Upgrade from 1.6 to the 2.0
Regular User
| 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 |
| 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 |
| 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 |
| 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
| 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 |
| 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 |
| 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 |
| 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
| 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 |
| 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 |
| 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 |
| 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 |
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