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 |