- Added `Accuracy` metric now generalizes to Top-k accuracy for (multi-dimensional) multi-class inputs using the `top_k` parameter ([#4838](https://github.com/PyTorchLightning/pytorch-lightning/pull/4838))
- Added `Accuracy` metric now enables the computation of subset accuracy for multi-label or multi-dimensional multi-class inputs with the `subset_accuracy` parameter ([#4838](https://github.com/PyTorchLightning/pytorch-lightning/pull/4838))
- Added `StatScores` metric to compute the number of true positives, false positives, true negatives and false negatives ([#4839](https://github.com/PyTorchLightning/pytorch-lightning/pull/4839))
- Added `image_gradients` functional metric to compute the image gradients of a given input image. ([#5056](https://github.com/PyTorchLightning/pytorch-lightning/pull/5056))
-`Recall` and `Precision` metrics (and their functional counterparts `recall` and `precision`) can now be generalized to Recall@K and Precision@K with the use of `top_k` parameter ([#4842](https://github.com/PyTorchLightning/pytorch-lightning/pull/4842))
- Changed `stat_scores` metric now calculates stat scores over all classes and gains new parameters, in line with the new `StatScores` metric ([#4839](https://github.com/PyTorchLightning/pytorch-lightning/pull/4839))
- Changed `computer_vision_fine_tunning` example to use `BackboneLambdaFinetuningCallback` ([#5377](https://github.com/PyTorchLightning/pytorch-lightning/pull/5377))
- Set PyTorch 1.4 as min requirements, also for testing and examples `torchvision>=0.5` and `torchtext>=0.5` ([#5418](https://github.com/PyTorchLightning/pytorch-lightning/pull/5418))
- Changed the default value for the `progress_bar_refresh_rate` Trainer argument in Google COLAB notebooks to 20 ([#5516](https://github.com/PyTorchLightning/pytorch-lightning/pull/5516))
- Made `LightningModule.global_rank`, `LightningModule.local_rank` and `LightningModule.logger` read-only properties ([#5730](https://github.com/PyTorchLightning/pytorch-lightning/pull/5730))
- Forced `ModelCheckpoint` callbacks to run after all others to guarantee all states are saved to the checkpoint ([#5731](https://github.com/PyTorchLightning/pytorch-lightning/pull/5731))
* Added RPC and Sharded plugins ([#5732](https://github.com/PyTorchLightning/pytorch-lightning/pull/5732))
* Added missing `LightningModule`-wrapper logic to new plugins and accelerator ([#5734](https://github.com/PyTorchLightning/pytorch-lightning/pull/5734))
- Function `stat_scores_multiple_classes` is deprecated in favor of `stat_scores` ([#4839](https://github.com/PyTorchLightning/pytorch-lightning/pull/4839))
- Deprecated `LightningDistributedDataParallel` in favor of new wrapper module `LightningDistributedModule` ([#5185](https://github.com/PyTorchLightning/pytorch-lightning/pull/5185))
- Deprecated `LightningDataParallel` in favor of new wrapper module `LightningParallelModule` ([#5670](https://github.com/PyTorchLightning/pytorch-lightning/pull/5670))
- Fixed `DDPHPCAccelerator` hangs in DDP construction by calling `init_device` ([#5157](https://github.com/PyTorchLightning/pytorch-lightning/pull/5157))
- Fixed a race condition in `ModelCheckpoint` when checking if a checkpoint file exists ([#5144](https://github.com/PyTorchLightning/pytorch-lightning/pull/5144))
- Fixed duplicate logs appearing in console when using the python logging module ([#5509](https://github.com/PyTorchLightning/pytorch-lightning/pull/5509))
- Fixed tensor printing in `trainer.test()` ([#5138](https://github.com/PyTorchLightning/pytorch-lightning/pull/5138))
- Fixed not using dataloader when `hparams` present ([#4559](https://github.com/PyTorchLightning/pytorch-lightning/pull/4559))
- Added a check for optimizer attached to `lr_scheduler` ([#5338](https://github.com/PyTorchLightning/pytorch-lightning/pull/5338))
- Added support for passing non-existing filepaths to `resume_from_checkpoint` ([#4402](https://github.com/PyTorchLightning/pytorch-lightning/pull/4402))
- Add a notebook example to reach a quick baseline of ~94% accuracy on CIFAR10 using Resnet in Lightning ([#4818](https://github.com/PyTorchLightning/pytorch-lightning/pull/4818))
- Fixed `DDPHPCAccelerator` hangs in DDP construction by calling `init_device` ([#5157](https://github.com/PyTorchLightning/pytorch-lightning/pull/5157))
- Added global step indexing to the checkpoint name for a better sub-epoch checkpointing experience ([#3807](https://github.com/PyTorchLightning/pytorch-lightning/pull/3807))
- Added printing of total num of params, trainable and non-trainable params in ModelSummary ([#4521](https://github.com/PyTorchLightning/pytorch-lightning/pull/4521))
- Added `all_gather` method to `LightningModule` which allows gradient based tensor synchronizations for use-cases such as negative sampling. ([#5012](https://github.com/PyTorchLightning/pytorch-lightning/pull/5012))
- Enabled `self.log` in most functions ([#4969](https://github.com/PyTorchLightning/pytorch-lightning/pull/4969))
- Added changeable extension variable for `ModelCheckpoint` ([#4977](https://github.com/PyTorchLightning/pytorch-lightning/pull/4977))
-`WandbLogger` does not force wandb `reinit` arg to True anymore and creates a run only when needed ([#4648](https://github.com/PyTorchLightning/pytorch-lightning/pull/4648))
- Changed `automatic_optimization` to be a model attribute ([#4602](https://github.com/PyTorchLightning/pytorch-lightning/pull/4602))
- Changed `Simple Profiler` report to order by percentage time spent + num calls ([#4880](https://github.com/PyTorchLightning/pytorch-lightning/pull/4880))
