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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.Features] - YYYY-MM-DD

Added

  • Added DataType, AverageMethod and MDMCAverageMethod enum in metrics (#5657

  • Added support for summarized model total params size in megabytes (#5590)

  • Added support for multiple train loaders (#1959)

  • Added Accuracy metric now generalizes to Top-k accuracy for (multi-dimensional) multi-class inputs using the top_k parameter (#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)

  • Added HammingDistance metric to compute the hamming distance (loss) (#4838)

  • Added max_fpr parameter to auroc metric for computing partial auroc metric (#3790)

  • Added StatScores metric to compute the number of true positives, false positives, true negatives and false negatives (#4839)

  • Added R2Score metric (#5241)

  • Added LambdaCallback (#5347)

  • Added BackboneLambdaFinetuningCallback (#5377)

  • Accelerator all_gather supports collection (#5221)

  • Added image_gradients functional metric to compute the image gradients of a given input image. (#5056)

  • Added MetricCollection (#4318)

  • Added .clone() method to metrics (#4318)

  • Added IoU class interface (#4704)

  • Added missing val/test hooks in LightningModule (#5467)

  • 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)

  • Added ModelPruning Callback (#5618)

  • Added PyTorchProfiler (#5560)

  • Added compositional metrics (#5464)

  • Added Trainer method predict(...) for high performence predictions (#5579)

  • Added AUC/AUROC class interface (#5479)

Changed

  • Changed stat_scores metric now calculates stat scores over all classes and gains new parameters, in line with the new StatScores metric (#4839)

  • Changed computer_vision_fine_tunning example to use BackboneLambdaFinetuningCallback (#5377)

  • Changed automatic casting for LoggerConnector metrics (#5218)

  • Changed iou [func] to allow float input (#4704)

  • Metric compute() method will no longer automatically call reset() (#5409)

  • Set PyTorch 1.4 as min requirements, also for testing and examples torchvision>=0.5 and torchtext>=0.5 (#5418)

  • Changed callbacks argument in Trainer to allow Callback input (#5446)

  • Changed the default of find_unused_parameters to False in DDP (#5185)

  • Changed ModelCheckpoint version suffixes to start at 1 (5008)

  • Changed the default value for the progress_bar_refresh_rate Trainer argument in Google COLAB notebooks to 20 (#5516)

  • Made LightningModule.global_rank, LightningModule.local_rank and LightningModule.logger read-only properties (#5730)

  • Forced ModelCheckpoint callbacks to run after all others to guarantee all states are saved to the checkpoint (#5731)

  • Refactored Accelerators and Plugins

    • Added base classes for plugins (#5715)
    • Added parallel plugins for DP, DDP, DDPSpawn, DDP2 and Horovod (#5714)
    • Added new Accelerators for CPU, GPU and TPU (#5719)
    • Added Plugins for TPU training (#5719)
    • Added RPC and Sharded plugins (#5732)
    • Added missing LightningModule-wrapper logic to new plugins and accelerator (#5734)

Deprecated

  • Function stat_scores_multiple_classes is deprecated in favor of stat_scores (#4839)

  • Moved accelerators and plugins to its legacy pkg (#5645)

  • Deprecated LightningDistributedDataParallel in favor of new wrapper module LightningDistributedModule (#5185)

  • Deprecated LightningDataParallel in favor of new wrapper module LightningParallelModule (#5670)

Removed

  • Removed deprecated checkpoint argument filepath (#5321)

  • Removed deprecated Fbeta, f1_score and fbeta_score metrics (#5322)

  • Removed deprecated TrainResult (#5323)

  • Removed deprecated EvalResult (#5633)

  • Removed LoggerStages (#5673)

Fixed

  • Fixed distributed setting and ddp_cpu only with num_processes>1 (#5297)

  • Fixed the saved filename in ModelCheckpoint when it already exists (#4861)

  • Fixed DDPHPCAccelerator hangs in DDP construction by calling init_device (#5157)

  • Fixed num_workers for Windows example (#5375)

  • Fixed loading yaml (#5619)

[unreleased.Bugfixes] - YYYY-MM-DD

Added

Changed

Deprecated

Removed

Fixed

  • Fixed filtering of pytorch "unsqueeze" warning when using DP (#5622)

  • Fixed num_classes argument in F1 metric (#5663)

  • Fixed support custom DataLoader with DDP if they can be re-instantiated (#5745)

  • Fixed a race condition in ModelCheckpoint when checking if a checkpoint file exists (#5144)

[1.1.6] - 2021-01-26

Changed

  • Increased TPU check timeout from 20s to 100s (#5598)
  • Ignored step param in Neptune logger's log_metric method (#5510)
  • Pass batch outputs to on_train_batch_end instead of epoch_end outputs (#4369)

Fixed

  • Fixed toggle_optimizer to reset requires_grad state (#5574)
  • Fixed FileNotFoundError for best checkpoint when using DDP with Hydra (#5629)
  • Fixed an error when logging a progress bar metric with a reserved name (#5620)
  • Fixed Metric's state_dict not included when child modules (#5614)
  • Fixed Neptune logger creating multiple experiments when GPUs > 1 (#3256)
  • Fixed duplicate logs appearing in console when using the python logging module (#5509)
  • Fixed tensor printing in trainer.test() (#5138)
  • Fixed not using dataloader when hparams present (#4559)

[1.1.5] - 2021-01-19

Fixed

  • Fixed a visual bug in the progress bar display initialization (#4579)
  • Fixed logging on_train_batch_end in a callback with multiple optimizers (#5521)
  • Fixed reinit_scheduler_properties with correct optimizer (#5519)
  • Fixed val_check_interval with fast_dev_run (#5540)

