningModule.configure_gradient_clipping	use manual optimization	PR16539 Manual Optimization
used optimizer_idx argument in LightningModule.optimizer_step	use manual optimization	PR16539 Manual Optimization
used optimizer_idx argument in LightningModule.optimizer_zero_grad	use manual optimization	PR16539 Manual Optimization
used optimizer_idx argument in LightningModule.lr_scheduler_step	use manual optimization	PR16539 Manual Optimization
used declaring optimizer frequencies in the dictionary returned from LightningModule.configure_optimizers	use manual optimization	PR16539 Manual Optimization
used optimizer argument in LightningModule.backward	use manual optimization	PR16539 Manual Optimization
used optimizer_idx argument in LightningModule.backward	use manual optimization	PR16539 Manual Optimization
used optimizer_idx argument in PrecisionPlugin.optimizer_step	use manual optimization	PR16539 Manual Optimization
used optimizer_idx argument in PrecisionPlugin.,backward	use manual optimization	PR16539 Manual Optimization
used optimizer_idx argument in PrecisionPlugin.optimizer_step	use manual optimization	PR16539 Manual Optimization
used optimizer_idx argument in Strategy.backward	use manual optimization	PR16539 Manual Optimization
used optimizer_idx argument in Strategy.optimizer_step	use manual optimization	PR16539 Manual Optimization
used Trainer’s Trainer.optimizer_frequencies attribute	use manual optimization	Manual Optimization
used PL_INTER_BATCH_PARALLELISM environment flag		PR16355
used training integration with Horovod	install standalone package/project	lightning-Horovod
used training integration with ColossalAI	install standalone package/project	lightning-ColossalAI
used QuantizationAwareTraining callback	use Torch’s Quantization directly	PR16750
had any logic except reducing the DP outputs in LightningModule.training_step_end hook	port it to LightningModule.on_train_batch_end hook	PR16791
had any logic except reducing the DP outputs in LightningModule.validation_step_end hook	port it to LightningModule.on_validation_batch_end hook	PR16791
had any logic except reducing the DP outputs in LightningModule.test_step_end hook	port it to LightningModule.on_test_batch_end hook	PR16791
used pl.strategies.DDPSpawnStrategy	switch to general DDPStrategy(start_method='spawn') with proper starting method	PR16809
used the automatic addition of a moving average of the training_step loss in the progress bar	use self.log("loss", ..., prog_bar=True) instead.	PR16192
rely on the outputs argument from the on_predict_epoch_end hook	access them via trainer.predict_loop.predictions	PR16655
need to pass a dictionary to self.log()	pass them independently.	PR16389

Developer

devel 1.5

If	Then	Ref
called CheckpointConnector.hpc_load()	just call CheckpointConnector.restore()	PR7652
used TrainerModelHooksMixin	now rely on the corresponding utility functions in pytorch_lightning.utilities.signature_utils	PR7422
assigned the Trainer.train_loop property	now assign the equivalent Trainer.fit_loop property	PR8025
accessed LightningModule.loaded_optimizer_states_dict	the property has been removed	PR8229

devel 1.6

If	Then	Ref
called LightningLoggerBase.close	switch to LightningLoggerBase.finalize.	PR9422
called LoggerCollection.close	switch to LoggerCollection.finalize.	PR9422
used AcceleratorConnector.is_slurm_managing_tasks attribute	it is set not as protected and discouraged from direct use	PR10101
used AcceleratorConnector.configure_slurm_ddp attributes	it is set not as protected and discouraged from direct use	PR10101
used ClusterEnvironment.creates_children() method	change it to ClusterEnvironment.creates_processes_externally which is property now.	PR10106
called PrecisionPlugin.master_params()	update it PrecisionPlugin.main_params()	PR10105