- Deprecated the old way of assigning hyper-parameters through `self.hparams = ...` ([#4813](https://github.com/PyTorchLightning/pytorch-lightning/pull/4813))
- Deprecated `mode='auto'` from `ModelCheckpoint` and `EarlyStopping` ([#4695](https://github.com/PyTorchLightning/pytorch-lightning/pull/4695))
- Removed `multiclass_roc` and `multiclass_precision_recall_curve`, use `roc` and `precision_recall_curve` instead ([#4549](https://github.com/PyTorchLightning/pytorch-lightning/pull/4549))
- Added casting to python types for numpy scalars when logging `hparams` ([#4647](https://github.com/PyTorchLightning/pytorch-lightning/pull/4647))
- Added warning when progress bar refresh rate is less than 20 on Google Colab to prevent crashing ([#4654](https://github.com/PyTorchLightning/pytorch-lightning/pull/4654))
- Added `F1` class metric ([#4656](https://github.com/PyTorchLightning/pytorch-lightning/pull/4656))
- Consistently use `step=trainer.global_step` in `LearningRateMonitor` independently of `logging_interval` ([#4376](https://github.com/PyTorchLightning/pytorch-lightning/pull/4376))
- Metric states are no longer as default added to `state_dict` ([#4685](https://github.com/PyTorchLightning/pytorch-lightning/pull/4685))
- Renamed class metric `Fbeta` >> `FBeta` ([#4656](https://github.com/PyTorchLightning/pytorch-lightning/pull/4656))
- Model summary: add 1 decimal place ([#4745](https://github.com/PyTorchLightning/pytorch-lightning/pull/4745))
- Do not override `PYTHONWARNINGS` ([#4700](https://github.com/PyTorchLightning/pytorch-lightning/pull/4700))
- Fixed incomplete progress bars when total batches not divisible by refresh rate ([#4577](https://github.com/PyTorchLightning/pytorch-lightning/pull/4577))
- Fixed batch_arg_name - add `batch_arg_name` to all calls to `_adjust_batch_size`bug ([#4812](https://github.com/PyTorchLightning/pytorch-lightning/pull/4812))
- Fixed `torchtext` data to GPU ([#4785](https://github.com/PyTorchLightning/pytorch-lightning/pull/4785))
- Fixed a crash bug in MLFlow logger ([#4716](https://github.com/PyTorchLightning/pytorch-lightning/pull/4716))
- LoggerConnector log_metrics will use `total_batch_idx` instead of `global_step` when logging on `training step` ([#4738](https://github.com/PyTorchLightning/pytorch-lightning/pull/4738))
- Prevent crash if `sync_dist=True` on CPU ([#4626](https://github.com/PyTorchLightning/pytorch-lightning/pull/4626))
- Fixed average pbar Metrics ([#4534](https://github.com/PyTorchLightning/pytorch-lightning/pull/4534))
- Fixed `setup` callback hook to correctly pass the LightningModule through ([#4608](https://github.com/PyTorchLightning/pytorch-lightning/pull/4608))
- Allowing decorate model init with saving `hparams` inside ([#4662](https://github.com/PyTorchLightning/pytorch-lightning/pull/4662))
- Fixed `split_idx` set by `LoggerConnector` in `on_trainer_init` to `Trainer` ([#4697](https://github.com/PyTorchLightning/pytorch-lightning/pull/4697))
- Added `manual_optimizer_step` which work with `AMP Native` and `accumulated_grad_batches` ([#4485](https://github.com/PyTorchLightning/pytorch-lightning/pull/4485))
- Added `persistent(mode)` method to metrics, to enable and disable metric states being added to `state_dict` ([#4482](https://github.com/PyTorchLightning/pytorch-lightning/pull/4482))
- Added congratulations at the end of our notebooks ([#4555](https://github.com/PyTorchLightning/pytorch-lightning/pull/4555))
- Fixed `lightning_getattr`, `lightning_hasattr` not finding the correct attributes in datamodule ([#4347](https://github.com/PyTorchLightning/pytorch-lightning/pull/4347))
- Fixed automatic optimization AMP by `manual_optimization_step` ([#4485](https://github.com/PyTorchLightning/pytorch-lightning/pull/4485))
- Replace `MisconfigurationException` with warning in `ModelCheckpoint` Callback ([#4560](https://github.com/PyTorchLightning/pytorch-lightning/pull/4560))
- Fixed logged keys in mlflow logger ([#4412](https://github.com/PyTorchLightning/pytorch-lightning/pull/4412))
- Fixed `is_picklable` by catching `AttributeError` ([#4508](https://github.com/PyTorchLightning/pytorch-lightning/pull/4508))
- Added PyTorch 1.7 Stable support ([#3821](https://github.com/PyTorchLightning/pytorch-lightning/pull/3821))
- Added timeout for `tpu_device_exists` to ensure process does not hang indefinitely ([#4340](https://github.com/PyTorchLightning/pytorch-lightning/pull/4340))
### Changed
- W&B log in sync with `Trainer` step ([#4405](https://github.com/PyTorchLightning/pytorch-lightning/pull/4405))
- Hook `on_after_backward` is called only when `optimizer_step` is being called ([#4439](https://github.com/PyTorchLightning/pytorch-lightning/pull/4439))
- Moved `track_and_norm_grad` into `training loop` and called only when `optimizer_step` is being called ([#4439](https://github.com/PyTorchLightning/pytorch-lightning/pull/4439))
- Fixed `WandbLogger` not uploading checkpoint artifacts at the end of training ([#4341](https://github.com/PyTorchLightning/pytorch-lightning/pull/4341))
- Fixed `accumulation across batches` has completed `before breaking training loop` ([#4278](https://github.com/PyTorchLightning/pytorch-lightning/pull/4278))
- Fixed `ModelCheckpoint` don't increase current_epoch and global_step when not training ([#4291](https://github.com/PyTorchLightning/pytorch-lightning/pull/4291))
- Fixed `COMET_EXPERIMENT_KEY` environment variable usage in comet logger ([#4230](https://github.com/PyTorchLightning/pytorch-lightning/pull/4230))
- Fixed `hparams` saving - save the state when `save_hyperparameters()` is called [in `__init__`] ([#4163](https://github.com/PyTorchLightning/pytorch-lightning/pull/4163))
