# Changelog All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/). ## [unreleased] - YYYY-MM-DD ### Added - Added parity test between a vanilla MNIST model and lightning model ([#1284](https://github.com/PyTorchLightning/pytorch-lightning/pull/1284)) - Added Reinforcement Learning - Deep Q-network (DQN) lightning example ([#1232](https://github.com/PyTorchLightning/pytorch-lightning/pull/1232)) - Added support for hierarchical `dict` ([#1152](https://github.com/PyTorchLightning/pytorch-lightning/pull/1152)) - Added `TrainsLogger` class ([#1122](https://github.com/PyTorchLightning/pytorch-lightning/pull/1122)) - Added type hints to `pytorch_lightning.core` ([#946](https://github.com/PyTorchLightning/pytorch-lightning/pull/946)) - 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)) - Updated references to self.forward() to instead use the `__call__` interface. ([#1211](https://github.com/PyTorchLightning/pytorch-lightning/pull/1211)) - 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)) ### Changed - Enhanced load_from_checkpoint to also forward params to the model ([#1307](https://github.com/PyTorchLightning/pytorch-lightning/pull/1307)) - Made `evalaute` method private >> `Trainer._evaluate(...)`. ([#1260](https://github.com/PyTorchLightning/pytorch-lightning/pull/1260)) ### Deprecated - Deprecated Trainer argument `print_nan_grads` ([#1097](https://github.com/PyTorchLightning/pytorch-lightning/pull/1097)) ### Removed - Removed duplicated module `pytorch_lightning.utilities.arg_parse` for loading CLI arguments ([#1167](https://github.com/PyTorchLightning/pytorch-lightning/issues/1167)) - Dropped `torchvision` dependency in tests and added own MNIST dataset class instead ([#986](https://github.com/PyTorchLightning/pytorch-lightning/issues/986)) ### Fixed - `Trainer.add_argparse_args` classmethod fixed. Now it adds a type for the arguments ([#1147](https://github.com/PyTorchLightning/pytorch-lightning/pull/1147)). - Fixed bug related to type cheking of `ReduceLROnPlateau` lr schedulers([#1114](https://github.com/PyTorchLightning/pytorch-lightning/issues/1114)) - Fixed a bug to ensure lightning checkpoints to be backward compatible ([#1132](https://github.com/PyTorchLightning/pytorch-lightning/pull/1132)) - Fixed all warnings and errors in the docs build process ([#1191](https://github.com/PyTorchLightning/pytorch-lightning/pull/1191)) - Fixed an issue where `val_percent_check=0` would not disable validation ([#1251](https://github.com/PyTorchLightning/pytorch-lightning/pull/1251)) - Fixed average of incomplete `TensorRunningMean` ([#1309](https://github.com/PyTorchLightning/pytorch-lightning/pull/1309)) ## [0.7.1] - 2020-03-07 ### Fixed - Fixes `print` issues and `data_loader` ([#1080](https://github.com/PyTorchLightning/pytorch-lightning/pull/1080)) ## [0.7.0] - 2020-03-06 ### Added - 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)) - Updated governance docs - 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)) - Added semantic segmentation example ([#751](https://github.com/PyTorchLightning/pytorch-lightning/pull/751),[#876](https://github.com/PyTorchLightning/pytorch-lightning/pull/876), [#881](https://github.com/PyTorchLightning/pytorch-lightning/pull/881)) - Split callbacks in multiple files ([#849](https://github.com/PyTorchLightning/pytorch-lightning/pull/849)) - 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)) - Added support for step-based learning rate scheduling ([#941](https://github.com/PyTorchLightning/pytorch-lightning/pull/941)) - Added support for logging hparams as dict ([#1029](https://github.com/PyTorchLightning/pytorch-lightning/pull/1029)) - Checkpoint and early stopping now work without val. step ([#1041](https://github.com/PyTorchLightning/pytorch-lightning/pull/1041)) - Support graceful training cleanup after Keyboard Interrupt ([#856](https://github.com/PyTorchLightning/pytorch-lightning/pull/856), [#1019](https://github.com/PyTorchLightning/pytorch-lightning/pull/1019)) - Added type hints for function arguments ([#912](https://github.com/PyTorchLightning/pytorch-lightning/pull/912), ) - Added default `argparser` for `Trainer` ([#952](https://github.com/PyTorchLightning/pytorch-lightning/pull/1023), [#1023](https://github.com/PyTorchLightning/pytorch-lightning/pull/1023)) - Added TPU gradient clipping ([#963](https://github.com/PyTorchLightning/pytorch-lightning/pull/963)) - Added max/min number of steps in `Trainer` ([#728](https://github.com/PyTorchLightning/pytorch-lightning/pull/728)) ### Changed - 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)) - Changed Checkpoint path parameter from `filepath` to `dirpath` ([#1016](https://github.com/PyTorchLightning/pytorch-lightning/pull/1016)) - Freezed models `hparams` as `Namespace` property ([#1029](https://github.com/PyTorchLightning/pytorch-lightning/pull/1029)) - Dropped `logging` config in package init ([#1015](https://github.com/PyTorchLightning/pytorch-lightning/pull/1015)) - Renames model steps ([#1051](https://github.com/PyTorchLightning/pytorch-lightning/pull/1051)) * `training_end` >> `training_epoch_end` * `validation_end` >> `validation_epoch_end` * `test_end` >> `test_epoch_end` - Refactor dataloading, supports infinite dataloader ([#955](https://github.com/PyTorchLightning/pytorch-lightning/pull/955)) - Create single file in `TensorBoardLogger` ([#777](https://github.com/PyTorchLightning/pytorch-lightning/pull/777)) ### Deprecated - Deprecated `pytorch_lightning.logging` ([#767](https://github.com/PyTorchLightning/pytorch-lightning/pull/767)) - Deprecated `LightningModule.load_from_metrics` in favour of `LightningModule.load_from_checkpoint` ([#995](https://github.com/PyTorchLightning/pytorch-lightning/pull/995), [#1079](https://github.com/PyTorchLightning/pytorch-lightning/pull/1079)) - Deprecated `@data_loader` decorator ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926)) - Deprecated model steps `training_end`, `validation_end` and `test_end` ([#1051](https://github.com/PyTorchLightning/pytorch-lightning/pull/1051), [#1056](https://github.com/PyTorchLightning/pytorch-lightning/pull/1056)) ### Removed - Removed dependency on `pandas` ([#736](https://github.com/PyTorchLightning/pytorch-lightning/pull/736)) - Removed dependency on `torchvision` ([#797](https://github.com/PyTorchLightning/pytorch-lightning/pull/797)) - Removed dependency on `scikit-learn` ([#801](https://github.com/PyTorchLightning/pytorch-lightning/pull/801)) ### Fixed - 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)) - Fixed/tested pass overrides ([#918](https://github.com/PyTorchLightning/pytorch-lightning/pull/918)) - Fixed comet logger to log after train ([#892](https://github.com/PyTorchLightning/pytorch-lightning/pull/892)) - Remove deprecated args to learning rate step function ([#890](https://github.com/PyTorchLightning/pytorch-lightning/pull/890)) ## [0.6.0] - 2020-01-21 ### Added - 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 `TensorBoardLogger` ([#607](https://github.com/PyTorchLightning/pytorch-lightning/pull/607)) - 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 - 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)) ### Deprecated - Deprecated `max_nb_epochs` and `min_nb_epochs` ([#567](https://github.com/PyTorchLightning/pytorch-lightning/pull/567)) - Deprecated the `on_sanity_check_start` hook in `ModelHooks` ([#598](https://github.com/PyTorchLightning/pytorch-lightning/pull/598)) ### Removed - Removed the `save_best_only` argument from `ModelCheckpoint`, use `save_top_k=1` instead ([#128](https://github.com/PyTorchLightning/pytorch-lightning/pull/128)) ### Fixed - 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)) ## [0.5.3] - 2019-11-06 ### Added - 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` - Added support