Upgrade from 1.8 to the 2.0

Regular User

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.reset_train_dataloaders() and Trainer.reset_val_dataloaders() separately	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 1.8

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 1.8

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.TPUAccelerator.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
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