[1.1.4] - 2021-01-12

Added

  • Add automatic optimization property setter to lightning module (#5169)

Changed

  • Changed deprecated enable_pl_optimizer=True (#5244)

Fixed

  • Fixed transfer_batch_to_device for DDP with len(devices_ids) == 1 (#5195)
  • Logging only on not should_accumulate() during training (#5417)
  • Resolve interpolation bug with Hydra (#5406)
  • Check environ before selecting a seed to prevent warning message (#4743)
  • Fixed signature mismatch in model_to_device of DDPCPUHPCAccelerator (#5505)

[1.1.3] - 2021-01-05

Added

  • Added a check for optimizer attached to lr_scheduler (#5338)
  • Added support for passing non-existing filepaths to resume_from_checkpoint (#4402)

Changed

  • Skip restore from resume_from_checkpoint while testing (#5161)
  • Allowed log_momentum for adaptive optimizers in LearningRateMonitor (#5333)
  • Disabled checkpointing, earlystopping and logging with fast_dev_run (#5277)
  • Distributed group defaults to WORLD if None (#5125)

Fixed

  • Fixed trainer.test returning non-test metrics (#5214)
  • Fixed metric state reset (#5273)
  • Fixed --num-nodes on DDPSequentialPlugin (#5327)
  • Fixed invalid value for weights_summary (#5296)
  • Fixed Trainer.test not using the latest best_model_path (#5161)
  • Fixed existence check for hparams not using underlying filesystem (#5250)
  • Fixed LightningOptimizer AMP bug (#5191)
  • Fixed casted key to string in _flatten_dict (#5354)

[1.1.2] - 2020-12-23

Added

  • Support number for logging with sync_dist=True (#5080)
  • Added offset logging step when resuming for Wandb logger (#5050)

Removed

  • enable_pl_optimizer=False by default to temporarily fix AMP issues (#5163)

Fixed

  • Metric reduction with Logging (#5150)
  • Remove nan loss in manual optimization (#5121)
  • Un-balanced logging properly supported (#5119)
  • Fix hanging in DDP HPC accelerators (#5157)
  • Fix saved filename in ModelCheckpoint if it already exists (#4861)
  • Fix reset TensorRunningAccum (#5106)
  • Updated DALIClassificationLoader to not use deprecated arguments (#4925)
  • Corrected call to torch.no_grad (#5124)

[1.1.1] - 2020-12-15

Added

  • Add a notebook example to reach a quick baseline of ~94% accuracy on CIFAR10 using Resnet in Lightning (#4818)

Changed

  • Simplify accelerator steps (#5015)
  • Refactor load in checkpoint connector (#4593)
  • Fixed the saved filename in ModelCheckpoint when it already exists (#4861)

Removed

  • Drop duplicate metrics (#5014)
  • Remove beta arg from F1 class and functional (#5076)

Fixed

  • Fixed trainer by default None in DDPAccelerator (#4915)
  • Fixed LightningOptimizer to expose optimizer attributes (#5095)
  • Do not warn when the name key is used in the lr_scheduler dict (#5057)
  • Check if optimizer supports closure (#4981)
  • Extend LightningOptimizer to exposure underlying Optimizer attributes + update doc (#5095)
  • Add deprecated metric utility functions back to functional ( #5067, #5068)
  • Allow any input in to_onnx and to_torchscript (#4378)
  • Do not warn when the name key is used in the lr_scheduler dict (#5057)
  • Fixed DDPHPCAccelerator hangs in DDP construction by calling init_device (#5157)

[1.1.0] - 2020-12-09

Added

  • Added "monitor" key to saved ModelCheckpoints (#4383)
  • Added ConfusionMatrix class interface (#4348)
  • Added multiclass AUROC metric (#4236)
  • Added global step indexing to the checkpoint name for a better sub-epoch checkpointing experience (#3807)
  • Added optimizer hooks in callbacks (#4379)
  • Added option to log momentum (#4384)
  • Added current_score to ModelCheckpoint.on_save_checkpoint (#4721)
  • Added logging using self.log in train and evaluation for epoch end hooks ( #4552, #4495, #4439, #4684, #4913)
  • Added ability for DDP plugin to modify optimizer state saving (#4675)
  • Added casting to python types for numpy scalars when logging hparams (#4647)
  • Added prefix argument in loggers (#4557)
  • Added printing of total num of params, trainable and non-trainable params in ModelSummary (#4521)
  • Added PrecisionRecallCurve, ROC, AveragePrecision class metric (#4549)
  • Added custom Apex and NativeAMP as Precision plugins (#4355)
  • Added DALI MNIST example (#3721)
  • Added sharded plugin for DDP for multi-gpu training memory optimizations ( #4639, #4686, #4675, #4737, #4773)
  • Added experiment_id to the NeptuneLogger (#3462)
  • Added Pytorch Geometric integration example with Lightning (#4568)
  • Added all_gather method to LightningModule which allows gradient based tensor synchronizations for use-cases such as negative sampling. (#5012)
  • Enabled self.log in most functions (#4969)
  • Added changeable extension variable for ModelCheckpoint (#4977)

Changed

  • Tuner algorithms will be skipped if fast_dev_run=True (#3903)
  • WandbLogger does not force wandb reinit arg to True anymore and creates a run only when needed (#4648)
  • Changed automatic_optimization to be a model attribute (#4602)
  • Changed Simple Profiler report to order by percentage time spent + num calls (#4880)
  • Simplify optimization Logic (#4984)
  • Classification metrics overhaul (#4837)
  • Updated fast_dev_run to accept integer representing num_batches (#4629)
  • Refactored optimizer (#4658)

