Changelog

.github/PULL_REQUEST_TEMPLATE.md

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 - [ ] Did you read the [contributor guideline](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/.github/CONTRIBUTING.md)?
 - [ ] Did you make sure to update the docs?   
 - [ ] Did you write any new necessary tests?  
+- [ ] If you made a notable change (that affects users), did you update the [CHANGELOG](../CHANGELOG.md)?
 
 ## What does this PR do?
 Fixes # (issue).

CHANGELOG.md

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+# 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]
+### Added
+- Added a check to ensure that the metric used for early stopping exists before training commences
+- Added `optimizer_idx` argument to `backward` hook
+- Added `entity` argument to `WandbLogger` to be passed to `wandb.init`
+- Added a tool for profiling training runs
+- 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
+- Added option to specify `step` key when logging metrics
+### Changed
+- Changed default TQDM to use `tqdm.auto` for prettier outputs in IPython notebooks
+- Changed `pytorch_lightning.logging` to `pytorch_lightning.loggers`
+- Moved the default `tqdm_dict` definition from Trainer to `LightningModule`, so it can be overridden by the user
+### Deprecated
+### Removed
+- Removed dependency on pandas
+- Removed dependency on torchvision
+- Removed dependency on scikit-learn
+### Fixed
+- Fixed a bug where early stopping `on_end_epoch` would be called inconsistently when `check_val_every_n_epoch == 0`
+- Fixed a bug where the model checkpointer didn't write to the same directory as the logger
+- Fixed a bug where the `TensorBoardLogger` class would create an additional empty log file during fitting
+- Fixed a bug where `global_step` was advanced incorrectly when using `accumulate_grad_batches > 1`
+
+## [0.6.0] - 2020-01-21
+### Added
+- Added support for resuming from a specific checkpoint via `resume_from_checkpoint` argument
+- Added support for `ReduceLROnPlateau` scheduler
+- Added support for Apex mode `O2` in conjunction with Data Parallel
+- Added option (`save_top_k`) to save the top k models in the `ModelCheckpoint` class
+- Added `on_train_start` and `on_train_end` hooks to `ModelHooks`
+- Added `TensorBoardLogger`
+- Added support for weight summary of model with multiple inputs
+- Added `map_location` argument to `load_from_metrics` and `load_from_checkpoint`
+- Added option to disable validation by setting `val_percent_check=0`
+- Added `NeptuneLogger` class
+- Added `WandbLogger` class
+### Changed
+- Changed the default progress bar to print to stdout instead of stderr
+- Renamed `step_idx` to `step`, `epoch_idx` to `epoch`, `max_num_epochs` to `max_epochs` and `min_num_epochs` to `min_epochs`
+- 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`
+- Changed gradient logging to use parameter names instead of indexes
+- Changed the default logger to `TensorBoardLogger`
+- Changed the directory for tensorboard logging to be the same as model checkpointing
+### Deprecated
+- Deprecated `max_nb_epochs` and `min_nb_epochs`
+- Deprecated the `on_sanity_check_start` hook in `ModelHooks`
+### Removed
+- Removed the `save_best_only` argument from `ModelCheckpoint`, use `save_top_k=1` instead
+### Fixed
+- Fixed a bug which ocurred when using Adagrad with cuda
+- Fixed a bug where training would be on the GPU despite setting `gpus=0` or `gpus=[]`
+- Fixed an error with `print_nan_gradients` when some parameters do not require gradient
+- 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
+- Fixed support for PyTorch 1.1.0
+- Fixed an issue with early stopping when using a `val_check_interval < 1.0` in `Trainer`
+- Fixed bugs relating to the `CometLogger` object that would cause it to not work properly
+- Fixed a bug that would occur when returning `-1` from `on_batch_start` following an early exit or when the batch was `None`
+- Fixed a potential race condition with several processes trying to create checkpoint directories
+- Fixed a bug where batch 'segments' would remain on the GPU when using `truncated_bptt > 1`
+- Fixed a bug when using `IterableDataset`
+- Fixed a bug where `.item` was called on non-tensor objects
+- 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`
+- Fixed a bug where early stopping would begin two epochs early
+- Fixed a bug where `num_training_batches` and `num_test_batches` would sometimes be rounded down to zero
+- Fixed a bug where an additional batch would be processed when manually setting `num_training_batches`
+- Fixed a bug when batches did not have a `.copy` method
+- Fixed a bug when using `log_gpu_memory=True` in Python 3.6
+- Fixed a bug where checkpoint writing could exit before completion, giving incomplete checkpoints
+- Fixed a bug where `on_train_end` was not called when ealy stopping
+
+## [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`
+### Removed
+### 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`
+### Deprecated
+### 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
+### Deprecated
+### Removed
+### 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
+### Added
+### 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`
+### Deprecated
+### Removed
+### 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
+### Deprecated
+### Removed
+### 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`
+### Changed
+### Deprecated
+### Removed
+### Fixed
+- Fixed a bug when setting `nb_sanity_val_steps = 0`
+
+## [0.4.7] - 2019-08-24
+### Added
+### 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
+### Deprecated
+### Removed
+### 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
+### Changed
+### Deprecated
+### Removed
+### 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
+### Changed
+### Deprecated
+### Removed
+### Fixed
+
+## [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
+### Deprecated
+### Removed
+### 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
+### Added
+### Changed
+### Deprecated
+### Removed
+### Fixed
+- Fixed a bug where accumulate gradients would scale the loss incorrectly
+
+## [0.4.2] - 2019-08-08
+### Added
+### Changed
+- Changed install requirement to `torch==1.2.0`
+### Deprecated
+### Removed
+### Fixed
+
+## [0.4.1] - 2019-08-08
+### Added
+### Changed
+- Changed install requirement to `torch==1.1.0`
+### Deprecated
+### Removed
+### Fixed
+
+## [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
+### Deprecated
+### Removed
+- Removed need for `Experiment` object in `Trainer`
+### Fixed
+- Fixed issues with reducing outputs from generative models (such as images and text)
+
+## [0.3.6.1] - 2019-07-27
+### Added
+### Changed
+### Deprecated
+### Removed
+### Fixed
+- Fixed a bug where `Experiment` object was not process safe, potentially causing logs to be overwritten
+
+## [0.3.6] - 2019-07-25
+### Added
+- Added a decorator to do lazy data loading internally
+### Changed
+### Deprecated
+### Removed
+### Fixed