devel 1.7

If	Then	Ref
Removed the legacy Trainer.get_deprecated_arg_names()		PR14415
used the generic method Trainer.run_stage	switch to a specific one depending on your purpose Trainer.{fit,validate,test,predict} .	PR11000
used rank_zero_only from pl.utilities.distributed	import it from pl.utilities.rank_zero	PR11747
used rank_zero_debug from pl.utilities.distributed	import it from pl.utilities.rank_zero	PR11747
used rank_zero_info from pl.utilities.distributed	import it from pl.utilities.rank_zero	PR11747
used rank_zero_warn from pl.utilities.warnings	import it from pl.utilities.rank_zero	PR11747
used rank_zero_deprecation from pl.utilities.warnings	import it from pl.utilities.rank_zero	PR11747
used LightningDeprecationWarning from pl.utilities.warnings	import it from pl.utilities.rank_zero	PR11747
used LightningDeprecationWarning from pl.utilities.warnings	import it from pl.utilities.rank_zero	PR11747
used Trainer.data_parallel_device_ids attribute	switch it to Trainer.device_ids	PR12072
derived it from TrainerCallbackHookMixin	use Trainer base class	PR14401
used base class pytorch_lightning.profiler.BaseProfilerto	switch to use pytorch_lightning.profiler.Profiler instead	PR12150
set distributed backend via the environment variable PL_TORCH_DISTRIBUTED_BACKEND	use process_group_backend in the strategy constructor	PR11745
used PrecisionPlugin.on_load_checkpoint hooks	switch to PrecisionPlugin.load_state_dict	PR11978
used PrecisionPlugin.on_save_checkpoint hooks	switch to PrecisionPlugin.load_state_dict	PR11978
used Trainer.root_gpu attribute	use Trainer.strategy.root_device.index when GPU is used	PR12262
used Trainer.use_amp attribute	rely on Torch native AMP	PR12312
used LightningModule.use_amp attribute	rely on Torch native AMP	PR12315
used Trainer’s attribute Trainer.verbose_evaluate	rely on loop constructor EvaluationLoop(verbose=...)	PR10931
used Trainer’s attribute Trainer.should_rank_save_checkpoint	it was removed	PR11068
derived from TrainerOptimizersMixin	rely on core/optimizer.py	PR11155
derived from TrainerDataLoadingMixin	rely on methods from Trainer and DataConnector	PR11282
used Trainer’s attribute Trainer.lightning_optimizers	switch to the Strategy and its attributes.	PR11444
used Trainer.call_hook	it was set as a protected method Trainer._call_callback_hooks, Trainer._call_lightning_module_hook, Trainer._call_ttp_hook, Trainer._call_accelerator_hook and shall not be used.	PR10979
used Profiler’s attribute SimpleProfiler.profile_iterable	it was removed	PR12102
used Profiler’s attribute AdvancedProfiler.profile_iterable	it was removed	PR12102
used the device_stats_monitor.prefix_metric_keys		PR11254
used on_train_batch_end(outputs, ...) with 2d list with sizes (n_optimizers, tbptt_steps)	chang it to (tbptt_steps, n_optimizers). You can update your code by adding the following parameter to your hook signature: on_train_batch_end(outputs, ..., new_format=True).	PR12182
used training_epoch_end(outputs) with a 3d list with sizes (n_optimizers, n_batches, tbptt_steps)	change it to (n_batches, tbptt_steps, n_optimizers). You can update your code by adding the following parameter to your hook signature: training_epoch_end(outputs, new_format=True).	PR12182

devel 1.8

If	Then	Ref
derived from pytorch_lightning.loggers.base.LightningLoggerBase	derive from pytorch_lightning.loggers.logger.Logger	PR12014
derived from pytorch_lightning.profiler.base.BaseProfiler	derive from pytorch_lightning.profilers.profiler.Profiler	PR12150
derived from pytorch_lightning.profiler.base.AbstractProfiler	derive from pytorch_lightning.profilers.profiler.Profiler	PR12106

