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lightning/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.

[unreleased] - YYYY-MM-DD

Added

  • Added type hints in Trainer.fit() and Trainer.test() to reflect that also a list of dataloaders can be passed in (#1723).

Changed

  • Allow user to select individual TPU core to train on (#1729)

Deprecated

Removed

Fixed

[0.7.6] - 2020-05-16

Added

  • Added callback for logging learning rates (#1498)
  • Added transfer learning example (for a binary classification task in computer vision) (#1564)
  • Added type hints in Trainer.fit() and Trainer.test() to reflect that also a list of dataloaders can be passed in (#1723).
  • Added auto scaling of batch size (#1638)
  • The progress bar metrics now also get updated in training_epoch_end (#1724)
  • Enable NeptuneLogger to work with distributed_backend=ddp (#1753)
  • Added option to provide seed to random generators to ensure reproducibility (#1572)
  • Added override for hparams in load_from_ckpt (#1797)
  • Added support multi-node distributed execution under torchelastic (#1811, #1818)
  • Added using store_true for bool args (#1822, #1842)
  • Added dummy logger for internally disabling logging for some features (#1836)

Changed

  • Enable non-blocking for device transfers to GPU (#1843)
  • Replace mata_tags.csv with hparams.yaml (#1271)
  • Reduction when batch_size < num_gpus (#1609)
  • Updated LightningTemplateModel to look more like Colab example (#1577)
  • Don't convert namedtuple to tuple when transferring the batch to target device (#1589)
  • Allow passing hparams as keyword argument to LightningModule when loading from checkpoint (#1639)
  • Args should come after the last positional argument (#1807)
  • Made ddp the default if no backend specified with multiple GPUs (#1789)

Deprecated

  • Deprecated tags_csv in favor of hparams_file (#1271)

Fixed

  • Fixed broken link in PR template (#1675)

  • Fixed ModelCheckpoint not None checking filepath (#1654)

  • Trainer now calls on_load_checkpoint() when resuming from a checkpoint (#1666)

  • Fixed sampler logic for ddp with iterable dataset (#1734)

  • Fixed _reset_eval_dataloader() for IterableDataset (#1560)

  • Fixed Horovod distributed backend to set the root_gpu property (#1669)

  • Fixed wandb logger global_step affects other loggers (#1492)

  • Fixed disabling progress bar on non-zero ranks using Horovod backend (#1709)

  • Fixed bugs that prevent lr finder to be used together with early stopping and validation dataloaders (#1676)

  • Fixed a bug in Trainer that prepended the checkpoint path with version_ when it shouldn't (#1748)

  • Fixed lr key name in case of param groups in LearningRateLogger (#1719)

  • Fixed saving native AMP scaler state (introduced in #1561)

  • Fixed accumulation parameter and suggestion method for learning rate finder (#1801)

  • Fixed num processes wasn't being set properly and auto sampler was ddp failing (#1819)

  • Fixed bugs in semantic segmentation example (#1824)

  • Fixed saving native AMP scaler state (#1561, #1777)

  • Fixed native amp + ddp (#1788)

  • Fixed hparam logging with metrics (#1647)

  • Fixed an issue with Trainer constructor silently ignoring unkown/misspelled arguments (#1820)

  • Fixed save_weights_only in ModelCheckpoint (#1780)

[0.7.5] - 2020-04-27

Changed

  • Allow logging of metrics together with hparams (#1630)
  • Allow metrics logged together with hparams (#1630)

Removed

  • Removed Warning from trainer loop (#1634)

Fixed

  • Fixed ModelCheckpoint not being fixable (#1632)
  • Fixed CPU DDP breaking change and DDP change (#1635)
  • Tested pickling (#1636)

[0.7.4] - 2020-04-26

Added

  • Added flag replace_sampler_ddp to manually disable sampler replacement in DDP (#1513)
  • Added speed parity tests (max 1 sec difference per epoch)(#1482)
  • Added auto_select_gpus flag to trainer that enables automatic selection of available GPUs on exclusive mode systems.
  • Added learning rate finder (#1347)
  • Added support for ddp mode in clusters without SLURM (#1387)
  • Added test_dataloaders parameter to Trainer.test() (#1434)
  • Added terminate_on_nan flag to trainer that performs a NaN check with each training iteration when set to True (#1475)
  • Added speed parity tests (max 1 sec difference per epoch)(#1482)
  • Added terminate_on_nan flag to trainer that performs a NaN check with each training iteration when set to True. (#1475)
  • Added ddp_cpu backend for testing ddp without GPUs (#1158)
  • Added Horovod support as a distributed backend Trainer(distributed_backend='horovod') (#1529)
  • Added support for 8 core distributed training on Kaggle TPU's (#1568)
  • Added support for native AMP (#1561, #1580)

Changed

  • Changed the default behaviour to no longer include a NaN check with each training iteration. (#1475)
  • Decoupled the progress bar from trainer` it is a callback now and can be customized or even be replaced entirely (#1450).
  • Changed lr schedule step interval behavior to update every backwards pass instead of every forwards pass (#1477)
  • Defines shared proc. rank, remove rank from instances (e.g. loggers) (#1408)
  • Updated semantic segmentation example with custom U-Net and logging (#1371)
  • Disabled val and test shuffling (#1600)

Deprecated

  • Deprecated training_tqdm_dict in favor of progress_bar_dict (#1450).