Deprecated

  • Deprecated prefix argument in ModelCheckpoint (#4765)
  • Deprecated the old way of assigning hyper-parameters through self.hparams = ... (#4813)
  • Deprecated mode='auto' from ModelCheckpoint and EarlyStopping (#4695)

Removed

  • Removed reorder parameter of the auc metric (#5004)
  • Removed multiclass_roc and multiclass_precision_recall_curve, use roc and precision_recall_curve instead (#4549)

Fixed

  • Added feature to move tensors to CPU before saving (#4309)
  • Fixed LoggerConnector to have logged metrics on root device in DP (#4138)
  • Auto convert tensors to contiguous format when gather_all (#4907)
  • Fixed PYTHONPATH for ddp test model (#4528)
  • Fixed allowing logger to support indexing (#4595)
  • Fixed DDP and manual_optimization (#4976)

[1.0.8] - 2020-11-24

Added

  • Added casting to python types for numpy scalars when logging hparams (#4647)
  • Added warning when progress bar refresh rate is less than 20 on Google Colab to prevent crashing (#4654)
  • Added F1 class metric (#4656)

Changed

  • Consistently use step=trainer.global_step in LearningRateMonitor independently of logging_interval (#4376)
  • Metric states are no longer as default added to state_dict (#4685)
  • Renamed class metric Fbeta >> FBeta (#4656)
  • Model summary: add 1 decimal place (#4745)
  • Do not override PYTHONWARNINGS (#4700)
  • Changed init_ddp_connection moved from DDP to DDPPlugin (#4407)

Fixed

  • Fixed checkpoint hparams dict casting when omegaconf is available (#4770)
  • Fixed incomplete progress bars when total batches not divisible by refresh rate (#4577)
  • Updated SSIM metric (#4566)(#4656)
  • Fixed batch_arg_name - add batch_arg_name to all calls to _adjust_batch_sizebug (#4812)
  • Fixed torchtext data to GPU (#4785)
  • Fixed a crash bug in MLFlow logger (#4716)

[1.0.7] - 2020-11-17

Added

  • Added lambda closure to manual_optimizer_step (#4618)

Changed

  • Change Metrics persistent default mode to False (#4685)
  • LoggerConnector log_metrics will use total_batch_idx instead of global_step when logging on training step (#4738)

Fixed

  • Prevent crash if sync_dist=True on CPU (#4626)
  • Fixed average pbar Metrics (#4534)
  • Fixed setup callback hook to correctly pass the LightningModule through (#4608)
  • Allowing decorate model init with saving hparams inside (#4662)
  • Fixed split_idx set by LoggerConnector in on_trainer_init to Trainer (#4697)

[1.0.6] - 2020-11-11

Added

  • Added metrics aggregation in Horovod and fixed early stopping (#3775)
  • Added manual_optimizer_step which work with AMP Native and accumulated_grad_batches (#4485)
  • Added persistent(mode) method to metrics, to enable and disable metric states being added to state_dict (#4482)
  • Added congratulations at the end of our notebooks (#4555)
  • Added parameters move_metrics_to_cpu in Trainer to disable gpu leak (#4592)

Changed

Fixed

  • Fixed feature-lack in hpc_load (#4526)
  • Fixed metrics states being overridden in DDP mode (#4482)
  • Fixed lightning_getattr, lightning_hasattr not finding the correct attributes in datamodule (#4347)
  • Fixed automatic optimization AMP by manual_optimization_step (#4485)
  • Replace MisconfigurationException with warning in ModelCheckpoint Callback (#4560)
  • Fixed logged keys in mlflow logger (#4412)
  • Fixed is_picklable by catching AttributeError (#4508)
  • Fixed multi test dataloaders dict AttributeError error (#4480)
  • Fixed show progress bar only for progress_rank 0 on DDP_SLURM (#4437)

[1.0.5] - 2020-11-03

Added

  • Added PyTorch 1.7 Stable support (#3821)
  • Added timeout for tpu_device_exists to ensure process does not hang indefinitely (#4340)

Changed

  • W&B log in sync with Trainer step (#4405)
  • Hook on_after_backward is called only when optimizer_step is being called (#4439)
  • Moved track_and_norm_grad into training loop and called only when optimizer_step is being called (#4439)
  • Changed type checker with explicit cast of ref_model object (#4457)
  • Changed distributed_backend -> accelerator (#4429)

Deprecated

  • Deprecated passing ModelCheckpoint instance to checkpoint_callback Trainer argument (#4336)

Fixed

  • Disable saving checkpoints if not trained (#4372)
  • Fixed error using auto_select_gpus=True with gpus=-1 (#4209)
  • Disabled training when limit_train_batches=0 (#4371)
  • Fixed that metrics do not store computational graph for all seen data (#4313)
  • Fixed AMP unscale for on_after_backward (#4439)
  • Fixed TorchScript export when module includes Metrics (#4428)
  • Fixed TorchScript trace method's data to device and docstring (#4360)
  • Fixed CSV logger warning (#4419)
  • Fixed skip DDP parameter sync (#4301)
  • Fixed WandbLogger _sanitize_callable function (#4422)
  • Fixed AMP Native _unscale gradient (#4441)

[1.0.4] - 2020-10-27

Added

  • Added dirpath and filename parameter in ModelCheckpoint (#4213)
  • Added plugins docs and DDPPlugin to customize ddp across all accelerators (#4258)
  • Added strict option to the scheduler dictionary (#3586)
  • Added fsspec support for profilers (#4162)
  • Added autogenerated helptext to Trainer.add_argparse_args (#4344)
  • Added support for string values in Trainer's profiler parameter (#3656)
  • Added support for string values in Trainer's profiler parameter (#3656)
  • Added optimizer_closure to optimizer.step when supported (#4190)
  • Added unification of regression metrics (#4166)
  • Added checkpoint load from Bytes (#4314)

