---
title: 1.9.x
canonical: 'https://lightning.ai/docs/pytorch/stable/home/migration_guide/from_1_9'
---
# 1.9.x {#1-9-x}

## Regular User {#regular-user}

| If | Then | Ref |
| --- | --- | --- |
| used Python 3.7 | upgrade to Python 3.8 or higher | [PR16579](https://github.com/Lightning-AI/pytorch-lightning/pull/16579) |
| used PyTorch 1.10 | upgrade to PyTorch 1.11 or higher | [PR16492](https://github.com/Lightning-AI/pytorch-lightning/pull/16492) |
| used Trainer’s flag `gpus` | use `devices` with the same number | [PR16171](https://github.com/Lightning-AI/pytorch-lightning/pull/16171) |
| used Trainer’s flag `tpu_cores` | use `devices` with the same number | [PR16171](https://github.com/Lightning-AI/pytorch-lightning/pull/16171) |
| used Trainer’s flag `ipus` | use `devices` with the same number | [PR16171](https://github.com/Lightning-AI/pytorch-lightning/pull/16171) |
| used Trainer’s flag `num_processes` | use `devices` with the same number | [PR16171](https://github.com/Lightning-AI/pytorch-lightning/pull/16171) |
| used Trainer’s flag `resume_from_checkpoint` | pass the path to the `Trainer.fit(ckpt_path="...")` method, | [PR10061](https://github.com/Lightning-AI/pytorch-lightning/pull/10061) |
| used Trainer’s flag `auto_select_gpus` | use `devices="auto"` | [PR16184](https://github.com/Lightning-AI/pytorch-lightning/pull/16184) |
| called the `pl.tuner.auto_gpu_select.pick_single_gpu` function | use Trainer’s flag `devices="auto"` | [PR16184](https://github.com/Lightning-AI/pytorch-lightning/pull/16184) |
| called the `pl.tuner.auto_gpu_select.pick_multiple_gpus` functions | use Trainer’s flag `devices="auto"` | [PR16184](https://github.com/Lightning-AI/pytorch-lightning/pull/16184) |
| used Trainer’s flag  `accumulate_grad_batches` with a scheduling dictionary value | use the  `GradientAccumulationScheduler` callback and configure it | [PR16729](https://github.com/Lightning-AI/pytorch-lightning/pull/16729) |
| imported profiles from `pl.profiler` | import from `pl.profilers` | [PR16359](https://github.com/Lightning-AI/pytorch-lightning/pull/16359) |
| 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](/docs/pytorch/2.6.6/reference/advanced/training_tricks.md) [learning_rate_finder](/docs/pytorch/2.6.6/reference/advanced/training_tricks.md) |
| 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 {#advanced-user}

| If | Then | Ref |
| --- | --- | --- |
| used the `pl.lite` module | switch to `lightning_fabric` | [PR15953](https://github.com/Lightning-AI/pytorch-lightning/pull/15953) |
| used Trainer’s flag `strategy='dp'` | use DDP with `strategy='ddp'` or DeepSpeed instead | [PR16748](https://github.com/Lightning-AI/pytorch-lightning/pull/16748) |
| implemented `LightningModule.training_epoch_end` hooks | port your logic to  `LightningModule.on_train_epoch_end` hook | [PR16520](https://github.com/Lightning-AI/pytorch-lightning/pull/16520) |
| implemented `LightningModule.validation_epoch_end` hook | port your logic to  `LightningModule.on_validation_epoch_end` hook | [PR16520](https://github.com/Lightning-AI/pytorch-lightning/pull/16520) |
| implemented `LightningModule.test_epoch_end` hooks | port your logic to  `LightningModule.on_test_epoch_end` hook | [PR16520](https://github.com/Lightning-AI/pytorch-lightning/pull/16520) |
| used Trainer’s flag `multiple_trainloader_mode` | switch to  `CombinedLoader(..., mode=...)` and set mode directly now | [PR16800](https://github.com/Lightning-AI/pytorch-lightning/pull/16800) |
| used Trainer’s flag `move_metrics_to_cpu` | implement particular offload logic in your custom metric or turn it on in `torchmetrics` | [PR16358](https://github.com/Lightning-AI/pytorch-lightning/pull/16358) |