for truncated backprop through time via the `truncated_bptt_steps` argument in `Trainer` - Added option to operate on all outputs from `training_step` in DDP2 - Added a hook for modifying DDP init - Added a hook for modifying Apex ### Changed - Changed experiment version to be padded with zeros (e.g. `/dir/version_9` becomes `/dir/version_0009`) - Changed callback metrics to include any metrics given in logs or progress bar - Changed the default for `save_best_only` in `ModelCheckpoint` to `True` - Added `tng_data_loader` for backwards compatibility - Renamed `MLFlowLogger.client` to `MLFlowLogger.experiment` for consistency - Moved `global_step` increment to happen after the batch has been processed - Changed weights restore to first attempt HPC weights before restoring normally, preventing both weights being restored and running out of memory - Changed progress bar functionality to add multiple progress bars for train/val/test - Changed calls to `print` to use `logging` instead ### Deprecated - Deprecated `tng_dataloader` ### Fixed - Fixed an issue where the number of batches was off by one during training - Fixed a bug that occured when setting a ckeckpoint callback and `early_stop_callback=False` - Fixed an error when importing CometLogger - Fixed a bug where the `gpus` argument had some unexpected behaviour - Fixed a bug where the computed total number of batches was sometimes incorrect - Fixed a bug where the progress bar would sometimes not show the total number of batches in test mode - Fixed a bug when using the `log_gpu_memory='min_max'` option in `Trainer` - Fixed a bug where checkpointing would sometimes erase the current directory ## [0.5.2] - 2019-10-10 ### Added - Added `weights_summary` argument to `Trainer` to be set to `full` (full summary), `top` (just top level modules) or other - Added `tags` argument to `MLFlowLogger` ### Changed - Changed default for `amp_level` to `O1` ### Removed - Removed the `print_weights_summary` argument from `Trainer` ### Fixed - Fixed a bug where logs were not written properly - Fixed a bug where `logger.finalize` wasn't called after training is complete - Fixed callback metric errors in DDP - Fixed a bug where `TestTubeLogger` didn't log to the correct directory ## [0.5.1] - 2019-10-05 ### Added - Added the `LightningLoggerBase` class for experiment loggers - Added `MLFlowLogger` for logging with `mlflow` - Added `TestTubeLogger` for logging with `test_tube` - Added a different implementation of DDP (`distributed_backed='ddp2'`) where every node has one model using all GPUs - Added support for optimisers which require a closure (e.g. LBFGS) - Added automatic `MASTER_PORT` defualt for DDP when not set manually - Added new GPU memory logging options `'min_max'` (log only the min/max utilization) and `'all'` (log all the GPU memory) ### Changed - Changed schedulers to always be called with the current epoch - Changed `test_tube` to an optional dependency - Changed data loaders to internally use a getter instead of a python property - Disabled auto GPU loading when restoring weights to prevent out of memory errors - Changed logging, early stopping and checkpointing to occur by default ### Fixed - Fixed a bug with samplers that do not specify `set_epoch` - Fixed a bug when using the `MLFlowLogger` with unsupported data types, this will now raise a warning - Fixed a bug where gradient norms were alwasy zero using `track_grad_norm` - Fixed a bug which causes a crash when logging memory ## [0.5.0] - 2019-09-26 ### Changed - Changed `data_batch` argument to `batch` throughout - Changed `batch_i` argument to `batch_idx` throughout - Changed `tng_dataloader` method to `train_dataloader` - Changed `on_tng_metrics` method to `on_training_metrics` - Changed `gradient_clip` argument to `gradient_clip_val` - Changed `add_log_row_interval` to `row_log_interval` ### Fixed - Fixed a bug with tensorboard