PyTorch-Ignite 0.4.0 RC - Release Notes

Core

BC breaking changes

• Simplified engine - BC breaking change (#940 #939 #938)
  • no more internal patching of torch DataLoader.
  • seed argument of Engine.run is deprecated.
  • previous behaviour can be achieved with DeterministicEngine, introduced in #939.
• Make all Events be CallableEventsWithFilter (#788).
• Make ignite compatible only with pytorch >1.0 (#1016).
  • ignite is tested on the latest and nightly versions of pytorch.
  • exact compatibility with previous versions can be checked here.
• Remove deprecated arguments from BaseLogger (#1051).
• Deprecated CustomPeriodicEvent (#984).
• RunningAverage now computes output quantity average instead of a sum in DDP (#991).
• Checkpoint stores now files with .pt extension instead of .pth (#873).
• Arguments archived of Checkpoint and ModelCheckpoint are deprecated (#873).
• Now create_supervised_trainer and create_supervised_evaluator do not move model to device (#910).

New Features and bug fixes

Ignite Distributed [Experimental]

• Introduction of ignite.distributed as idist module (#1045)
  • common interface for distributed applications and helper methods, e.g. get_world_size(), get_rank(), ...
  • supports native torch distributed configuration, XLA devices.
  • metrics computation works in all supported distributed configurations: GPUs and TPUs.

Engine & Events

• Add flexibility on event handlers by packing triggering events (#868).
• Engine argument is now optional in event handlers (#889, #919).
• We initialize engine.state before calling engine.run (#1028).
• Engine can run on dataloader based on IterableDataset and without specifying epoch_length (#1077).
• Added user keys into Engine's state dict (#914).
• Bug fixes in Engine class (#1048, #994).
• Now epoch_length argument is optional (#985)
  • suitable to work with finite-unknown-length iterators.
• Added times in engine.state (#958).

Metrics

• Add Frequency metric for ops/s calculations (#760, #783, #976).
• Metrics computation can be customized with introduced MetricUsage (#979, #1054)
  • batch-wise/epoch-wise or customly programmed metric's update and compute methods.
• Metric can be detached (#827).
• Fixed bug in RunningAverage when output is torch tensor (#943).
• Improved computation performance of EpochMetric (#967).
• Fixed average recall value of ConfusionMatrix (#846).
• Now metrics can be serialized using dill (#930).
• Added support for nested metric values (#968).

Handlers and utils

• Checkpoint : improved filename when score value is Integer (#758).
• Checkpoint : fix returning worst model of the saved models. (#745).
• Checkpoint : load_objects can load single object checkpoints (#772).
• Checkpoint : we now save only one checkpoint per priority (#847).
• Checkpoint : added kwargs to Checkpoint.load_objects (#861).
• Checkpoint : now saves model.module.state_dict() for DDP and DP (#1086).
• Checkpoint and related: other improvements (#937).
• Support namedtuple for convert_tensor (#740).
• Added decorator one_rank_only (#882).
• Update common.py (#904).

Contrib

• Added FastaiLRFinder (#596).

Metrics

• Added Roc Curve and Precision/Recall Curve to the metrics (#875).

Parameters scheduling

• Enabled multi params group for LRScheduler (#1027).
• Parameters scheduling improvements (#1072, #859).

Support of experiment tracking systems

• Add NeptuneLogger (#730, #821, #951, #954).
• Add TrainsLogger (#1020, #1036, #1043).
• Add WandbLogger (#926).
• Added visdom_logger to common module (#796).
• TensorboardX is no longer mandatory if pytorch>=1.2 (#858).
• Simplified BaseLogger attach APIs (#1006).
• Added kwargs to loggers' constructors and respective setup functions (#1015).

Time profiling

• Added basic time profiler to contrib.handlers (#729).

Bug fixes (some of PRs)

• ProgressBar output not in sync with epoch counts (#773).
• Fixed ProgressBar.log_message (#768).
• Progressbar now accounts for epoch_length argument (#785).
• Fixed broken ProgressBar if data is iterator without epoch length (#995).
• Improved setup_logger for multiple calls (#962).
• Fixed incorrect log position (#1099).
• Added missing colon to logging message (#1101).

Examples

• Basic example of FastaiLRFinder on MNIST (#838).
• CycleGAN auto-mixed precision training example with NVidia/Apex or native torch.cuda.amp (#888).
• Added setup_logger to mnist examples (#953).
• Added MNIST example on TPU (#956).
• Benchmark amp on Cifar100 (#917).
• TrainsLogger semantic segmentation example (#1095).

Housekeeping (some of PRs)

• Documentation updates (#711, #727, #734, #736, #742, #743, #759, #798, #780, #808, #817, #826, #867, #877, #908, #909, #911, #928, #942, #986, #989, #1002, #1031, #1035, #1083, #1092).
• Offerings to the CI gods (#713, #761, #762, #776, #791, #801, #803, #879, #885, #890, #894, #933, #981, #982, #1010, #1026, #1046, #1084, #1093).
• Test improvements (#779, #807, #854, #891, #975, #1021, #1033, #1041, #1058).
• Added Serializable in mixins (#1000).
• Merge of EpochMetric in _BaseRegressionEpoch (#970).
• Adding typing to ignite (#716, #751, #800, #844, #944, #1037).
• Drop Python 2 support finalized (#806).
• Dynamic typing (#723).
• Splits engine into multiple parts (#724).
• Add Python 3.8 to Conda builds (#781).
• Black formatted codebase with pre-commit files (#792).
• Activate dpl v2 for Travis CI (#804).
• AutoPEP8 (#805).
• Fixes nightly version bug (#809).
• Fixed device conversion method (#887).
• Refactored deps installation (#931).
• Return handler in helpers (#997).
• Fixes #833 (#1001).
• Disable propagation of loggers to ancestrors (#1013).
• Consistent PEP8-compliant imports layout (#901).

Acknowledgments

🎉 Thanks to our community and all our contributors for the issues, PRs and 🌟 ⭐️ 🌟 !
💯 We really appreciate your implication into the project (in alphabetical order):

@Crissman, @DhDeepLIT, @GabrielePicco, @InCogNiTo124, @ItamarWilf, @Joxis, @Muhamob, @Yevgnen, @anmolsjoshi, @bendboaz, @bmartinn, @cajanond, @chm90, @cqql, @czotti, @erip, @fdlm, @hoangmit, @Isolet, @jakubczakon, @jkhenning, @kai-tub, @maxfrei750, @michiboo, @mkartik, @sdesrozis, @sisp, @vfdev-5, @willfrey, @xen0f0n, @y0ast, @ykumards