Changed

  • Improved error messages for invalid configure_optimizers returns (#3587)
  • Allow changing the logged step value in validation_step (#4130)
  • Allow setting replace_sampler_ddp=True with a distributed sampler already added (#4273)
  • Fixed santized parameters for WandbLogger.log_hyperparams (#4320)

Deprecated

  • Deprecated filepath in ModelCheckpoint (#4213)
  • Deprecated reorder parameter of the auc metric (#4237)
  • Deprecated bool values in Trainer's profiler parameter (#3656)

Fixed

  • Fixed setting device ids in DDP (#4297)
  • Fixed synchronization of best model path in ddp_accelerator (#4323)
  • Fixed WandbLogger not uploading checkpoint artifacts at the end of training (#4341)
  • Fixed FBeta computation (#4183)
  • Fixed accumulation across batches has completed before breaking training loop (#4278)
  • Fixed ModelCheckpoint don't increase current_epoch and global_step when not training (#4291)
  • Fixed COMET_EXPERIMENT_KEY environment variable usage in comet logger (#4230)

[1.0.3] - 2020-10-20

Added

  • Added persistent flag to Metric.add_state (#4195)

Changed

  • Used checkpoint_connector.hpc_save in SLURM (#4217)
  • Moved base req. to root (#4219)

Fixed

  • Fixed hparams assign in init (#4189)
  • Fixed overwrite check for model hooks (#4010)

[1.0.2] - 2020-10-15

Added

  • Added trace functionality to the function to_torchscript (#4142)

Changed

  • Called on_load_checkpoint before loading state_dict (#4057)

Removed

  • Removed duplicate metric vs step log for train loop (#4173)

Fixed

  • Fixed the self.log problem in validation_step() (#4169)
  • Fixed hparams saving - save the state when save_hyperparameters() is called [in __init__] (#4163)
  • Fixed runtime failure while exporting hparams to yaml (#4158)

[1.0.1] - 2020-10-14

Added

  • Added getstate/setstate method for torch.save serialization (#4127)

[1.0.0] - 2020-10-13

Added

  • Added Explained Variance Metric + metric fix (#4013)
  • Added Metric <-> Lightning Module integration tests (#4008)
  • Added parsing OS env vars in Trainer (#4022)
  • Added classification metrics (#4043)
  • Updated explained variance metric (#4024)
  • Enabled plugins (#4041)
  • Enabled custom clusters (#4048)
  • Enabled passing in custom accelerators (#4050)
  • Added LightningModule.toggle_optimizer (#4058)
  • Added LightningModule.manual_backward (#4063)
  • Added output argument to *_batch_end hooks (#3965, #3966)
  • Added output argument to *_epoch_end hooks (#3967)

Changed

Removed

  • Removed support for EvalResult and TrainResult (#3968)
  • Removed deprecated trainer flags: overfit_pct, log_save_interval, row_log_interval (#3969)
  • Removed deprecated early_stop_callback (#3982)
  • Removed deprecated model hooks (#3980)
  • Removed deprecated callbacks (#3979)
  • Removed trainer argument in LightningModule.backward #4056)

Fixed

  • Fixed current_epoch property update to reflect true epoch number inside LightningDataModule, when reload_dataloaders_every_epoch=True. (#3974)
  • Fixed to print scaler value in progress bar (#4053)
  • Fixed mismatch between docstring and code regarding when on_load_checkpoint hook is called (#3996)

[0.10.0] - 2020-10-07

Added

  • Added new Metrics API. (#3868, #3921)
  • Enable PyTorch 1.7 compatibility (#3541)
  • Added LightningModule.to_torchscript to support exporting as ScriptModule (#3258)
  • Added warning when dropping unpicklable hparams (#2874)
  • Added EMB similarity (#3349)
  • Added ModelCheckpoint.to_yaml method (#3048)
  • Allow ModelCheckpoint monitor to be None, meaning it will always save (#3630)
  • Disabled optimizers setup during testing (#3059)
  • Added support for datamodules to save and load checkpoints when training (#3563)
  • Added support for datamodule in learning rate finder (#3425)
  • Added gradient clip test for native AMP (#3754)
  • Added dist lib to enable syncing anything across devices (#3762)
  • Added broadcast to TPUBackend (#3814)
  • Added XLADeviceUtils class to check XLA device type (#3274)