- Fixed `current_epoch` property update to reflect true epoch number inside `LightningDataModule`, when `reload_dataloaders_every_epoch=True`. ([#3974](https://github.com/PyTorchLightning/pytorch-lightning/pull/3974))
- Fixed mismatch between docstring and code regarding when `on_load_checkpoint` hook is called ([#3996](https://github.com/PyTorchLightning/pytorch-lightning/pull/3996))
- Added `LightningModule.to_torchscript` to support exporting as `ScriptModule` ([#3258](https://github.com/PyTorchLightning/pytorch-lightning/pull/3258))
* remove obscure forward call in eval + CPU backend `___step` ([#3123](https://github.com/PyTorchLightning/pytorch-lightning/pull/3123))
* reduced all simplified forward ([#3126](https://github.com/PyTorchLightning/pytorch-lightning/pull/3126))
* added hook base method ([#3127](https://github.com/PyTorchLightning/pytorch-lightning/pull/3127))
* refactor eval loop to use hooks - use `test_mode` for if so we can split later ([#3129](https://github.com/PyTorchLightning/pytorch-lightning/pull/3129))
* moved `___step_end` hooks ([#3130](https://github.com/PyTorchLightning/pytorch-lightning/pull/3130))
* training forward refactor ([#3134](https://github.com/PyTorchLightning/pytorch-lightning/pull/3134))
* training AMP scaling refactor ([#3135](https://github.com/PyTorchLightning/pytorch-lightning/pull/3135))
* epoch can now log independently ([#3843](https://github.com/PyTorchLightning/pytorch-lightning/pull/3843))
* test selecting the correct backend. temp backends while slurm and TorchElastic are decoupled ([#3848](https://github.com/PyTorchLightning/pytorch-lightning/pull/3848))
- Changed defaults of `save_top_k` and `save_last` to `None` in ModelCheckpoint ([#3680](https://github.com/PyTorchLightning/pytorch-lightning/pull/3680))
-`row_log_interval` and `log_save_interval` are now based on training loop's `global_step` instead of epoch-internal batch index ([#3667](https://github.com/PyTorchLightning/pytorch-lightning/pull/3667))
- Deprecated `TrainResult` and `EvalResult`, use `self.log` and `self.write` from the `LightningModule` to log metrics and write predictions. `training_step` can now only return a scalar (for the loss) or a dictionary with anything you want. ([#3681](https://github.com/PyTorchLightning/pytorch-lightning/pull/3681))
* Added hooks to metric module interface ([#2528](https://github.com/PyTorchLightning/pytorch-lightning/pull/2528))
* Added error when AUROC metric is used for multiclass problems ([#3350](https://github.com/PyTorchLightning/pytorch-lightning/pull/3350))
* Fixed `ModelCheckpoint` with `save_top_k=-1` option not tracking the best models when a monitor metric is available ([#3735](https://github.com/PyTorchLightning/pytorch-lightning/pull/3735))
* Fixed counter-intuitive error being thrown in `Accuracy` metric for zero target tensor ([#3764](https://github.com/PyTorchLightning/pytorch-lightning/pull/3764))
* Fixed aggregation of metrics ([#3517](https://github.com/PyTorchLightning/pytorch-lightning/pull/3517))
- Fixed setting batch size in `LightningModule.datamodule` when using `auto_scale_batch_size` ([#3266](https://github.com/PyTorchLightning/pytorch-lightning/pull/3266))
- Fixed getting `experiment_id` from MLFlow only once instead of each training loop ([#3394](https://github.com/PyTorchLightning/pytorch-lightning/pull/3394))
- Fixed `overfit_batches` which now correctly disables shuffling for the training loader. ([#3501](https://github.com/PyTorchLightning/pytorch-lightning/pull/3501))
- Fixed example implementation of AutoEncoder ([#3190](https://github.com/PyTorchLightning/pytorch-lightning/pull/3190))
- Fixed invalid paths when remote logging with TensorBoard ([#3236](https://github.com/PyTorchLightning/pytorch-lightning/pull/3236))
- Fixed change `t()` to `transpose()` as XLA devices do not support `.t()` on 1-dim tensor ([#3252](https://github.com/PyTorchLightning/pytorch-lightning/pull/3252))
- Fixed (weights only) checkpoints loading without PL ([#3287](https://github.com/PyTorchLightning/pytorch-lightning/pull/3287))
- Fixed `gather_all_tensors` cross GPUs in DDP ([#3319](https://github.com/PyTorchLightning/pytorch-lightning/pull/3319))
- Fixed CometML save dir ([#3419](https://github.com/PyTorchLightning/pytorch-lightning/pull/3419))
- Fixed normalize mode at confusion matrix (replace NaNs with zeros) ([#3465](https://github.com/PyTorchLightning/pytorch-lightning/pull/3465))
- Fixed global step increment in training loop when `training_epoch_end` hook is used ([#3673](https://github.com/PyTorchLightning/pytorch-lightning/pull/3673))
- Fixed dataloader shuffling not getting turned off with `overfit_batches > 0` and `distributed_backend = "ddp"` ([#3534](https://github.com/PyTorchLightning/pytorch-lightning/pull/3534))
- Fixed determinism in `DDPSpawnBackend` when using `seed_everything` in main process ([#3335](https://github.com/PyTorchLightning/pytorch-lightning/pull/3335))
- Fixed `current_epoch` and `global_step` properties mismatch between `Trainer` and `LightningModule` ([#3785](https://github.com/PyTorchLightning/pytorch-lightning/pull/3785))
- Fixed `tbptt_reduce_fx` when non-floating tensors are logged ([#3796](https://github.com/PyTorchLightning/pytorch-lightning/pull/3796))
- Fixed model checkpoint frequency ([#3852](https://github.com/PyTorchLightning/pytorch-lightning/pull/3852))
- Fixed logging non-tensor scalar with result breaks subsequent epoch aggregation ([#3855](https://github.com/PyTorchLightning/pytorch-lightning/pull/3855))
- Fixed `TrainerEvaluationLoopMixin` activates `model.train()` at the end ([#3858](https://github.com/PyTorchLightning/pytorch-lightning/pull/3858))
