standard weekly patch release

Detail changes

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)

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)

Contributors

@awaelchli, @jonashaag, @jungwhank, @M-Salti, @moi90, @pgagarinov, @s-rog, @Samyak2, @SkafteNicki, @teddykoker, @ydcjeff

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standard weekly patch release

Detail changes

Added

• Added lambda closure to manual_optimizer_step (#4618)

Changed

• Change Metrics persistent default mode to False (#4685)

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)

Contributors

@ananthsub, @Borda, @SeanNaren, @SkafteNicki, @tchaton

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standard weekly patch release

Detail changes

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)

Changed

• Changed fsspec to tuner (#4458)
• Unify sLURM/TorchElastic under backend plugin (#4578, #4580, #4581, #4582, #4583)

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)

Contributors

@dscarmo, @jtamir, @kazhang, @maxjeblick, @rohitgr7, @SkafteNicki, @tarepan, @tchaton, @tgaddair, @williamFalcon

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standard weekly patch release

Detail changes

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)

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 CSV logger warning (#4419)
• Fixed skip DDP parameter sync (#4301)

Contributors

@ananthsub, @awaelchli, @borisdayma, @carmocca, @justusschock, @lezwon, @rohitgr7, @SeanNaren, @SkafteNicki, @ssaru, @tchaton, @ydcjeff

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standard weekly patch release

Detail changes

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)

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)

Contributors

@ananthsub, @awaelchli, @carmocca, @ddrevicky, @louis-she, @mauvilsa, @rohitgr7, @SeanNaren, @tchaton

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standard weekly patch release

Detail changes

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)

Contributors

@Borda, @EspenHa, @teddykoker

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fixes a major logging bug for val in 1.0

Fixes the last major bugs for validation logging.
Also removes duplicate charts for metric / metric_loss.
Doing this minor release because correct validation metrics logging is critical.

Details changes

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)

Contributors

@Borda, @NumesSanguis, @rohitgr7, @williamFalcon

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minor jit fixes

Obligatory post 1.0 minor release. Main fix is to make Lightning module fully compatible with Jit (had some edge-cases we had not covered).

1.0.0 - General availability

Overview

...

Detail changes

• 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)

Changed

• Integrated metrics API with self.log (#3961)
• Decoupled Appex (#4052, #4054, #4055, #4056, #4058, #4060, #4061, #4062, #4063, #4064, #4065)
• Renamed all backends to Accelerator (#4066)
• Enabled manual returns (#4089)

Removed

• Removed output argument from *_batch_end hooks (#3965, #3966)
• Removed output argument from *_epoch_end hooks (#3967)
• 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)

Contributors

@ananyahjha93, @Borda, @edenlightning, @hbredin, @rohitgr7, @SkafteNicki, @teddykoker, @williamFalcon

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Buffer release before 1.0

This release is a buffer in case 1.0 breaks any compatibility for people who upgrade. 0.10.0 has all the bug fixes and features of 1.0 but is 100% backward compatible. The 1.0 release following in the next 24 hours.

Overview

The major changes are:

• Results objects are deprecated (we hated them too haha)
• This means dataflow and logging have been decoupled

To log:

def any_step(...):
   self.log('something', i_computed)

Separately, return whatever you want from methods:

def training_step(...):
  return loss

or

def training_step(...):
   return {'loss': loss, 'whatever': [1, 'want']}

Detail changes

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 (#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 (#363...