Changed

  • Refactored accelerator backends:
    • moved TPU xxx_step to backend (#3118)
    • refactored DDP backend forward (#3119)
    • refactored GPU backend __step (#3120)
    • refactored Horovod backend (#3121, #3122)
    • remove obscure forward call in eval + CPU backend ___step (#3123)
    • reduced all simplified forward (#3126)
    • added hook base method (#3127)
    • refactor eval loop to use hooks - use test_mode for if so we can split later (#3129)
    • moved ___step_end hooks (#3130)
    • training forward refactor (#3134)
    • training AMP scaling refactor (#3135)
    • eval step scaling factor (#3136)
    • add eval loop object to streamline eval loop (#3138)
    • refactored dataloader process hook (#3139)
    • refactored inner eval loop (#3141)
    • final inner eval loop hooks (#3154)
    • clean up hooks in run_evaluation (#3156)
    • clean up data reset (#3161)
    • expand eval loop out (#3165)
    • moved hooks around in eval loop (#3195)
    • remove _evaluate fx (#3197)
    • Trainer.fit hook clean up (#3198)
    • DDPs train hooks (#3203)
    • refactor DDP backend (#3204, #3207, #3208, #3209, #3210)
    • reduced accelerator selection (#3211)
    • group prepare data hook (#3212)
    • added data connector (#3285)
    • modular is_overridden (#3290)
    • adding Trainer.tune() (#3293)
    • move run_pretrain_routine -> setup_training (#3294)
    • move train outside of setup training (#3297)
    • move prepare_data to data connector (#3307)
    • moved accelerator router (#3309)
    • train loop refactor - moving train loop to own object (#3310, #3312, #3313, #3314)
    • duplicate data interface definition up into DataHooks class (#3344)
    • inner train loop (#3359, #3361, #3362, #3363, #3365, #3366, #3367, #3368, #3369, #3370, #3371, #3372, #3373, #3374, #3375, #3376, #3385, #3388, #3397)
    • all logging related calls in a connector (#3395)
    • device parser (#3400, #3405)
    • added model connector (#3407)
    • moved eval loop logging to loggers (#3408)
    • moved eval loop (#3412#3408)
    • trainer/separate argparse (#3421, #3428, #3432)
    • move lr_finder (#3434)
    • organize args (##3435, #3442, #3447, #3448, #3449, #3456)
    • move specific accelerator code (#3457)
    • group connectors (#3472)
    • accelerator connector methods x/n (#3469, #3470, #3474)
    • merge backends x/n (#3476, #3477, #3478, #3480, #3482)
    • apex plugin (#3502)
    • precision plugins (#3504)
    • Result - make monitor default to checkpoint_on to simplify (#3571)
    • reference to the Trainer on the LightningDataModule (#3684)
    • add .log to lightning module (#3686, #3699, #3701, #3704, #3715)
    • enable tracking original metric when step and epoch are both true (#3685)
    • deprecated results obj, added support for simpler comms (#3681)
    • move backends back to individual files (#3712)
    • fixes logging for eval steps (#3763)
    • decoupled DDP, DDP spawn (#3733, #3766, #3767, #3774, #3802, #3806)
    • remove weight loading hack for ddp_cpu (#3808)
    • separate torchelastic from DDP (#3810)
    • separate SLURM from DDP (#3809)
    • decoupled DDP2 (#3816)
    • bug fix with logging val epoch end + monitor (#3812)
    • decoupled DDP, DDP spawn (#3733, #3817, #3819, #3927)
    • callback system and init DDP (#3836)
    • adding compute environments (#3837, #3842)
    • epoch can now log independently (#3843)
    • test selecting the correct backend. temp backends while slurm and TorchElastic are decoupled (#3848)
    • fixed init_slurm_connection causing hostname errors (#3856)
    • moves init apex from LM to apex connector (#3923)
    • moves sync bn to each backend (#3925)
    • moves configure ddp to each backend (#3924)
  • Deprecation warning (#3844)
  • Changed LearningRateLogger to LearningRateMonitor (#3251)
  • Used fsspec instead of gfile for all IO (#3320)
    • Swaped torch.load for fsspec load in DDP spawn backend (#3787)
    • Swaped torch.load for fsspec load in cloud_io loading (#3692)
    • Added support for to_disk() to use remote filepaths with fsspec (#3930)
    • Updated model_checkpoint's to_yaml to use fsspec open (#3801)
    • Fixed fsspec is inconsistant when doing fs.ls (#3805)
  • Refactor GPUStatsMonitor to improve training speed (#3257)
  • Changed IoU score behavior for classes absent in target and pred (#3098)
  • Changed IoU remove_bg bool to ignore_index optional int (#3098)
  • Changed defaults of save_top_k and save_last to None in ModelCheckpoint (#3680)
  • row_log_interval and log_save_interval are now based on training loop's global_step instead of epoch-internal batch index (#3667)
  • Silenced some warnings. verified ddp refactors (#3483)
  • Cleaning up stale logger tests (#3490)
  • Allow ModelCheckpoint monitor to be None (#3633)
  • Enable None model checkpoint default (#3669)
  • Skipped best_model_path if checkpoint_callback is None (#2962)
  • Used raise .. from .. to explicitly chain exceptions (#3750)
  • Mocking loggers (#3596, #3617, #3851, #3859, #3884, #3853, #3910, #3889, #3926)
  • Write predictions in LightningModule instead of EvalResult #3882

Deprecated

  • 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)
  • Deprecate early_stop_callback Trainer argument (#3845)
  • Rename Trainer arguments row_log_interval >> log_every_n_steps and log_save_interval >> flush_logs_every_n_steps (#3748)

Removed

  • Removed experimental Metric API (#3868, #3943, #3949, #3946), listed changes before final removal:
    • Added EmbeddingSimilarity metric (#3349, #3358)
    • Added hooks to metric module interface (#2528)
    • Added error when AUROC metric is used for multiclass problems (#3350)
    • Fixed ModelCheckpoint with save_top_k=-1 option not tracking the best models when a monitor metric is available (#3735)
    • Fixed counter-intuitive error being thrown in Accuracy metric for zero target tensor (#3764)
    • Fixed aggregation of metrics (#3517)
    • Fixed Metric aggregation (#3321)
    • Fixed RMSLE metric (#3188)
    • Renamed reduction to class_reduction in classification metrics (#3322)
    • Changed class_reduction similar to sklearn for classification metrics (#3322)
    • Renaming of precision recall metric (#3308)