- Fixed `overfit_batches` when using with multiple val/test_dataloaders ([#3857](https://github.com/PyTorchLightning/pytorch-lightning/pull/3857))
- Fixed enables `training_step` to return `None` ([#3862](https://github.com/PyTorchLightning/pytorch-lightning/pull/3862))
- Fixed init nan for checkpointing ([#3863](https://github.com/PyTorchLightning/pytorch-lightning/pull/3863))
- Fixed for `load_from_checkpoint` ([#2776](https://github.com/PyTorchLightning/pytorch-lightning/pull/2776))
- Fixes incorrect `batch_sizes` when Dataloader returns a dict with multiple tensors ([#3668](https://github.com/PyTorchLightning/pytorch-lightning/pull/3668))
- Fixed unexpected signature for `validation_step` ([#3947](https://github.com/PyTorchLightning/pytorch-lightning/pull/3947))
- Added support for `Trainer(num_sanity_val_steps=-1)` to check all validation data before training ([#2246](https://github.com/PyTorchLightning/pytorch-lightning/pull/2246))
- Added support for `limit_{mode}_batches (int)` to work with infinite dataloader (IterableDataset) ([#2840](https://github.com/PyTorchLightning/pytorch-lightning/pull/2840))
- Deprecated Trainer attribute `ckpt_path`, which will now be set by `weights_save_path` ([#2681](https://github.com/PyTorchLightning/pytorch-lightning/pull/2681))
- Fixed `save_dir` in loggers getting ignored by default value of `weights_save_path` when user did not specify `weights_save_path` ([#2681](https://github.com/PyTorchLightning/pytorch-lightning/pull/2681))
- Fixed `weights_save_path` getting ignored when `logger=False` is passed to Trainer ([#2681](https://github.com/PyTorchLightning/pytorch-lightning/pull/2681))
- Fixed data transfer to device when using `torchtext.data.Field` and `include_lengths is True` ([#2689](https://github.com/PyTorchLightning/pytorch-lightning/pull/2689))
- Fixed loss value in the progress bar is wrong when `accumulate_grad_batches > 1` ([#2738](https://github.com/PyTorchLightning/pytorch-lightning/pull/2738))
- Fixed correct CWD for ddp sub-processes when using Hydra ([#2719](https://github.com/PyTorchLightning/pytorch-lightning/pull/2719))
- Fixed `ModelCheckpoint` not saving the latest information when `save_last=True` ([#2881](https://github.com/PyTorchLightning/pytorch-lightning/pull/2881))
- Fixed ImageNet example: learning rate scheduler, number of workers and batch size when using DDP ([#2889](https://github.com/PyTorchLightning/pytorch-lightning/pull/2889))
- Fixed a model loading issue with inheritance and variable positional arguments ([#2911](https://github.com/PyTorchLightning/pytorch-lightning/pull/2911))
- Fixed passing `non_blocking=True` when transferring a batch object that does not support it ([#2910](https://github.com/PyTorchLightning/pytorch-lightning/pull/2910))
- Fixed batch size auto-scaling feature to set the new value on the correct model attribute ([#3043](https://github.com/PyTorchLightning/pytorch-lightning/pull/3043))
- Fixed using the same DDP python interpreter and actually running ([#2482](https://github.com/PyTorchLightning/pytorch-lightning/pull/2482))
- Fixed model summary input type conversion for models that have input dtype different from model parameters ([#2510](https://github.com/PyTorchLightning/pytorch-lightning/pull/2510))
- Made `TensorBoardLogger` and `CometLogger` pickleable ([#2518](https://github.com/PyTorchLightning/pytorch-lightning/pull/2518))
- Fixed Apex scaling with decoupled backward ([#2433](https://github.com/PyTorchLightning/pytorch-lightning/pull/2433))
- Fixed crashing or wrong displaying progressbar because of missing ipywidgets ([#2417](https://github.com/PyTorchLightning/pytorch-lightning/pull/2417))
- Fixed TPU saving dir ([fc26078e](https://github.com/PyTorchLightning/pytorch-lightning/commit/fc26078e395f8a001f4c6dd7b3fe7ca202f914a3), [04e68f02](https://github.com/PyTorchLightning/pytorch-lightning/commit/04e68f022fc03dd5f1555ee86dea997d42a448ad))
- Fixed logging on rank 0 only ([#2425](https://github.com/PyTorchLightning/pytorch-lightning/pull/2425))
- Fixed number batches in case of multiple dataloaders and `limit_{*}_batches` ([#1920](https://github.com/PyTorchLightning/pytorch-lightning/pull/1920),
- Fix for `load_from_checkpoint()` not working with absolute path on Windows ([#2294](https://github.com/PyTorchLightning/pytorch-lightning/pull/2294))
- Fixed an issue how _has_len handles `NotImplementedError` e.g. raised by `torchtext.data.Iterator` ([#2293](https://github.com/PyTorchLightning/pytorch-lightning/pull/2293)), ([#2307](https://github.com/PyTorchLightning/pytorch-lightning/pull/2307))
- Added `overfit_batches`, `limit_{val|test}_batches` flags (overfit now uses training set for all three) ([#2213](https://github.com/PyTorchLightning/pytorch-lightning/pull/2213))
- Added type hints in `Trainer.fit()` and `Trainer.test()` to reflect that also a list of dataloaders can be passed in ([#1723](https://github.com/PyTorchLightning/pytorch-lightning/pull/1723))
- Allow dataloaders without sampler field present ([#1907](https://github.com/PyTorchLightning/pytorch-lightning/pull/1907))
- Added option `save_last` to save the model at the end of every epoch in `ModelCheckpoint` [(#1908)](https://github.com/PyTorchLightning/pytorch-lightning/pull/1908)
- Early stopping checks `on_validation_end` ([#1458](https://github.com/PyTorchLightning/pytorch-lightning/pull/1458))
- Attribute `best_model_path` to `ModelCheckpoint` for storing and later retrieving the path to the best saved model file ([#1799](https://github.com/PyTorchLightning/pytorch-lightning/pull/1799))
