Lightning-AI/pytorch-lightning 1.1.0

Model Parallelism Training and More Logging Options

on GitHub

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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