Fixed

  • Fixed on_train_batch_start hook to end epoch early (#3700)
  • Fixed num_sanity_val_steps is clipped to limit_val_batches (#2917)
  • Fixed ONNX model save on GPU (#3145)
  • Fixed GpuUsageLogger to work on different platforms (#3008)
  • Fixed auto-scale batch size not dumping auto_lr_find parameter (#3151)
  • Fixed batch_outputs with optimizer frequencies (#3229)
  • Fixed setting batch size in LightningModule.datamodule when using auto_scale_batch_size (#3266)
  • Fixed Horovod distributed backend compatibility with native AMP (#3404)
  • Fixed batch size auto scaling exceeding the size of the dataset (#3271)
  • Fixed getting experiment_id from MLFlow only once instead of each training loop (#3394)
  • Fixed overfit_batches which now correctly disables shuffling for the training loader. (#3501)
  • Fixed gradient norm tracking for row_log_interval > 1 (#3489)
  • Fixed ModelCheckpoint name formatting (3164)
  • Fixed auto-scale batch size (#3151)
  • Fixed example implementation of AutoEncoder (#3190)
  • Fixed invalid paths when remote logging with TensorBoard (#3236)
  • Fixed change t() to transpose() as XLA devices do not support .t() on 1-dim tensor (#3252)
  • Fixed (weights only) checkpoints loading without PL (#3287)
  • Fixed gather_all_tensors cross GPUs in DDP (#3319)
  • Fixed CometML save dir (#3419)
  • Fixed forward key metrics (#3467)
  • Fixed normalize mode at confusion matrix (replace NaNs with zeros) (#3465)
  • Fixed global step increment in training loop when training_epoch_end hook is used (#3673)
  • Fixed dataloader shuffling not getting turned off with overfit_batches > 0 and distributed_backend = "ddp" (#3534)
  • Fixed determinism in DDPSpawnBackend when using seed_everything in main process (#3335)
  • Fixed ModelCheckpoint period to actually save every period epochs (#3630)
  • Fixed val_progress_bar total with num_sanity_val_steps (#3751)
  • Fixed Tuner dump: add current_epoch to dumped_params (#3261)
  • Fixed current_epoch and global_step properties mismatch between Trainer and LightningModule (#3785)
  • Fixed learning rate scheduler for optimizers with internal state (#3897)
  • Fixed tbptt_reduce_fx when non-floating tensors are logged (#3796)
  • Fixed model checkpoint frequency (#3852)
  • Fixed logging non-tensor scalar with result breaks subsequent epoch aggregation (#3855)
  • Fixed TrainerEvaluationLoopMixin activates model.train() at the end (#3858)
  • Fixed overfit_batches when using with multiple val/test_dataloaders (#3857)
  • Fixed enables training_step to return None (#3862)
  • Fixed init nan for checkpointing (#3863)
  • Fixed for load_from_checkpoint (#2776)
  • Fixes incorrect batch_sizes when Dataloader returns a dict with multiple tensors (#3668)
  • Fixed unexpected signature for validation_step (#3947)

[0.9.0] - 2020-08-20

Added

  • Added SyncBN for DDP (#2801, #2838)
  • Added basic CSVLogger (#2721)
  • Added SSIM metrics (#2671)
  • Added BLEU metrics (#2535)
  • Added support to export a model to ONNX format (#2596)
  • Added support for Trainer(num_sanity_val_steps=-1) to check all validation data before training (#2246)
  • Added struct. output:
    • tests for val loop flow (#2605)
    • EvalResult support for train and val. loop (#2615, #2651)
    • weighted average in results obj (#2930)
    • fix result obj DP auto reduce (#3013)
  • Added class LightningDataModule (#2668)
  • Added support for PyTorch 1.6 (#2745)
  • Added call DataModule hooks implicitly in trainer (#2755)
  • Added support for Mean in DDP Sync (#2568)
  • Added remaining sklearn metrics: AveragePrecision, BalancedAccuracy, CohenKappaScore, DCG, Hamming, Hinge, Jaccard, MeanAbsoluteError, MeanSquaredError, MeanSquaredLogError, MedianAbsoluteError, R2Score, MeanPoissonDeviance, MeanGammaDeviance, MeanTweedieDeviance, ExplainedVariance (#2562)
  • Added support for limit_{mode}_batches (int) to work with infinite dataloader (IterableDataset) (#2840)
  • Added support returning python scalars in DP (#1935)
  • Added support to Tensorboard logger for OmegaConf hparams (#2846)
  • Added tracking of basic states in Trainer (#2541)
  • Tracks all outputs including TBPTT and multiple optimizers (#2890)
  • Added GPU Usage Logger (#2932)
  • Added strict=False for load_from_checkpoint (#2819)
  • Added saving test predictions on multiple GPUs (#2926)
  • Auto log the computational graph for loggers that support this (#3003)
  • Added warning when changing monitor and using results obj (#3014)
  • Added a hook transfer_batch_to_device to the LightningDataModule (#3038)

Changed

  • Truncated long version numbers in progress bar (#2594)
  • Enabling val/test loop disabling (#2692)
  • Refactored into accelerator module:
    • GPU training (#2704)
    • TPU training (#2708)
    • DDP(2) backend (#2796)
    • Retrieve last logged val from result by key (#3049)
  • Using .comet.config file for CometLogger (#1913)
  • Updated hooks arguments - breaking for setup and teardown (#2850)
  • Using gfile to support remote directories (#2164)
  • Moved optimizer creation after device placement for DDP backends (#2904)
  • Support **DictConfig for hparam serialization (#2519)
  • Removed callback metrics from test results obj (#2994)
  • Re-enabled naming metrics in ckpt name (#3060)
  • Changed progress bar epoch counting to start from 0 (#3061)

Deprecated

  • Deprecated Trainer attribute ckpt_path, which will now be set by weights_save_path (#2681)

Removed

  • Removed deprecated: (#2760)
    • core decorator data_loader
    • Module hook on_sanity_check_start and loading load_from_metrics
    • package pytorch_lightning.logging
    • Trainer arguments: show_progress_bar, num_tpu_cores, use_amp, print_nan_grads
    • LR Finder argument num_accumulation_steps