Removed

  • Removed test_dataloaders parameter from Trainer.fit() (#1434)

Fixed

  • Added the possibility to pass nested metrics dictionaries to loggers (#1582)
  • Fixed memory leak from opt return (#1528)
  • Fixed saving checkpoint before deleting old ones (#1453)
  • Fixed loggers - flushing last logged metrics even before continue, e.g. trainer.test() results (#1459)
  • Fixed optimizer configuration when configure_optimizers returns dict without lr_scheduler (#1443)
  • Fixed LightningModule - mixing hparams and arguments in LightningModule.__init__() crashes load_from_checkpoint() (#1505)
  • Added a missing call to the on_before_zero_grad model hook (#1493).
  • Allow use of sweeps with WandbLogger (#1512)
  • Fixed a bug that caused the callbacks Trainer argument to reference a global variable (#1534).
  • Fixed a bug that set all boolean CLI arguments from Trainer.add_argparse_args always to True (#1571)
  • Fixed do not copy the batch when training on a single GPU (#1576, #1579)
  • Fixed soft checkpoint removing on DDP (#1408)
  • Fixed automatic parser bug (#1585)
  • Fixed bool conversion from string (#1606)

[0.7.3] - 2020-04-09

Added

  • Added rank_zero_warn for warning only in rank 0 (#1428)

Fixed

  • Fixed default DistributedSampler for DDP training (#1425)
  • Fixed workers warning not on windows (#1430)
  • Fixed returning tuple from run_training_batch (#1431)
  • Fixed gradient clipping (#1438)
  • Fixed pretty print (#1441)

[0.7.2] - 2020-04-07

Added

  • Added same step loggers' metrics aggregation (#1278)
  • Added parity test between a vanilla MNIST model and lightning model (#1284)
  • Added parity test between a vanilla RNN model and lightning model (#1351)
  • Added Reinforcement Learning - Deep Q-network (DQN) lightning example (#1232)
  • Added support for hierarchical dict (#1152)
  • Added TrainsLogger class (#1122)
  • Added type hints to pytorch_lightning.core (#946)
  • Added support for IterableDataset in validation and testing (#1104)
  • Added support for non-primitive types in hparams for TensorboardLogger (#1130)
  • Added a check that stops the training when loss or weights contain NaN or inf values. (#1097)
  • Added support for IterableDataset when val_check_interval=1.0 (default), this will trigger validation at the end of each epoch. (#1283)
  • Added summary method to Profilers. (#1259)
  • Added informative errors if user defined dataloader has zero length (#1280)
  • Added testing for python 3.8 (#915)
  • Added a training_epoch_end method which is the mirror of validation_epoch_end. (#1357)
  • Added model configuration checking (#1199)
  • Added support for optimizer frequencies through LightningModule.configure_optimizers() (#1269)
  • Added option to run without an optimizer by returning None from configure_optimizers. (#1279)
  • Added a warning when the number of data loader workers is small. (#1378)

Changed

  • Changed (renamed and refatored) TensorRunningMean -> TensorRunningAccum: running accumulations were generalized. (#1278)
  • Changed progress_bar_refresh_rate trainer flag to disable progress bar when set to 0. (#1108)
  • Enhanced load_from_checkpoint to also forward params to the model (#1307)
  • Updated references to self.forward() to instead use the __call__ interface. (#1211)
  • Changed default behaviour of configure_optimizers to use no optimizer rather than Adam. (#1279)
  • Allow to upload models on W&B (#1339)
  • On DP and DDP2 unsqueeze is automated now (#1319)
  • Did not always create a DataLoader during reinstantiation, but the same type as before (if subclass of DataLoader) (#1346)
  • Did not interfere with a default sampler (#1318)
  • Remove default Adam optimizer (#1317)
  • Give warnings for unimplemented required lightning methods (#1317)
  • Made evaluate method private >> Trainer._evaluate(...). (#1260)
  • Simplify the PL examples structure (shallower and more readable) (#1247)
  • Changed min max gpu memory to be on their own plots (#1358)
  • Remove .item which causes sync issues (#1254)
  • Changed smoothing in TQDM to decrease variability of time remaining between training / eval (#1194)
  • Change default logger to dedicated one (#1064)