logging in multi-gpu setup ## [0.4.9] - 2019-09-16 ### Added - Added the flag `log_gpu_memory` to `Trainer` to deactivate logging of GPU memory utilization - Added SLURM resubmit functionality (port from test-tube) - Added optional weight_save_path to trainer to remove the need for a checkpoint_callback when using cluster training - Added option to use single gpu per node with `DistributedDataParallel` ### Changed - 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 - Fixed a bug where data types that implement `.to` but not `.cuda` would not be properly moved onto the GPU - Fixed a bug where data would not be re-shuffled every epoch when using a `DistributedSampler` ## [0.4.8] - 2019-08-31 ### Added - Added `test_step` and `test_end` methods, used when `Trainer.test` is called - Added `GradientAccumulationScheduler` callback which can be used to schedule changes to the number of accumulation batches - Added option to skip the validation sanity check by setting `nb_sanity_val_steps = 0` ### Fixed - Fixed a bug when setting `nb_sanity_val_steps = 0` ## [0.4.7] - 2019-08-24 ### Changed - Changed the default `val_check_interval` to `1.0` - Changed defaults for `nb_val_batches`, `nb_tng_batches` and `nb_test_batches` to 0 ### Fixed - Fixed a bug where the full validation set as used despite setting `val_percent_check` - Fixed a bug where an `Exception` was thrown when using a data set containing a single batch - Fixed a bug where an `Exception` was thrown if no `val_dataloader` was given - Fixed a bug where tuples were not properly transfered to the GPU - Fixed a bug where data of a non standard type was not properly handled by the trainer - Fixed a bug when loading data as a tuple - Fixed a bug where `AttributeError` could be suppressed by the `Trainer` ## [0.4.6] - 2019-08-15 ### Added - Added support for data to be given as a `dict` or `list` with a single gpu - Added support for `configure_optimizers` to return a single optimizer, two list (optimizers and schedulers), or a single list ### Fixed - Fixed a bug where returning just an optimizer list (i.e. without schedulers) from `configure_optimizers` would throw an `Exception` ## [0.4.5] - 2019-08-13 ### Added - Added `optimizer_step` method that can be overridden to change the standard optimizer behaviour ## [0.4.4] - 2019-08-12 ### Added - Added supoort for multiple validation dataloaders - Added support for latest test-tube logger (optimised for `torch==1.2.0`) ### Changed - `validation_step` and `val_dataloader` are now optional - `lr_scheduler` is now activated after epoch ### Fixed - Fixed a bug where a warning would show when using `lr_scheduler` in `torch>1.1.0` - Fixed a bug where an `Exception` would be thrown if using `torch.DistributedDataParallel` without using a `DistributedSampler`, this now throws a `Warning` instead ## [0.4.3] - 2019-08-10 ### Fixed - Fixed a bug where accumulate gradients would scale the loss incorrectly ## [0.4.2] - 2019-08-08 ### Changed - Changed install requirement to `torch==1.2.0` ## [0.4.1] - 2019-08-08 ### Changed - Changed install requirement to `torch==1.1.0` ## [0.4.0] - 2019-08-08 ### Added - Added 16-bit support for a single GPU - Added support for training continuation (preserves epoch, global step etc.) ### Changed - Changed `training_step` and `validation_step`, outputs will no longer be automatically reduced ### Removed - Removed need for `Experiment` object in `Trainer` ### Fixed - Fixed issues with reducing outputs from generative models (such as images and text) ## [0.3.6] - 2019-07-25 ### Added - Added a decorator to do lazy data loading internally ### Fixed - Fixed a bug where `Experiment` object was not process safe, potentially causing logs to be overwritten ## [0.3.5] - 2019-MM-DD ## [0.3.4] - 2019-MM-DD ## [0.3.3] - 2019-MM-DD ## [0.3.2] - 2019-MM-DD ## [0.3.1] - 2019-MM-DD ## [0.2.x] - YYYY-MM-DD ## [0.1.x] - YYYY-MM-DD