Fixed

  • Fixed accumulate_grad_batches for last batch (#2853)
  • Fixed setup call while testing (#2624)
  • Fixed local rank zero casting (#2640)
  • Fixed single scalar return from training (#2587)
  • Fixed Horovod backend to scale LR schedlers with the optimizer (#2626)
  • Fixed dtype and device properties not getting updated in submodules (#2657)
  • Fixed fast_dev_run to run for all dataloaders (#2581)
  • Fixed save_dir in loggers getting ignored by default value of weights_save_path when user did not specify weights_save_path (#2681)
  • Fixed weights_save_path getting ignored when logger=False is passed to Trainer (#2681)
  • Fixed TPU multi-core and Float16 (#2632)
  • Fixed test metrics not being logged with LoggerCollection (#2723)
  • Fixed data transfer to device when using torchtext.data.Field and include_lengths is True (#2689)
  • Fixed shuffle argument for distributed sampler (#2789)
  • Fixed logging interval (#2694)
  • Fixed loss value in the progress bar is wrong when accumulate_grad_batches > 1 (#2738)
  • Fixed correct CWD for ddp sub-processes when using Hydra (#2719)
  • Fixed selecting GPUs using CUDA_VISIBLE_DEVICES (#2739, #2796)
  • Fixed false num_classes warning in metrics (#2781)
  • Fixed shell injection vulnerability in subprocess call (#2786)
  • Fixed LR finder and hparams compatibility (#2821)
  • Fixed ModelCheckpoint not saving the latest information when save_last=True (#2881)
  • Fixed ImageNet example: learning rate scheduler, number of workers and batch size when using DDP (#2889)
  • Fixed apex gradient clipping (#2829)
  • Fixed save apex scaler states (#2828)
  • Fixed a model loading issue with inheritance and variable positional arguments (#2911)
  • Fixed passing non_blocking=True when transferring a batch object that does not support it (#2910)
  • Fixed checkpointing to remote file paths (#2925)
  • Fixed adding val step argument to metrics (#2986)
  • Fixed an issue that caused Trainer.test() to stall in ddp mode (#2997)
  • Fixed gathering of results with tensors of varying shape (#3020)
  • Fixed batch size auto-scaling feature to set the new value on the correct model attribute (#3043)
  • Fixed automatic batch scaling not working with half precision (#3045)
  • Fixed setting device to root gpu (#3042)

[0.8.5] - 2020-07-09

Added

  • Added a PSNR metric: peak signal-to-noise ratio (#2483)
  • Added functional regression metrics (#2492)

Removed

  • Removed auto val reduce (#2462)

Fixed

  • Flattening Wandb Hyperparameters (#2459)
  • Fixed using the same DDP python interpreter and actually running (#2482)
  • Fixed model summary input type conversion for models that have input dtype different from model parameters (#2510)
  • Made TensorBoardLogger and CometLogger pickleable (#2518)
  • Fixed a problem with MLflowLogger creating multiple run folders (#2502)
  • Fixed global_step increment (#2455)
  • Fixed TPU hanging example (#2488)
  • Fixed argparse default value bug (#2526)
  • Fixed Dice and IoU to avoid NaN by adding small eps (#2545)
  • Fixed accumulate gradients schedule at epoch 0 (continued) (#2513)
  • Fixed Trainer .fit() returning last not best weights in "ddp_spawn" (#2565)
  • Fixed passing (do not pass) TPU weights back on test (#2566)
  • Fixed DDP tests and .test() (#2512, #2570)

[0.8.4] - 2020-07-01

Added

  • Added reduce ddp results on eval (#2434)
  • Added a warning when an IterableDataset has __len__ defined (#2437)

Changed

  • Enabled no returns from eval (#2446)

Fixed

  • Fixes train outputs (#2428)
  • Fixes Conda dependencies (#2412)
  • Fixed Apex scaling with decoupled backward (#2433)
  • Fixed crashing or wrong displaying progressbar because of missing ipywidgets (#2417)
  • Fixed TPU saving dir (fc26078e, 04e68f02)
  • Fixed logging on rank 0 only (#2425)

[0.8.3] - 2020-06-29

Fixed

[0.8.2] - 2020-06-28

Added

  • Added TorchText support for moving data to GPU (#2379)

Changed

  • Changed epoch indexing from 0 instead of 1 (#2289)
  • Refactor Model backward (#2276)
  • Refactored training_batch + tests to verify correctness (#2327, #2328)
  • Refactored training loop (#2336)
  • Made optimization steps for hooks (#2363)
  • Changed default apex level to 'O2' (#2362)

Removed

  • Moved TrainsLogger to Bolts (#2384)

Fixed

  • Fixed parsing TPU arguments and TPU tests (#2094)
  • Fixed number batches in case of multiple dataloaders and limit_{*}_batches (#1920, #2226)
  • Fixed an issue with forward hooks not being removed after model summary (#2298)
  • Fix for load_from_checkpoint() not working with absolute path on Windows (#2294)
  • Fixed an issue how _has_len handles NotImplementedError e.g. raised by torchtext.data.Iterator (#2293), (#2307)
  • Fixed average_precision metric (#2319)
  • Fixed ROC metric for CUDA tensors (#2304)
  • Fixed average_precision metric (#2319)
  • Fixed lost compatibility with custom datatypes implementing .to (#2335)
  • Fixed loading model with kwargs (#2387)
  • Fixed sum(0) for trainer.num_val_batches (#2268)
  • Fixed checking if the parameters are a DictConfig Object (#2216)
  • Fixed SLURM weights saving (#2341)
  • Fixed swaps LR scheduler order (#2356)
  • Fixed adding tensorboard hparams logging test (#2342)
  • Fixed use model ref for tear down (#2360)
  • Fixed logger crash on DDP (#2388)
  • Fixed several issues with early stopping and checkpoint callbacks (#1504, #2391)
  • Fixed loading past checkpoints from v0.7.x (#2405)
  • Fixed loading model without arguments (#2403)
  • Fixed Windows compatibility issue (#2358)