- Added a model hook `transfer_batch_to_device` that enables moving custom data structures to the target device ([1756](https://github.com/PyTorchLightning/pytorch-lightning/pull/1756))
- Added [black](https://black.readthedocs.io/en/stable/) formatter for the code with code-checker on pull ([1610](https://github.com/PyTorchLightning/pytorch-lightning/pull/1610))
- Added a callback method `on_keyboard_interrupt` for handling KeyboardInterrupt events during training ([#2134](https://github.com/PyTorchLightning/pytorch-lightning/pull/2134))
- Added a decorator `auto_move_data` that moves data to the correct device when using the LightningModule for inference ([#1905](https://github.com/PyTorchLightning/pytorch-lightning/pull/1905))
- Renamed `ModelCheckpoint`'s attributes `best` to `best_model_score` and `kth_best_model` to `kth_best_model_path` ([#1799](https://github.com/PyTorchLightning/pytorch-lightning/pull/1799))
- Changed the default value of the Trainer argument `weights_summary` from `full` to `top` ([#2029](https://github.com/PyTorchLightning/pytorch-lightning/pull/2029))
- Enabled `prepare_data` from correct processes - clarify local vs global rank ([#2166](https://github.com/PyTorchLightning/pytorch-lightning/pull/2166))
- Removed unintended Trainer argument `progress_bar_callback`, the callback should be passed in by `Trainer(callbacks=[...])` instead ([#1855](https://github.com/PyTorchLightning/pytorch-lightning/pull/1855))
- Fixed user warning when apex was used together with learning rate schedulers ([#1873](https://github.com/PyTorchLightning/pytorch-lightning/pull/1873))
- Fixed an issue with `Trainer.from_argparse_args` when passing in unknown Trainer args ([#1932](https://github.com/PyTorchLightning/pytorch-lightning/pull/1932))
- Fixed bug related to logger not being reset correctly for model after tuner algorithms ([#1933](https://github.com/PyTorchLightning/pytorch-lightning/pull/1933))
- Fixed an issue with `_auto_collect_arguments` collecting local variables that are not constructor arguments and not working for signatures that have the instance not named `self` ([#2048](https://github.com/PyTorchLightning/pytorch-lightning/pull/2048))
- Fixed an issue with the model summary and `example_input_array` depending on a specific ordering of the submodules in a LightningModule ([#1773](https://github.com/PyTorchLightning/pytorch-lightning/pull/1773))
- Added transfer learning example (for a binary classification task in computer vision) ([#1564](https://github.com/PyTorchLightning/pytorch-lightning/pull/1564))
- Added type hints in `Trainer.fit()` and `Trainer.test()` to reflect that also a list of dataloaders can be passed in ([#1723](https://github.com/PyTorchLightning/pytorch-lightning/pull/1723)).
- Added option to provide seed to random generators to ensure reproducibility ([#1572](https://github.com/PyTorchLightning/pytorch-lightning/pull/1572))
- Don't convert `namedtuple` to `tuple` when transferring the batch to target device ([#1589](https://github.com/PyTorchLightning/pytorch-lightning/pull/1589))
- Allow passing hparams as keyword argument to LightningModule when loading from checkpoint ([#1639](https://github.com/PyTorchLightning/pytorch-lightning/pull/1639))
- Fixed bugs that prevent lr finder to be used together with early stopping and validation dataloaders ([#1676](https://github.com/PyTorchLightning/pytorch-lightning/pull/1676))
- Fixed a bug in Trainer that prepended the checkpoint path with `version_` when it shouldn't ([#1748](https://github.com/PyTorchLightning/pytorch-lightning/pull/1748))
- Fixed accumulation parameter and suggestion method for learning rate finder ([#1801](https://github.com/PyTorchLightning/pytorch-lightning/pull/1801))
- Fixed num processes wasn't being set properly and auto sampler was ddp failing ([#1819](https://github.com/PyTorchLightning/pytorch-lightning/pull/1819))
- Fixed bugs in semantic segmentation example ([#1824](https://github.com/PyTorchLightning/pytorch-lightning/pull/1824))
- Added flag `replace_sampler_ddp` to manually disable sampler replacement in DDP ([#1513](https://github.com/PyTorchLightning/pytorch-lightning/pull/1513))
- Added `auto_select_gpus` flag to trainer that enables automatic selection of available GPUs on exclusive mode systems.
- Added learning rate finder ([#1347](https://github.com/PyTorchLightning/pytorch-lightning/pull/1347))
- Added support for ddp mode in clusters without SLURM ([#1387](https://github.com/PyTorchLightning/pytorch-lightning/pull/1387))
- Added `test_dataloaders` parameter to `Trainer.test()` ([#1434](https://github.com/PyTorchLightning/pytorch-lightning/pull/1434))
- Added `terminate_on_nan` flag to trainer that performs a NaN check with each training iteration when set to `True` ([#1475](https://github.com/PyTorchLightning/pytorch-lightning/pull/1475))
- Added `terminate_on_nan` flag to trainer that performs a NaN check with each training iteration when set to `True`. ([#1475](https://github.com/PyTorchLightning/pytorch-lightning/pull/1475))
- Added [Horovod](http://horovod.ai) support as a distributed backend `Trainer(distributed_backend='horovod')` ([#1529](https://github.com/PyTorchLightning/pytorch-lightning/pull/1529))
- Changed the default behaviour to no longer include a NaN check with each training iteration. ([#1475](https://github.com/PyTorchLightning/pytorch-lightning/pull/1475))
- Decoupled the progress bar from trainer` it is a callback now and can be customized or even be replaced entirely ([#1450](https://github.com/PyTorchLightning/pytorch-lightning/pull/1450)).