| 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](https://github.com/Lightning-AI/pytorch-lightning/pull/16745) [PR16745](https://github.com/Lightning-AI/pytorch-lightning/pull/16745) |
| 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](https://github.com/Lightning-AI/pytorch-lightning/pull/16537) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| relied on the `using_lbfgs` argument in `LightningModule.optimizer_step` hook | switch to manual optimization | [PR16538](https://github.com/Lightning-AI/pytorch-lightning/pull/16538) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| were using `nvidia/apex` in any form | switch to PyTorch native mixed precision `torch.amp` instead | [PR16039](https://github.com/Lightning-AI/pytorch-lightning/pull/16039) [Precision](/docs/pytorch/2.6.6/reference/common/precision.md) |
| used Trainer’s flag `using_native_amp` | use PyTorch native mixed precision | [PR16039](https://github.com/Lightning-AI/pytorch-lightning/pull/16039) [Precision](/docs/pytorch/2.6.6/reference/common/precision.md) |
| used Trainer’s flag `amp_backend` | use PyTorch native mixed precision | [PR16039](https://github.com/Lightning-AI/pytorch-lightning/pull/16039) [Precision](/docs/pytorch/2.6.6/reference/common/precision.md) |
| used Trainer’s flag `amp_level` | use PyTorch native mixed precision | [PR16039](https://github.com/Lightning-AI/pytorch-lightning/pull/16039) [Precision](/docs/pytorch/2.6.6/reference/common/precision.md) |
| used Trainer’s attribute `using_native_amp` | use PyTorch native mixed precision | [PR16039](https://github.com/Lightning-AI/pytorch-lightning/pull/16039) [Precision](/docs/pytorch/2.6.6/reference/common/precision.md) |
| used Trainer’s attribute `amp_backend` | use PyTorch native mixed precision | [PR16039](https://github.com/Lightning-AI/pytorch-lightning/pull/16039) [Precision](/docs/pytorch/2.6.6/reference/common/precision.md) |
| used Trainer’s attribute `amp_level` | use PyTorch native mixed precision | [PR16039](https://github.com/Lightning-AI/pytorch-lightning/pull/16039) [Precision](/docs/pytorch/2.6.6/reference/common/precision.md) |
| use the `FairScale` integration | consider using PyTorch's native FSDP implementation or outsourced implementation into own project | [lightning-Fairscale](https://github.com/Lightning-Sandbox/lightning-Fairscale) |
| used `pl.overrides.fairscale.LightningShardedDataParallel` | use native FSDP instead | [PR16400](https://github.com/Lightning-AI/pytorch-lightning/pull/16400) [FSDP](/docs/pytorch/2.6.6/reference/accelerators/gpu_expert.md) |
| used `pl.plugins.precision.fully_sharded_native_amp.FullyShardedNativeMixedPrecisionPlugin` | use native FSDP instead | [PR16400](https://github.com/Lightning-AI/pytorch-lightning/pull/16400) [FSDP](/docs/pytorch/2.6.6/reference/accelerators/gpu_expert.md) |
| used `pl.plugins.precision.sharded_native_amp.ShardedNativeMixedPrecisionPlugin` | use native FSDP instead | [PR16400](https://github.com/Lightning-AI/pytorch-lightning/pull/16400) [FSDP](/docs/pytorch/2.6.6/reference/accelerators/gpu_expert.md) |
| used `pl.strategies.fully_sharded.DDPFullyShardedStrategy` | use native FSDP instead | [PR16400](https://github.com/Lightning-AI/pytorch-lightning/pull/16400) [FSDP](/docs/pytorch/2.6.6/reference/accelerators/gpu_expert.md) |
| used `pl.strategies.sharded.DDPShardedStrategy` | use native FSDP instead | [PR16400](https://github.com/Lightning-AI/pytorch-lightning/pull/16400) [FSDP](/docs/pytorch/2.6.6/reference/accelerators/gpu_expert.md) |
| used `pl.strategies.sharded_spawn.DDPSpawnShardedStrategy` | use native FSDP instead | [PR16400](https://github.com/Lightning-AI/pytorch-lightning/pull/16400) [FSDP](/docs/pytorch/2.6.6/reference/accelerators/gpu_expert.md) |