[0.8.1] - 2020-06-19

Fixed

  • Fixed the load_from_checkpoint path detected as URL bug (#2244)
  • Fixed hooks - added barrier (#2245, #2257, #2260)
  • Fixed hparams - remove frame inspection on self.hparams (#2253)
  • Fixed setup and on fit calls (#2252)
  • Fixed GPU template (#2255)

[0.8.0] - 2020-06-18

Added

  • Added overfit_batches, limit_{val|test}_batches flags (overfit now uses training set for all three) (#2213)
  • Added metrics
  • Added type hints in Trainer.fit() and Trainer.test() to reflect that also a list of dataloaders can be passed in (#1723)
  • Allow dataloaders without sampler field present (#1907)
  • Added option save_last to save the model at the end of every epoch in ModelCheckpoint (#1908)
  • Early stopping checks on_validation_end (#1458)
  • Attribute best_model_path to ModelCheckpoint for storing and later retrieving the path to the best saved model file (#1799)
  • Speed up single-core TPU training by loading data using ParallelLoader (#2033)
  • Added a model hook transfer_batch_to_device that enables moving custom data structures to the target device (1756)
  • Added black formatter for the code with code-checker on pull (1610)
  • Added back the slow spawn ddp implementation as ddp_spawn (#2115)
  • Added loading checkpoints from URLs (#1667)
  • Added a callback method on_keyboard_interrupt for handling KeyboardInterrupt events during training (#2134)
  • Added a decorator auto_move_data that moves data to the correct device when using the LightningModule for inference (#1905)
  • Added ckpt_path option to LightningModule.test(...) to load particular checkpoint (#2190)
  • Added setup and teardown hooks for model (#2229)

Changed

  • Allow user to select individual TPU core to train on (#1729)
  • Removed non-finite values from loss in LRFinder (#1862)
  • Allow passing model hyperparameters as complete kwarg list (#1896)
  • Renamed ModelCheckpoint's attributes best to best_model_score and kth_best_model to kth_best_model_path (#1799)
  • Re-Enable Logger's ImportErrors (#1938)
  • Changed the default value of the Trainer argument weights_summary from full to top (#2029)
  • Raise an error when lightning replaces an existing sampler (#2020)
  • Enabled prepare_data from correct processes - clarify local vs global rank (#2166)
  • Remove explicit flush from tensorboard logger (#2126)
  • Changed epoch indexing from 1 instead of 0 (#2206)

Deprecated

  • Deprecated flags: (#2213)
    • overfit_pct in favour of overfit_batches
    • val_percent_check in favour of limit_val_batches
    • test_percent_check in favour of limit_test_batches
  • Deprecated ModelCheckpoint's attributes best and kth_best_model (#1799)
  • Dropped official support/testing for older PyTorch versions <1.3 (#1917)
  • Deprecated Trainer proc_rank in favour of global_rank (#2166, #2269)

Removed

  • Removed unintended Trainer argument progress_bar_callback, the callback should be passed in by Trainer(callbacks=[...]) instead (#1855)
  • Removed obsolete self._device in Trainer (#1849)
  • Removed deprecated API (#2073)
    • Packages: pytorch_lightning.pt_overrides, pytorch_lightning.root_module
    • Modules: pytorch_lightning.logging.comet_logger, pytorch_lightning.logging.mlflow_logger, pytorch_lightning.logging.test_tube_logger, pytorch_lightning.overrides.override_data_parallel, pytorch_lightning.core.model_saving, pytorch_lightning.core.root_module
    • Trainer arguments: add_row_log_interval, default_save_path, gradient_clip, nb_gpu_nodes, max_nb_epochs, min_nb_epochs, nb_sanity_val_steps
    • Trainer attributes: nb_gpu_nodes, num_gpu_nodes, gradient_clip, max_nb_epochs, min_nb_epochs, nb_sanity_val_steps, default_save_path, tng_tqdm_dic

Fixed

  • Run graceful training teardown on interpreter exit (#1631)
  • Fixed user warning when apex was used together with learning rate schedulers (#1873)
  • Fixed multiple calls of EarlyStopping callback (#1863)
  • Fixed an issue with Trainer.from_argparse_args when passing in unknown Trainer args (#1932)
  • Fixed bug related to logger not being reset correctly for model after tuner algorithms (#1933)
  • Fixed root node resolution for SLURM cluster with dash in host name (#1954)
  • Fixed LearningRateLogger in multi-scheduler setting (#1944)
  • Fixed test configuration check and testing (#1804)
  • Fixed an issue with Trainer constructor silently ignoring unknown/misspelled arguments (#1820)
  • Fixed save_weights_only in ModelCheckpoint (#1780)
  • Allow use of same WandbLogger instance for multiple training loops (#2055)
  • 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)
  • Fixed mistake in parameters' grad norm tracking (#2012)
  • Fixed CPU and hanging GPU crash (#2118)
  • Fixed an issue with the model summary and example_input_array depending on a specific ordering of the submodules in a LightningModule (#1773)
  • Fixed Tpu logging (#2230)
  • Fixed Pid port + duplicate rank_zero logging (#2140, #2231)

[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)

[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] - 2019-07-25

[0.3.4] - 2019-07-22

[0.3.3] - 2019-07-22

[0.3.2] - 2019-07-21

[0.3.1] - 2019-07-21

[0.2.x] - 2019-07-09

[0.1.x] - 2019-06-DD