standard weekly patch release

Overview

Detail changes

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)

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)

Contributors

@Borda, @carmocca, @hemildesai, @rohitgr7, @s-rog, @tarepan, @tchaton

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Model Parallelism Training and More Logging Options

Overview

Lightning 1.1 is out! You can now train models with twice the parameters and zero code changes with the new sharded model training! We also have a new plugin for sequential model parallelism, more logging options, and a lot of improvements!
Release highlights: https://bit.ly/3gyLZpP

Learn more about sharded training: https://bit.ly/2W3hgI0

Detail changes

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 (#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 (#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

• Removed multiclass_roc and multiclass_precision_recall_curve, use roc and precision_recall_curve instead (#4549)
• 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)

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)

Contributors

@ananyahjha93, @awaelchli, @blatr, @Borda, @borisdayma, @carmocca, @ddrevicky, @george-gca, @gianscarpe, @irustandi, @janhenriklambrechts, @jeremyjordan, @justusschock, @lezwon, @rohitgr7, @s-rog, @SeanNaren, @SkafteNicki, @tadejsv, @tchaton, @williamFalcon, @zippeurfou

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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).