- Changed lr schedule step interval behavior to update every backwards pass instead of every forwards pass ([#1477](https://github.com/PyTorchLightning/pytorch-lightning/pull/1477))
- Fixed loggers - flushing last logged metrics even before continue, e.g. `trainer.test()` results ([#1459](https://github.com/PyTorchLightning/pytorch-lightning/pull/1459))
- Fixed a bug that caused the `callbacks` Trainer argument to reference a global variable ([#1534](https://github.com/PyTorchLightning/pytorch-lightning/pull/1534)).
- Fixed a bug that set all boolean CLI arguments from `Trainer.add_argparse_args` always to True ([#1571](https://github.com/PyTorchLightning/pytorch-lightning/pull/1571))
- Added support for `IterableDataset` in validation and testing ([#1104](https://github.com/PyTorchLightning/pytorch-lightning/pull/1104))
- Added support for non-primitive types in `hparams` for `TensorboardLogger` ([#1130](https://github.com/PyTorchLightning/pytorch-lightning/pull/1130))
- Added a check that stops the training when loss or weights contain `NaN` or `inf` values. ([#1097](https://github.com/PyTorchLightning/pytorch-lightning/pull/1097))
- Added support for `IterableDataset` when `val_check_interval=1.0` (default), this will trigger validation at the end of each epoch. ([#1283](https://github.com/PyTorchLightning/pytorch-lightning/pull/1283))
- Added `summary` method to Profilers. ([#1259](https://github.com/PyTorchLightning/pytorch-lightning/pull/1259))
- Added informative errors if user defined dataloader has zero length ([#1280](https://github.com/PyTorchLightning/pytorch-lightning/pull/1280))
- Added a `training_epoch_end` method which is the mirror of `validation_epoch_end`. ([#1357](https://github.com/PyTorchLightning/pytorch-lightning/pull/1357))
- Added model configuration checking ([#1199](https://github.com/PyTorchLightning/pytorch-lightning/pull/1199))
- Added support for optimizer frequencies through `LightningModule.configure_optimizers()` ([#1269](https://github.com/PyTorchLightning/pytorch-lightning/pull/1269))
- Added option to run without an optimizer by returning `None` from `configure_optimizers`. ([#1279](https://github.com/PyTorchLightning/pytorch-lightning/pull/1279))
- Changed `progress_bar_refresh_rate` trainer flag to disable progress bar when set to 0. ([#1108](https://github.com/PyTorchLightning/pytorch-lightning/pull/1108))
- Updated references to `self.forward()` to instead use the `__call__` interface. ([#1211](https://github.com/PyTorchLightning/pytorch-lightning/pull/1211))
- Changed default behaviour of `configure_optimizers` to use no optimizer rather than Adam. ([#1279](https://github.com/PyTorchLightning/pytorch-lightning/pull/1279))
- Did not always create a DataLoader during reinstantiation, but the same type as before (if subclass of DataLoader) ([#1346](https://github.com/PyTorchLightning/pytorch-lightning/pull/1346))
- Did not interfere with a default sampler ([#1318](https://github.com/PyTorchLightning/pytorch-lightning/pull/1318))
- Changed min max gpu memory to be on their own plots ([#1358](https://github.com/PyTorchLightning/pytorch-lightning/pull/1358))
- Remove `.item` which causes sync issues ([#1254](https://github.com/PyTorchLightning/pytorch-lightning/pull/1254))
- Changed smoothing in TQDM to decrease variability of time remaining between training / eval ([#1194](https://github.com/PyTorchLightning/pytorch-lightning/pull/1194))
- Change default logger to dedicated one ([#1064](https://github.com/PyTorchLightning/pytorch-lightning/pull/1064))
- Dropped `torchvision` dependency in tests and added own MNIST dataset class instead ([#986](https://github.com/PyTorchLightning/pytorch-lightning/pull/986))
- Fixed `model_checkpoint` when saving all models ([#1359](https://github.com/PyTorchLightning/pytorch-lightning/pull/1359))
-`Trainer.add_argparse_args` classmethod fixed. Now it adds a type for the arguments ([#1147](https://github.com/PyTorchLightning/pytorch-lightning/pull/1147))
- Fixed a bug that created an extra dataloader with active `reload_dataloaders_every_epoch` ([#1196](https://github.com/PyTorchLightning/pytorch-lightning/pull/1196))
- Fixed an issue with early stopping that would prevent it from monitoring training metrics when validation is disabled / not implemented ([#1235](https://github.com/PyTorchLightning/pytorch-lightning/pull/1235)).
