From a33beb6ebf89595614696f584be2f58a126ebc5b Mon Sep 17 00:00:00 2001 From: Ethan Harris Date: Mon, 17 Feb 2020 13:09:11 +0000 Subject: [PATCH] Changelog (#869) * Create CHANGELOG.md * Update CHANGELOG.md * Update CHANGELOG.md * Update PULL_REQUEST_TEMPLATE.md * Update PULL_REQUEST_TEMPLATE.md * Add PR links to Version 0.6.0 in CHANGELOG.md * Add PR links for Unreleased in CHANGELOG.md * Update PULL_REQUEST_TEMPLATE.md --- .github/PULL_REQUEST_TEMPLATE.md | 1 + CHANGELOG.md | 335 +++++++++++++++++++++++++++++++ 2 files changed, 336 insertions(+) create mode 100644 CHANGELOG.md diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 258aac92..95f3b62f 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -4,6 +4,7 @@ - [ ] 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](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/.github/CHANGELOG.md)? ## What does this PR do? Fixes # (issue). diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 00000000..9069b116 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,335 @@ +# 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 ([#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)) +### Changed +- 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)) +### Deprecated +- None +### 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)) + +## [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` +### Removed +- None +### 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 +- None +### 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 +- None +### Removed +- None +### 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 +- None +### 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 +- None +### Removed +- None +### 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 +- None +### Removed +- None +### 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 +- None +### Deprecated +- None +### Removed +- None +### Fixed +- Fixed a bug when setting `nb_sanity_val_steps = 0` + +## [0.4.7] - 2019-08-24 +### Added +- None +### 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 +- None +### Removed +- None +### 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 +- None +### Deprecated +- None +### Removed +- None +### 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 +- None +### Deprecated +- None +### Removed +- None +### Fixed +- None + +## [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 +- None +### Removed +- None +### 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 +- None +### Changed +- None +### Deprecated +- None +### Removed +- None +### Fixed +- Fixed a bug where accumulate gradients would scale the loss incorrectly + +## [0.4.2] - 2019-08-08 +### Added +- None +### Changed +- Changed install requirement to `torch==1.2.0` +### Deprecated +- None +### Removed +- None +### Fixed +- None + +## [0.4.1] - 2019-08-08 +### Added +- None +### Changed +- Changed install requirement to `torch==1.1.0` +### Deprecated +- None +### Removed +- None +### Fixed +- None + +## [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 +- None +### 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 +- None +### Changed +- None +### Deprecated +- None +### Removed +- None +### 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 +- None +### Deprecated +- None +### Removed +- None +### Fixed +- None