Regular User

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 1.9
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 1.9
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