- Fixed a bug that would cause `trainer.test()` to run on the validation set when overloading `validation_epoch_end` and `test_end` ([#1353](https://github.com/PyTorchLightning/pytorch-lightning/pull/1353))
- Fixed `WandbLogger.watch` - use of the watch method without importing `wandb` ([#1311](https://github.com/PyTorchLightning/pytorch-lightning/pull/1311))
- Fixed `WandbLogger` to be used with 'ddp' - allow reinits in sub-processes ([#1149](https://github.com/PyTorchLightning/pytorch-lightning/pull/1149),
- Fixed Tensorboard logger error: lightning_logs directory not exists in multi-node DDP on nodes with rank != 0 ([#1377](https://github.com/PyTorchLightning/pytorch-lightning/pull/1377))
- Added automatic sampler setup. Depending on DDP or TPU, lightning configures the sampler correctly (user needs to do nothing) ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
- Added `reload_dataloaders_every_epoch=False` flag for trainer. Some users require reloading data every epoch ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
- Added `progress_bar_refresh_rate=50` flag for trainer. Throttle refresh rate on notebooks ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
- Added a check to ensure that the metric used for early stopping exists before training commences ([#542](https://github.com/PyTorchLightning/pytorch-lightning/pull/542))
- Added `optimizer_idx` argument to `backward` hook ([#733](https://github.com/PyTorchLightning/pytorch-lightning/pull/733))
- Added `entity` argument to `WandbLogger` to be passed to `wandb.init` ([#783](https://github.com/PyTorchLightning/pytorch-lightning/pull/783))
- Added a tool for profiling training runs ([#782](https://github.com/PyTorchLightning/pytorch-lightning/pull/782))
- Improved flexibility for naming of TensorBoard logs, can now set `version` to a `str` to just save to that directory, and use `name=''` to prevent experiment-name directory ([#804](https://github.com/PyTorchLightning/pytorch-lightning/pull/804))
- Added option to specify `step` key when logging metrics ([#808](https://github.com/PyTorchLightning/pytorch-lightning/pull/808))
- Added `train_dataloader`, `val_dataloader` and `test_dataloader` arguments to `Trainer.fit()`, for alternative data parsing ([#759](https://github.com/PyTorchLightning/pytorch-lightning/pull/759))
- Added Tensor Processing Unit (TPU) support ([#868](https://github.com/PyTorchLightning/pytorch-lightning/pull/868))
- Support for user defined callbacks ([#889](https://github.com/PyTorchLightning/pytorch-lightning/pull/889) and [#950](https://github.com/PyTorchLightning/pytorch-lightning/pull/950))
- Added support for multiple loggers to be passed to `Trainer` as an iterable (e.g. list, tuple, etc.) ([#903](https://github.com/PyTorchLightning/pytorch-lightning/pull/903))
- Improved `NeptuneLogger` by adding `close_after_fit` argument to allow logging after training([#908](https://github.com/PyTorchLightning/pytorch-lightning/pull/1084))
- Changed default TQDM to use `tqdm.auto` for prettier outputs in IPython notebooks ([#752](https://github.com/PyTorchLightning/pytorch-lightning/pull/752))
- Changed `pytorch_lightning.logging` to `pytorch_lightning.loggers` ([#767](https://github.com/PyTorchLightning/pytorch-lightning/pull/767))
- Moved the default `tqdm_dict` definition from Trainer to `LightningModule`, so it can be overridden by the user ([#749](https://github.com/PyTorchLightning/pytorch-lightning/pull/749))
- Moved functionality of `LightningModule.load_from_metrics` into `LightningModule.load_from_checkpoint` ([#995](https://github.com/PyTorchLightning/pytorch-lightning/pull/995))
- Deprecated `LightningModule.load_from_metrics` in favour of `LightningModule.load_from_checkpoint` ([#995](https://github.com/PyTorchLightning/pytorch-lightning/pull/995),
- Fixed a bug where early stopping `on_end_epoch` would be called inconsistently when `check_val_every_n_epoch == 0` ([#743](https://github.com/PyTorchLightning/pytorch-lightning/pull/743))
- Fixed a bug where the model checkpointer didn't write to the same directory as the logger ([#771](https://github.com/PyTorchLightning/pytorch-lightning/pull/771))
- Fixed a bug where the `TensorBoardLogger` class would create an additional empty log file during fitting ([#777](https://github.com/PyTorchLightning/pytorch-lightning/pull/777))
- Fixed a bug where `global_step` was advanced incorrectly when using `accumulate_grad_batches > 1` ([#832](https://github.com/PyTorchLightning/pytorch-lightning/pull/832))
- Fixed a bug when calling `self.logger.experiment` with multiple loggers ([#1009](https://github.com/PyTorchLightning/pytorch-lightning/pull/1009))
- Fixed a bug when calling `logger.append_tags` on a `NeptuneLogger` with a single tag ([#1009](https://github.com/PyTorchLightning/pytorch-lightning/pull/1009))
- Fixed sending back data from `.spawn` by saving and loading the trained model in/out of the process ([#1017](https://github.com/PyTorchLightning/pytorch-lightning/pull/1017)
- Fixed port collision on DDP ([#1010](https://github.com/PyTorchLightning/pytorch-lightning/pull/1010))
- Added support for resuming from a specific checkpoint via `resume_from_checkpoint` argument ([#516](https://github.com/PyTorchLightning/pytorch-lightning/pull/516))
- Added support for `ReduceLROnPlateau` scheduler ([#320](https://github.com/PyTorchLightning/pytorch-lightning/pull/320))
- Added support for Apex mode `O2` in conjunction with Data Parallel ([#493](https://github.com/PyTorchLightning/pytorch-lightning/pull/493))
- Added option (`save_top_k`) to save the top k models in the `ModelCheckpoint` class ([#128](https://github.com/PyTorchLightning/pytorch-lightning/pull/128))
- Added `on_train_start` and `on_train_end` hooks to `ModelHooks` ([#598](https://github.com/PyTorchLightning/pytorch-lightning/pull/598))
- Added support for weight summary of model with multiple inputs ([#543](https://github.com/PyTorchLightning/pytorch-lightning/pull/543))
- Added `map_location` argument to `load_from_metrics` and `load_from_checkpoint` ([#625](https://github.com/PyTorchLightning/pytorch-lightning/pull/625))