devel 1.9

If	Then	Ref
passed the pl_module argument to distributed module wrappers	passed the (required) forward_module argument	PR16386
used DataParallel and the LightningParallelModule wrapper	use DDP or DeepSpeed instead	PR16748 DDP
used pl_module argument from the distributed module wrappers	use DDP or DeepSpeed instead	PR16386 DDP
called pl.overrides.base.unwrap_lightning_module function	use DDP or DeepSpeed instead	PR16386 DDP
used or derived from pl.overrides.distributed.LightningDistributedModule class	use DDP instead	PR16386 DDP
used the pl.plugins.ApexMixedPrecisionPlugin plugin	use PyTorch native mixed precision	PR16039
used the pl.plugins.NativeMixedPrecisionPlugin plugin	switch to the pl.plugins.MixedPrecisionPlugin plugin	PR16039
used the fit_loop.min_steps setters	implement your training loop with Fabric	PR16803
used the fit_loop.max_steps setters	implement your training loop with Fabric	PR16803
used the data_parallel attribute in Trainer	check the same using isinstance(trainer.strategy, ParallelStrategy)	PR16703
used any function from pl.utilities.xla_device	switch to pl.accelerators.XLAAccelerator.is_available()	PR14514 PR14550
imported functions from pl.utilities.device_parser.*	import them from lightning_fabric.utilities.device_parser.*	PR14492 PR14753
imported functions from pl.utilities.cloud_io.*	import them from lightning_fabric.utilities.cloud_io.*	PR14515
imported functions from pl.utilities.apply_func.*	import them from lightning_utilities.core.apply_func.*	PR14516 PR14537
used any code from pl.core.mixins	use the base classes	PR16424
used any code from pl.utilities.distributed	rely on Pytorch’s native functions	PR16390
used any code from pl.utilities.data	it was removed	PR16440
used any code from pl.utilities.optimizer	it was removed	PR16439
used any code from pl.utilities.seed	it was removed	PR16422
were using truncated backpropagation through time (TBPTT) with LightningModule.truncated_bptt_steps	use manual optimization	PR16172 Manual Optimization
were using truncated backpropagation through time (TBPTT) with LightningModule.tbptt_split_batch	use manual optimization	PR16172 Manual Optimization
were using truncated backpropagation through time (TBPTT) and passing hidden to LightningModule.training_step	use manual optimization	PR16172 Manual Optimization
used pl.utilities.finite_checks.print_nan_gradients function	it was removed
used pl.utilities.finite_checks.detect_nan_parameters function	it was removed
used pl.utilities.parsing.flatten_dict function	it was removed
used pl.utilities.metrics.metrics_to_scalars function	it was removed
used pl.utilities.memory.get_model_size_mb function	it was removed
used pl.strategies.utils.on_colab_kaggle function	it was removed	PR16437
used LightningDataModule.add_argparse_args() method	switch to using LightningCLI	PR16708
used LightningDataModule.parse_argparser() method	switch to using LightningCLI	PR16708
used LightningDataModule.from_argparse_args() method	switch to using LightningCLI	PR16708
used LightningDataModule.get_init_arguments_and_types() method	switch to using LightningCLI	PR16708
used Trainer.default_attributes() method	switch to using LightningCLI	PR16708
used Trainer.from_argparse_args() method	switch to using LightningCLI	PR16708
used Trainer.parse_argparser() method	switch to using LightningCLI	PR16708
used Trainer.match_env_arguments() method	switch to using LightningCLI	PR16708
used Trainer.add_argparse_args() method	switch to using LightningCLI	PR16708
used pl.utilities.argparse.from_argparse_args() function	switch to using LightningCLI	PR16708
used pl.utilities.argparse.parse_argparser() function	switch to using LightningCLI	PR16708
used pl.utilities.argparseparse_env_variables() function	switch to using LightningCLI	PR16708
used get_init_arguments_and_types() function	switch to using LightningCLI	PR16708
used pl.utilities.argparse.add_argparse_args() function	switch to using LightningCLI	PR16708
used pl.utilities.parsing.str_to_bool() function	switch to using LightningCLI	PR16708
used pl.utilities.parsing.str_to_bool_or_int() function	switch to using LightningCLI	PR16708
used pl.utilities.parsing.str_to_bool_or_str() function	switch to using LightningCLI	PR16708
derived from pl.utilities.distributed.AllGatherGrad class	switch to PyTorch native equivalent	PR15364
used PL_RECONCILE_PROCESS=1 env. variable	customize your logger	PR16204
if you derived from mixin’s method pl.core.saving.ModelIO.load_from_checkpoint	rely on pl.core.module.LightningModule	PR16999
used Accelerator.setup_environment method	switch to Accelerator.setup_device	PR16436
used PL_FAULT_TOLERANT_TRAINING env. variable	implement own logic with Fabric	PR16516 PR16533
used or derived from public pl.overrides.distributed.IndexBatchSamplerWrapper class	it is set as protected	PR16826
used the DataLoaderLoop class	use manual optimization	PR16726 Manual Optimization
used the EvaluationEpochLoop class	use manual optimization	PR16726 Manual Optimization
used the PredictionEpochLoop class	use manual optimization	PR16726 Manual Optimization
used trainer.reset_*_dataloader() methods	use Loop.setup_data() for the top-level loops	PR16726
used LightningModule.precision attribute	rely on Trainer precision attribute	PR16203
used Trainer.model setter	you shall pass the model in fit/test/predict method	PR16462
relied on pl.utilities.supporters.CombinedLoaderIterator class	pass dataloders directly	PR16714
relied on pl.utilities.supporters.CombinedLoaderIterator class	pass dataloders directly	PR16714
used pl.callbacks.progress.base.ProgressBarBase	rename to pl.callbacks.progress.ProgressBar	PR17058
accessed ProgressBarBase.train_batch_idx property	rely on Trainer internal loops’ properties	PR16760
accessed ProgressBarBase.val_batch_idx property	rely on Trainer internal loops’ properties	PR16760
accessed ProgressBarBase.test_batch_idx property	rely on Trainer internal loops’ properties	PR16760
accessed ProgressBarBase.predict_batch_idx property	rely on Trainer internal loops’ properties	PR16760
used Trainer.prediction_writer_callbacks property	rely on precision plugin	PR16759
used PrecisionPlugin.dispatch	it was removed	PR16618
used Strategy.dispatch	it was removed	PR16618