| used `save_config_overwrite` parameters in `LightningCLI` | pass this option and via dictionary of `save_config_kwargs` parameter | [PR14998](https://github.com/Lightning-AI/pytorch-lightning/pull/14998) |
| used `save_config_multifile` parameters in `LightningCLI` | pass this option and via dictionary of `save_config_kwargs` parameter | [PR14998](https://github.com/Lightning-AI/pytorch-lightning/pull/14998) |
| have customized loops `Loop.replace()` | implement your training loop with Fabric. | [PR14998](https://github.com/Lightning-AI/pytorch-lightning/pull/14998) [Fabric](/docs/fabric) |
| have customized loops `Loop.run()` | implement your training loop with Fabric. | [PR14998](https://github.com/Lightning-AI/pytorch-lightning/pull/14998) [Fabric](/docs/fabric) |
| have customized loops `Loop.connect()` | implement your training loop with Fabric. | [PR14998](https://github.com/Lightning-AI/pytorch-lightning/pull/14998) [Fabric](/docs/fabric) |
| used the Trainer’s `trainer.fit_loop` property | implement your training loop with Fabric | [PR14998](https://github.com/Lightning-AI/pytorch-lightning/pull/14998) [Fabric](/docs/fabric) |
| used the Trainer’s `trainer.validate_loop` property | implement your training loop with Fabric | [PR14998](https://github.com/Lightning-AI/pytorch-lightning/pull/14998) [Fabric](/docs/fabric) |
| used the Trainer’s `trainer.test_loop` property | implement your training loop with Fabric | [PR14998](https://github.com/Lightning-AI/pytorch-lightning/pull/14998) [Fabric](/docs/fabric) |
| used the Trainer’s `trainer.predict_loop` property | implement your training loop with Fabric | [PR14998](https://github.com/Lightning-AI/pytorch-lightning/pull/14998) [Fabric](/docs/fabric) |
| used the `Trainer.loop` and fetching classes | being marked as protected |  |
| used `opt_idx` argument in `BaseFinetuning.finetune_function` | use manual optimization | [PR16539](https://github.com/Lightning-AI/pytorch-lightning/pull/16539) |
| used `opt_idx` argument in `Callback.on_before_optimizer_step` | use manual optimization | [PR16539](https://github.com/Lightning-AI/pytorch-lightning/pull/16539) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used `optimizer_idx` as an optional argument in `LightningModule.training_step` | use manual optimization | [PR16539](https://github.com/Lightning-AI/pytorch-lightning/pull/16539) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used `optimizer_idx` argument in `LightningModule.on_before_optimizer_step` | use manual optimization | [PR16539](https://github.com/Lightning-AI/pytorch-lightning/pull/16539) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used `optimizer_idx` argument in `LightningModule.configure_gradient_clipping` | use manual optimization | [PR16539](https://github.com/Lightning-AI/pytorch-lightning/pull/16539) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used `optimizer_idx` argument in `LightningModule.optimizer_step` | use manual optimization | [PR16539](https://github.com/Lightning-AI/pytorch-lightning/pull/16539) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used `optimizer_idx` argument in `LightningModule.optimizer_zero_grad` | use manual optimization | [PR16539](https://github.com/Lightning-AI/pytorch-lightning/pull/16539) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used `optimizer_idx` argument in `LightningModule.lr_scheduler_step` | use manual optimization | [PR16539](https://github.com/Lightning-AI/pytorch-lightning/pull/16539) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used declaring optimizer frequencies in the dictionary returned from `LightningModule.configure_optimizers` | use manual optimization | [PR16539](https://github.com/Lightning-AI/pytorch-lightning/pull/16539) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used `optimizer` argument in `LightningModule.backward` | use manual optimization | [PR16539](https://github.com/Lightning-AI/pytorch-lightning/pull/16539) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used `optimizer_idx` argument in `LightningModule.backward` | use manual optimization | [PR16539](https://github.com/Lightning-AI/pytorch-lightning/pull/16539) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used `optimizer_idx` argument in `PrecisionPlugin.optimizer_step` | use manual optimization | [PR16539](https://github.com/Lightning-AI/pytorch-lightning/pull/16539) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used `optimizer_idx` argument in `PrecisionPlugin.,backward` | use manual optimization | [PR16539](https://github.com/Lightning-AI/pytorch-lightning/pull/16539) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used `optimizer_idx` argument in `PrecisionPlugin.optimizer_step` | use manual optimization | [PR16539](https://github.com/Lightning-AI/pytorch-lightning/pull/16539) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used `optimizer_idx` argument in `Strategy.backward` | use manual optimization | [PR16539](https://github.com/Lightning-AI/pytorch-lightning/pull/16539) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used `optimizer_idx` argument in `Strategy.optimizer_step` | use manual optimization | [PR16539](https://github.com/Lightning-AI/pytorch-lightning/pull/16539) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used Trainer’s `Trainer.optimizer_frequencies` attribute | use manual optimization | [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used `PL_INTER_BATCH_PARALLELISM` environment flag |  | [PR16355](https://github.com/Lightning-AI/pytorch-lightning/pull/16355) |
| used training integration with Horovod | install standalone package/project | [lightning-Horovod](https://github.com/Lightning-AI/lightning-Horovod) |
| used training integration with ColossalAI | install standalone package/project | [lightning-ColossalAI](/docs/pytorch/2.1.0/integrations/strategies/colossalai) |
| used `QuantizationAwareTraining` callback | use Torch’s Quantization directly | [PR16750](https://github.com/Lightning-AI/pytorch-lightning/pull/16750) |
| had any logic except reducing the DP outputs in  `LightningModule.training_step_end` hook | port it to `LightningModule.on_train_batch_end` hook | [PR16791](https://github.com/Lightning-AI/pytorch-lightning/pull/16791) |
| had any logic except reducing the DP outputs in  `LightningModule.validation_step_end` hook | port it to `LightningModule.on_validation_batch_end` hook | [PR16791](https://github.com/Lightning-AI/pytorch-lightning/pull/16791) |
| had any logic except reducing the DP outputs in  `LightningModule.test_step_end` hook | port it to `LightningModule.on_test_batch_end` hook | [PR16791](https://github.com/Lightning-AI/pytorch-lightning/pull/16791) |
| used `pl.strategies.DDPSpawnStrategy` | switch to general  `DDPStrategy(start_method='spawn')` with proper starting method | [PR16809](https://github.com/Lightning-AI/pytorch-lightning/pull/16809) |
| 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](https://github.com/Lightning-AI/pytorch-lightning/pull/16192) |
| rely on the `outputs` argument from the `on_predict_epoch_end` hook | access them via `trainer.predict_loop.predictions` | [PR16655](https://github.com/Lightning-AI/pytorch-lightning/pull/16655) |
| need to pass a dictionary to `self.log()` | pass them independently. | [PR16389](https://github.com/Lightning-AI/pytorch-lightning/pull/16389) |

## Developer {#developer}

| If | Then | Ref |
| --- | --- | --- |