- Added option to disable validation by setting `val_percent_check=0` ([#649](https://github.com/PyTorchLightning/pytorch-lightning/pull/649))
- Added `NeptuneLogger` class ([#648](https://github.com/PyTorchLightning/pytorch-lightning/pull/648))
- Added `WandbLogger` class ([#627](https://github.com/PyTorchLightning/pytorch-lightning/pull/627))
- Changed the default progress bar to print to stdout instead of stderr ([#531](https://github.com/PyTorchLightning/pytorch-lightning/pull/531))
- Renamed `step_idx` to `step`, `epoch_idx` to `epoch`, `max_num_epochs` to `max_epochs` and `min_num_epochs` to `min_epochs` ([#589](https://github.com/PyTorchLightning/pytorch-lightning/pull/589))
- Renamed `total_batch_nb` to `total_batches`, `nb_val_batches` to `num_val_batches`, `nb_training_batches` to `num_training_batches`, `max_nb_epochs` to `max_epochs`, `min_nb_epochs` to `min_epochs`, `nb_test_batches` to `num_test_batches`, and `nb_val_batches` to `num_val_batches` ([#567](https://github.com/PyTorchLightning/pytorch-lightning/pull/567))
- Changed gradient logging to use parameter names instead of indexes ([#660](https://github.com/PyTorchLightning/pytorch-lightning/pull/660))
- Changed the default logger to `TensorBoardLogger` ([#609](https://github.com/PyTorchLightning/pytorch-lightning/pull/609))
- Changed the directory for tensorboard logging to be the same as model checkpointing ([#706](https://github.com/PyTorchLightning/pytorch-lightning/pull/706))
- Removed the `save_best_only` argument from `ModelCheckpoint`, use `save_top_k=1` instead ([#128](https://github.com/PyTorchLightning/pytorch-lightning/pull/128))
- Fixed a bug which ocurred when using Adagrad with cuda ([#554](https://github.com/PyTorchLightning/pytorch-lightning/pull/554))
- Fixed a bug where training would be on the GPU despite setting `gpus=0` or `gpus=[]` ([#561](https://github.com/PyTorchLightning/pytorch-lightning/pull/561))
- Fixed an error with `print_nan_gradients` when some parameters do not require gradient ([#579](https://github.com/PyTorchLightning/pytorch-lightning/pull/579))
- Fixed a bug where the progress bar would show an incorrect number of total steps during the validation sanity check when using multiple validation data loaders ([#597](https://github.com/PyTorchLightning/pytorch-lightning/pull/597))
- Fixed support for PyTorch 1.1.0 ([#552](https://github.com/PyTorchLightning/pytorch-lightning/pull/552))
- Fixed an issue with early stopping when using a `val_check_interval < 1.0` in `Trainer` ([#492](https://github.com/PyTorchLightning/pytorch-lightning/pull/492))
- Fixed bugs relating to the `CometLogger` object that would cause it to not work properly ([#481](https://github.com/PyTorchLightning/pytorch-lightning/pull/481))
- Fixed a bug that would occur when returning `-1` from `on_batch_start` following an early exit or when the batch was `None` ([#509](https://github.com/PyTorchLightning/pytorch-lightning/pull/509))
- Fixed a potential race condition with several processes trying to create checkpoint directories ([#530](https://github.com/PyTorchLightning/pytorch-lightning/pull/530))
- Fixed a bug where batch 'segments' would remain on the GPU when using `truncated_bptt > 1` ([#532](https://github.com/PyTorchLightning/pytorch-lightning/pull/532))
- Fixed a bug when using `IterableDataset` ([#547](https://github.com/PyTorchLightning/pytorch-lightning/pull/547))
- Fixed a bug where `.item` was called on non-tensor objects ([#602](https://github.com/PyTorchLightning/pytorch-lightning/pull/602))
- Fixed a bug where `Trainer.train` would crash on an uninitialized variable if the trainer was run after resuming from a checkpoint that was already at `max_epochs` ([#608](https://github.com/PyTorchLightning/pytorch-lightning/pull/608))
- Fixed a bug where early stopping would begin two epochs early ([#617](https://github.com/PyTorchLightning/pytorch-lightning/pull/617))
- Fixed a bug where `num_training_batches` and `num_test_batches` would sometimes be rounded down to zero ([#649](https://github.com/PyTorchLightning/pytorch-lightning/pull/649))
- Fixed a bug where an additional batch would be processed when manually setting `num_training_batches` ([#653](https://github.com/PyTorchLightning/pytorch-lightning/pull/653))
- Fixed a bug when batches did not have a `.copy` method ([#701](https://github.com/PyTorchLightning/pytorch-lightning/pull/701))
- Fixed a bug when using `log_gpu_memory=True` in Python 3.6 ([#715](https://github.com/PyTorchLightning/pytorch-lightning/pull/715))
- Fixed a bug where checkpoint writing could exit before completion, giving incomplete checkpoints ([#689](https://github.com/PyTorchLightning/pytorch-lightning/pull/689))
- Fixed a bug where `on_train_end` was not called when ealy stopping ([#723](https://github.com/PyTorchLightning/pytorch-lightning/pull/723))
- Added option to disable default logger, checkpointer, and early stopping by passing `logger=False`, `checkpoint_callback=False` and `early_stop_callback=False` respectively
- Added `CometLogger` for use with Comet.ml
- Added `val_check_interval` argument to `Trainer` allowing validition to be performed at every given number of batches
- Added functionality to save and load hyperparameters using the standard checkpoint mechanism
- Added call to `torch.cuda.empty_cache` before training starts
- Added option for user to override the call t `backward`
- Changed functionality of `validation_end` and `test_end` with multiple dataloaders to be given all of the dataloaders at once rather than in seperate calls
- Changed print_nan_grads to only print the parameter value and gradients when they contain NaN
- Changed gpu API to take integers as well (e.g. `gpus=2` instead of `gpus=[0, 1]`)
- All models now loaded on to CPU to avoid device and out of memory issues in PyTorch
- Fixed a bug where an `Exception` would be thrown if using `torch.DistributedDataParallel` without using a `DistributedSampler`, this now throws a `Warning` instead