Deprecated

  • Deprecated Trainer argument print_nan_grads (#1097)
  • Deprecated Trainer argument show_progress_bar (#1108)

Removed

  • Removed test for no test dataloader in .fit (#1495)
  • Removed duplicated module pytorch_lightning.utilities.arg_parse for loading CLI arguments (#1167)
  • Removed wandb logger's finalize method (#1193)
  • Dropped torchvision dependency in tests and added own MNIST dataset class instead (#986)

Fixed

  • Fixed model_checkpoint when saving all models (#1359)
  • Trainer.add_argparse_args classmethod fixed. Now it adds a type for the arguments (#1147)
  • Fixed bug related to type checking of ReduceLROnPlateau lr schedulers(#1126)
  • Fixed a bug to ensure lightning checkpoints to be backward compatible (#1132)
  • Fixed a bug that created an extra dataloader with active reload_dataloaders_every_epoch (#1196)
  • Fixed all warnings and errors in the docs build process (#1191)
  • Fixed an issue where val_percent_check=0 would not disable validation (#1251)
  • Fixed average of incomplete TensorRunningMean (#1309)
  • Fixed WandbLogger.watch with wandb.init() (#1311)
  • Fixed an issue with early stopping that would prevent it from monitoring training metrics when validation is disabled / not implemented (#1235).
  • Fixed a bug that would cause trainer.test() to run on the validation set when overloading validation_epoch_end and test_end (#1353)
  • Fixed WandbLogger.watch - use of the watch method without importing wandb (#1311)
  • Fixed WandbLogger to be used with 'ddp' - allow reinits in sub-processes (#1149, #1360)
  • Made training_epoch_end behave like validation_epoch_end (#1357)
  • Fixed fast_dev_run running validation twice (#1365)
  • Fixed pickle error from quick patch __code__ (#1352)
  • Fixed memory leak on GPU0 (#1094, #1349)
  • Fixed checkpointing interval (#1272)
  • Fixed validation and training loops run the partial dataset (#1192)
  • Fixed running on_validation_end only on main process in DDP (#1125)
  • Fixed load_spawn_weights only in proc rank 0 (#1385)
  • Fixes use_amp issue (#1145)
  • Fixes using deprecated use_amp attribute (#1145)
  • Fixed Tensorboard logger error: lightning_logs directory not exists in multi-node DDP on nodes with rank != 0 (#1377)
  • Fixed Unimplemented backend XLA error on TPU (#1387)

[0.7.1] - 2020-03-07

Fixed

  • Fixes print issues and data_loader (#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)
  • Added reload_dataloaders_every_epoch=False flag for trainer. Some users require reloading data every epoch (#926)
  • Added progress_bar_refresh_rate=50 flag for trainer. Throttle refresh rate on notebooks (#926)
  • Updated governance docs
  • Added a check to ensure that the metric used for early stopping exists before training commences (#542)
  • Added optimizer_idx argument to backward hook (#733)
  • Added entity argument to WandbLogger to be passed to wandb.init (#783)
  • Added a tool for profiling training runs (#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)
  • Added option to specify step key when logging metrics (#808)
  • Added train_dataloader, val_dataloader and test_dataloader arguments to Trainer.fit(), for alternative data parsing (#759)
  • Added Tensor Processing Unit (TPU) support (#868)
  • Added semantic segmentation example (#751,#876, #881)
  • Split callbacks in multiple files (#849)
  • Support for user defined callbacks (#889 and #950)
  • Added support for multiple loggers to be passed to Trainer as an iterable (e.g. list, tuple, etc.) (#903)
  • Added support for step-based learning rate scheduling (#941)
  • Added support for logging hparams as dict (#1029)
  • Checkpoint and early stopping now work without val. step (#1041)
  • Support graceful training cleanup after Keyboard Interrupt (#856, #1019)
  • Added type hints for function arguments (#912, )
  • Added default argparser for Trainer (#952, #1023)
  • Added TPU gradient clipping (#963)
  • Added max/min number of steps in Trainer (#728)

Changed

  • Improved NeptuneLogger by adding close_after_fit argument to allow logging after training(#908)
  • Changed default TQDM to use tqdm.auto for prettier outputs in IPython notebooks (#752)
  • Changed pytorch_lightning.logging to pytorch_lightning.loggers (#767)
  • Moved the default tqdm_dict definition from Trainer to LightningModule, so it can be overridden by the user (#749)
  • Moved functionality of LightningModule.load_from_metrics into LightningModule.load_from_checkpoint (#995)
  • Changed Checkpoint path parameter from filepath to dirpath (#1016)
  • Freezed models hparams as Namespace property (#1029)
  • Dropped logging config in package init (#1015)
  • Renames model steps (#1051)
    • training_end >> training_epoch_end
    • validation_end >> validation_epoch_end
    • test_end >> test_epoch_end
  • Refactor dataloading, supports infinite dataloader (#955)
  • Create single file in TensorBoardLogger (#777)