| passed the `pl_module` argument to distributed module wrappers | passed the (required) `forward_module` argument | [PR16386](https://github.com/Lightning-AI/pytorch-lightning/pull/16386) |
| used `DataParallel` and the `LightningParallelModule` wrapper | use DDP or DeepSpeed instead | [PR16748](https://github.com/Lightning-AI/pytorch-lightning/pull/16748) [DDP](/docs/pytorch/2.6.6/reference/accelerators/gpu_expert.md) |
| used `pl_module` argument from the distributed module wrappers | use DDP or DeepSpeed instead | [PR16386](https://github.com/Lightning-AI/pytorch-lightning/pull/16386) [DDP](/docs/pytorch/2.6.6/reference/accelerators/gpu_expert.md) |
| called `pl.overrides.base.unwrap_lightning_module` function | use DDP or DeepSpeed instead | [PR16386](https://github.com/Lightning-AI/pytorch-lightning/pull/16386) [DDP](/docs/pytorch/2.6.6/reference/accelerators/gpu_expert.md) |
| used or derived from `pl.overrides.distributed.LightningDistributedModule` class | use DDP instead | [PR16386](https://github.com/Lightning-AI/pytorch-lightning/pull/16386) [DDP](/docs/pytorch/2.6.6/reference/accelerators/gpu_expert.md) |
| used the `pl.plugins.ApexMixedPrecisionPlugin` plugin | use PyTorch native mixed precision | [PR16039](https://github.com/Lightning-AI/pytorch-lightning/pull/16039) |
| used the `pl.plugins.NativeMixedPrecisionPlugin` plugin | switch to the `pl.plugins.MixedPrecisionPlugin` plugin | [PR16039](https://github.com/Lightning-AI/pytorch-lightning/pull/16039) |
| used the `fit_loop.min_steps` setters | implement your training loop with Fabric | [PR16803](https://github.com/Lightning-AI/pytorch-lightning/pull/16803) |
| used the `fit_loop.max_steps` setters | implement your training loop with Fabric | [PR16803](https://github.com/Lightning-AI/pytorch-lightning/pull/16803) |
| used the `data_parallel` attribute in `Trainer` | check the same using `isinstance(trainer.strategy, ParallelStrategy)` | [PR16703](https://github.com/Lightning-AI/pytorch-lightning/pull/16703) |
| used any function from `pl.utilities.xla_device` | switch to `pl.accelerators.XLAAccelerator.is_available()` | [PR14514](https://github.com/Lightning-AI/pytorch-lightning/pull/14514) [PR14550](https://github.com/Lightning-AI/pytorch-lightning/pull/14550) |
| imported functions from  `pl.utilities.device_parser.*` | import them from `lightning_fabric.utilities.device_parser.*` | [PR14492](https://github.com/Lightning-AI/pytorch-lightning/pull/14492) [PR14753](https://github.com/Lightning-AI/pytorch-lightning/pull/14753) |
| imported functions from `pl.utilities.cloud_io.*` | import them from `lightning_fabric.utilities.cloud_io.*` | [PR14515](https://github.com/Lightning-AI/pytorch-lightning/pull/14515) |
| imported functions from `pl.utilities.apply_func.*` | import them from `lightning_utilities.core.apply_func.*` | [PR14516](https://github.com/Lightning-AI/pytorch-lightning/pull/14516) [PR14537](https://github.com/Lightning-AI/pytorch-lightning/pull/14537) |
| used any code from `pl.core.mixins` | use the base classes | [PR16424](https://github.com/Lightning-AI/pytorch-lightning/pull/16424) |
| used any code from `pl.utilities.distributed` | rely on Pytorch's native functions | [PR16390](https://github.com/Lightning-AI/pytorch-lightning/pull/16390) |
| used any code from `pl.utilities.data` | it was removed | [PR16440](https://github.com/Lightning-AI/pytorch-lightning/pull/16440) |
| used any code from `pl.utilities.optimizer` | it was removed | [PR16439](https://github.com/Lightning-AI/pytorch-lightning/pull/16439) |
| used any code from `pl.utilities.seed` | it was removed | [PR16422](https://github.com/Lightning-AI/pytorch-lightning/pull/16422) |
