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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


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