Deprecated

  • Deprecated pytorch_lightning.logging (#767)
  • Deprecated LightningModule.load_from_metrics in favour of LightningModule.load_from_checkpoint (#995, #1079)
  • Deprecated @data_loader decorator (#926)
  • Deprecated model steps training_end, validation_end and test_end (#1051, #1056)

Removed

  • Removed dependency on pandas (#736)
  • Removed dependency on torchvision (#797)
  • Removed dependency on scikit-learn (#801)

Fixed

  • Fixed a bug where early stopping on_end_epoch would be called inconsistently when check_val_every_n_epoch == 0 (#743)
  • Fixed a bug where the model checkpointer didn't write to the same directory as the logger (#771)
  • Fixed a bug where the TensorBoardLogger class would create an additional empty log file during fitting (#777)
  • Fixed a bug where global_step was advanced incorrectly when using accumulate_grad_batches > 1 (#832)
  • Fixed a bug when calling self.logger.experiment with multiple loggers (#1009)
  • Fixed a bug when calling logger.append_tags on a NeptuneLogger with a single tag (#1009)
  • Fixed sending back data from .spawn by saving and loading the trained model in/out of the process (#1017
  • Fixed port collision on DDP (#1010)
  • Fixed/tested pass overrides (#918)
  • Fixed comet logger to log after train (#892)
  • Remove deprecated args to learning rate step function (#890)

[0.6.0] - 2020-01-21

Added

  • Added support for resuming from a specific checkpoint via resume_from_checkpoint argument (#516)
  • Added support for ReduceLROnPlateau scheduler (#320)
  • Added support for Apex mode O2 in conjunction with Data Parallel (#493)
  • Added option (save_top_k) to save the top k models in the ModelCheckpoint class (#128)
  • Added on_train_start and on_train_end hooks to ModelHooks (#598)
  • Added TensorBoardLogger (#607)
  • Added support for weight summary of model with multiple inputs (#543)
  • Added map_location argument to load_from_metrics and load_from_checkpoint (#625)
  • Added option to disable validation by setting val_percent_check=0 (#649)
  • Added NeptuneLogger class (#648)
  • Added WandbLogger class (#627)

Changed

  • Changed the default progress bar to print to stdout instead of stderr (#531)
  • Renamed step_idx to step, epoch_idx to epoch, max_num_epochs to max_epochs and min_num_epochs to min_epochs (#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)
  • Changed gradient logging to use parameter names instead of indexes (#660)
  • Changed the default logger to TensorBoardLogger (#609)
  • Changed the directory for tensorboard logging to be the same as model checkpointing (#706)

Deprecated

  • Deprecated max_nb_epochs and min_nb_epochs (#567)
  • Deprecated the on_sanity_check_start hook in ModelHooks (#598)

Removed

  • Removed the save_best_only argument from ModelCheckpoint, use save_top_k=1 instead (#128)

Fixed

  • Fixed a bug which ocurred when using Adagrad with cuda (#554)
  • Fixed a bug where training would be on the GPU despite setting gpus=0 or gpus=[] (#561)
  • Fixed an error with print_nan_gradients when some parameters do not require gradient (#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)
  • Fixed support for PyTorch 1.1.0 (#552)
  • Fixed an issue with early stopping when using a val_check_interval < 1.0 in Trainer (#492)
  • Fixed bugs relating to the CometLogger object that would cause it to not work properly (#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)
  • Fixed a potential race condition with several processes trying to create checkpoint directories (#530)
  • Fixed a bug where batch 'segments' would remain on the GPU when using truncated_bptt > 1 (#532)
  • Fixed a bug when using IterableDataset (#547)
  • Fixed a bug where .item was called on non-tensor objects (#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)
  • Fixed a bug where early stopping would begin two epochs early (#617)
  • Fixed a bug where num_training_batches and num_test_batches would sometimes be rounded down to zero (#649)
  • Fixed a bug where an additional batch would be processed when manually setting num_training_batches (#653)
  • Fixed a bug when batches did not have a .copy method (#701)
  • Fixed a bug when using log_gpu_memory=True in Python 3.6 (#715)
  • Fixed a bug where checkpoint writing could exit before completion, giving incomplete checkpoints (#689)
  • Fixed a bug where on_train_end was not called when ealy stopping (#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