| were using truncated backpropagation through time (TBPTT) with `LightningModule.truncated_bptt_steps` | use manual optimization | [PR16172](https://github.com/Lightning-AI/pytorch-lightning/pull/16172) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| were using truncated backpropagation through time (TBPTT) with `LightningModule.tbptt_split_batch` | use manual optimization | [PR16172](https://github.com/Lightning-AI/pytorch-lightning/pull/16172) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| were using truncated backpropagation through time (TBPTT) and passing `hidden`  to `LightningModule.training_step` | use manual optimization | [PR16172](https://github.com/Lightning-AI/pytorch-lightning/pull/16172) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| 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](https://github.com/Lightning-AI/pytorch-lightning/pull/16437) |
| used `LightningDataModule.add_argparse_args()` method | switch to using `LightningCLI` | [PR16708](https://github.com/Lightning-AI/pytorch-lightning/pull/16708) |
| used `LightningDataModule.parse_argparser()` method | switch to using `LightningCLI` | [PR16708](https://github.com/Lightning-AI/pytorch-lightning/pull/16708) |
| used `LightningDataModule.from_argparse_args()` method | switch to using `LightningCLI` | [PR16708](https://github.com/Lightning-AI/pytorch-lightning/pull/16708) |
| used `LightningDataModule.get_init_arguments_and_types()` method | switch to using `LightningCLI` | [PR16708](https://github.com/Lightning-AI/pytorch-lightning/pull/16708) |
| used `Trainer.default_attributes()` method | switch to using `LightningCLI` | [PR16708](https://github.com/Lightning-AI/pytorch-lightning/pull/16708) |
| used `Trainer.from_argparse_args()` method | switch to using `LightningCLI` | [PR16708](https://github.com/Lightning-AI/pytorch-lightning/pull/16708) |
| used `Trainer.parse_argparser()` method | switch to using `LightningCLI` | [PR16708](https://github.com/Lightning-AI/pytorch-lightning/pull/16708) |
| used `Trainer.match_env_arguments()` method | switch to using `LightningCLI` | [PR16708](https://github.com/Lightning-AI/pytorch-lightning/pull/16708) |
| used `Trainer.add_argparse_args()` method | switch to using `LightningCLI` | [PR16708](https://github.com/Lightning-AI/pytorch-lightning/pull/16708) |
| used `pl.utilities.argparse.from_argparse_args()` function | switch to using `LightningCLI` | [PR16708](https://github.com/Lightning-AI/pytorch-lightning/pull/16708) |
| used `pl.utilities.argparse.parse_argparser()` function | switch to using `LightningCLI` | [PR16708](https://github.com/Lightning-AI/pytorch-lightning/pull/16708) |
| used `pl.utilities.argparseparse_env_variables()` function | switch to using `LightningCLI` | [PR16708](https://github.com/Lightning-AI/pytorch-lightning/pull/16708) |
| used `get_init_arguments_and_types()` function | switch to using `LightningCLI` | [PR16708](https://github.com/Lightning-AI/pytorch-lightning/pull/16708) |
| used `pl.utilities.argparse.add_argparse_args()` function | switch to using `LightningCLI` | [PR16708](https://github.com/Lightning-AI/pytorch-lightning/pull/16708) |
| used `pl.utilities.parsing.str_to_bool()` function | switch to using `LightningCLI` | [PR16708](https://github.com/Lightning-AI/pytorch-lightning/pull/16708) |
| used `pl.utilities.parsing.str_to_bool_or_int()` function | switch to using `LightningCLI` | [PR16708](https://github.com/Lightning-AI/pytorch-lightning/pull/16708) |
| used `pl.utilities.parsing.str_to_bool_or_str()` function | switch to using `LightningCLI` | [PR16708](https://github.com/Lightning-AI/pytorch-lightning/pull/16708) |
| derived from `pl.utilities.distributed.AllGatherGrad` class | switch to PyTorch native equivalent | [PR15364](https://github.com/Lightning-AI/pytorch-lightning/pull/15364) |
