Remove deprecated code after the 1.6 release

Proposed refactor

Remove deprecated code after the 1.6 release.

NOTE: Please pick up a single item from the list (by commenting here in the issue) - and if there are no conflicts - we will happily assign you and put your name in front of the item in the list.

Please note that unless mentioned, the classes are importable from pytorch_lightning, example: from pytorch_lightning import Trainer.

LightningModule.summarize -> #12559
pytorch_lightning.core.memory.LayerSummary -> #12593
pytorch_lightning.core.memory.ModelSummary -> #12593
pytorch_lightning.core.memory.get_gpu_memory_map -> #12644
pytorch_lightning.core.memory.get_memory_profile -> #12659
LightningModule.model_size -> #12641
LightningDataModule.train_transforms -> #12662
LightningDataModule.val_transforms -> #12763
LightningDataModule.test_transforms -> #12773
LightningDataModule.size -> #12780
LightningDataModule.dims and LightningDataModule(dims=...) -> #12780
LightningModule.get_progress_bar_dict -> #12839
Trainer.progress_bar_dict -> #12839
Trainer(prepare_data_per_node=...) -> #12536
Trainer(stochastic_weight_avg=...) -> #12535
Trainer(terminate_on_nan=...) and Trainer.terminate_on_nan -> #12553
LightningModule.on_{train,val,test,predict}_dataloader -> #13033
pytorch_lightning.loggers.TestTubeLogger -> #12859
pytorch_lightning.Callback.on_keyboard_interrupt -> #13438
Trainer(process_position=...) -> #13071
Trainer(flush_logs_every_n_steps=...) -> #13074
LightningModule.add_to_queue -> @shenoynikhil
LightningModule.get_from_queue -> @shenoynikhil
Trainer(progress_bar_refresh_rate=...) -> #12514
LightningLoggerBase.close and pytorch_lightning.loggers.LoggerCollection.close -> #13149
pytorch_lightning.distributed.dist.LightningDistributed #13549
Trainer(checkpoint_callback=...) -> #13027
Passing dataloader_idx to on_train_batch_start of pytorch_lightning.Callback and LightningModule -> #12769
LightningModule.on_post_move_to_device #13548
pytorch_lightning.core.decorators.parameter_validation #13514
Trainer(accelerator="ddp_spawn") #12696
Trainer(plugins="ddp_spawn") #12700
Trainer(weights_summary="full"), Trainer(weights_summary=None), Trainer.weights_summary -> #13070
Trainer(log_gpu_memory=...) -> #12657
Trainer.slurm_job_id #13459
pytorch_lightning.callbacks.gpu_stats.GPUStatsMonitor -> #12554
pytorch_lightning.callbacks.gpu_stats.XLAStatsMonitor -> #12688
pytorch_lightning.callbacks.progress.ProgressBar -> #12658
Trainer(max_steps=None) and Trainer.fit_loop.max_steps = None #13591
pytorch_lightning.callbacks.lr_monitor.LearningRateMonitor.lr_sch_names -> #13353
KubeflowEnvironment.is_using_kubeflow, LSFEnvironment.is_using_lsf, TorchElasticEnvironment.is_using_torchelastic #13458
pytorch_lightning.overrides.distributed.IndexBatchSamplerWrapper.batch_indices #13565
pytorch_lightning.strategies.SingleDeviceStrategy.post_dispatch #13461
pytorch_lightning.trainer.connectors.logger_connector.logger_connector.LoggerConnector.gpu_metrics

Feel free to cross-check from the test file to ensure that the relevant test fails now (since it’s no more deprecated and instead removed).

Pitch

All the deprecated features we have are tested here:

https://github.com/PyTorchLightning/pytorch-lightning/blob/master/tests/deprecated_api/test_remove_1-7.py

If you are interested in taking care of one item, post a comment here asking to take it. This avoids multiple people working on the same thing.

Additional context

See pull requests linked in #10312 for examples on how to contribute 😃 Or a recent pull request #12514.

If you enjoy Lightning, check out our other projects! ⚡

Metrics: Machine learning metrics for distributed, scalable PyTorch applications.
Lite: enables pure PyTorch users to scale their existing code on any kind of device while retaining full control over their own loops and optimization logic.
Flash: The fastest way to get a Lightning baseline! A collection of tasks for fast prototyping, baselining, fine-tuning, and solving problems with deep learning.
Bolts: Pretrained SOTA Deep Learning models, callbacks, and more for research and production with PyTorch Lightning and PyTorch.
Lightning Transformers: Flexible interface for high-performance research using SOTA Transformers leveraging Pytorch Lightning, Transformers, and Hydra.