| used `PL_RECONCILE_PROCESS=1` env. variable | customize your logger | [PR16204](https://github.com/Lightning-AI/pytorch-lightning/pull/16204) |
| if you derived from mixin’s method `pl.core.saving.ModelIO.load_from_checkpoint` | rely on `pl.core.module.LightningModule` | [PR16999](https://github.com/Lightning-AI/pytorch-lightning/pull/16999) |
| used  `Accelerator.setup_environment` method | switch to `Accelerator.setup_device` | [PR16436](https://github.com/Lightning-AI/pytorch-lightning/pull/16436) |
| used `PL_FAULT_TOLERANT_TRAINING` env. variable | implement own logic with Fabric | [PR16516](https://github.com/Lightning-AI/pytorch-lightning/pull/16516) [PR16533](https://github.com/Lightning-AI/pytorch-lightning/pull/16533) |
| used or derived from public `pl.overrides.distributed.IndexBatchSamplerWrapper` class | it is set as protected | [PR16826](https://github.com/Lightning-AI/pytorch-lightning/pull/16826) |
| used the `DataLoaderLoop` class | use manual optimization | [PR16726](https://github.com/Lightning-AI/pytorch-lightning/pull/16726) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used the `EvaluationEpochLoop` class | use manual optimization | [PR16726](https://github.com/Lightning-AI/pytorch-lightning/pull/16726) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used the `PredictionEpochLoop` class | use manual optimization | [PR16726](https://github.com/Lightning-AI/pytorch-lightning/pull/16726) [Manual Optimization](/docs/pytorch/2.6.6/reference/model/manual_optimization.md) |
| used `trainer.reset_*_dataloader()` methods | use  `Loop.setup_data()` for the top-level loops | [PR16726](https://github.com/Lightning-AI/pytorch-lightning/pull/16726) |
| used `LightningModule.precision` attribute | rely on Trainer precision attribute | [PR16203](https://github.com/Lightning-AI/pytorch-lightning/pull/16203) |
| used  `Trainer.model` setter | you shall pass the `model` in fit/test/predict method | [PR16462](https://github.com/Lightning-AI/pytorch-lightning/pull/16462) |
| relied on `pl.utilities.supporters.CombinedLoaderIterator` class | pass dataloders directly | [PR16714](https://github.com/Lightning-AI/pytorch-lightning/pull/16714) |
| relied on `pl.utilities.supporters.CombinedLoaderIterator` class | pass dataloders directly | [PR16714](https://github.com/Lightning-AI/pytorch-lightning/pull/16714) |
| used `pl.callbacks.progress.base.ProgressBarBase` | rename to `pl.callbacks.progress.ProgressBar` | [PR17058](https://github.com/Lightning-AI/pytorch-lightning/pull/17058) |
| accessed `ProgressBarBase.train_batch_idx` property | rely on Trainer internal loops’ properties | [PR16760](https://github.com/Lightning-AI/pytorch-lightning/pull/16760) |
| accessed `ProgressBarBase.val_batch_idx` property | rely on Trainer internal loops’ properties | [PR16760](https://github.com/Lightning-AI/pytorch-lightning/pull/16760) |
| accessed `ProgressBarBase.test_batch_idx` property | rely on Trainer internal loops’ properties | [PR16760](https://github.com/Lightning-AI/pytorch-lightning/pull/16760) |
| accessed `ProgressBarBase.predict_batch_idx` property | rely on Trainer internal loops’ properties | [PR16760](https://github.com/Lightning-AI/pytorch-lightning/pull/16760) |
| used `Trainer.prediction_writer_callbacks` property | rely on precision plugin | [PR16759](https://github.com/Lightning-AI/pytorch-lightning/pull/16759) |
| used `PrecisionPlugin.dispatch` | it was removed | [PR16618](https://github.com/Lightning-AI/pytorch-lightning/pull/16618) |
| used `Strategy.dispatch` | it was removed | [PR16618](https://github.com/Lightning-AI/pytorch-lightning/pull/16618) |

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