Update CHANGELOG after the 1.6.4 release (#13201)
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Carlos Mocholí
GitHub
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CHANGELOG.md
76
CHANGELOG.md
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@ -63,9 +63,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
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- Added CPU metric tracking to `DeviceStatsMonitor` ([#11795](https://github.com/PyTorchLightning/pytorch-lightning/pull/11795))
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- Added all DDP params to be exposed through hpu parallel strategy ([#13067](https://github.com/PyTorchLightning/pytorch-lightning/pull/13067))
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- Added `teardown()` method to `Accelerator` ([#11935](https://github.com/PyTorchLightning/pytorch-lightning/pull/11935))
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### Changed
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@ -102,12 +99,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
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- Changed `pytorch_lightning.core.lightning` to `pytorch_lightning.core.module` ([#12740](https://github.com/PyTorchLightning/pytorch-lightning/pull/12740))
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- Keep `torch.backends.cudnn.benchmark=False` by default (unlike in v1.6.{0-4}) after speed and memory problems depending on the data used. Please consider tuning `Trainer(benchmark)` manually. ([#13154](https://github.com/PyTorchLightning/pytorch-lightning/pull/13154))
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- Prevent modification of `torch.backends.cudnn.benchmark` when `Trainer(benchmark=...)` is not set ([#13154](https://github.com/PyTorchLightning/pytorch-lightning/pull/13154))
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### Deprecated
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- Deprecated `pytorch_lightning.loggers.base.LightningLoggerBase` in favor of `pytorch_lightning.loggers.logger.Logger`, and deprecated `pytorch_lightning.loggers.base` in favor of `pytorch_lightning.loggers.logger` ([#120148](https://github.com/PyTorchLightning/pytorch-lightning/pull/12014))
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@ -214,57 +205,46 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
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### Fixed
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- Fixed an issue causing zero-division error for empty dataloaders ([#12885](https://github.com/PyTorchLightning/pytorch-lightning/pull/12885))
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- Fixed `Trainer(precision=64)` during evaluation which now uses the wrapped precision module ([#12983](https://github.com/PyTorchLightning/pytorch-lightning/pull/12983))
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- Fixed an issue to use wrapped `LightningModule` for evaluation during `trainer.fit` for `BaguaStrategy` ([#12983](https://github.com/PyTorchLightning/pytorch-lightning/pull/12983))
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- Fixed mismatching default values for the types of some arguments in the DeepSpeed and Fully-Sharded strategies which made the CLI unable to use them ([#12989](https://github.com/PyTorchLightning/pytorch-lightning/pull/12989))
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- Fixed an issue with unsupported torch.inference_mode() on hpu backends by making it use no_grad ([#13014](https://github.com/PyTorchLightning/pytorch-lightning/pull/13014))
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- Fixed `DDPStrategy` and `DDPSpawnStrategy` to initialize optimizers only after moving the module to the device ([#11952](https://github.com/PyTorchLightning/pytorch-lightning/pull/11952))
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- Fixed epoch logging on train epoch end ([#13025](https://github.com/PyTorchLightning/pytorch-lightning/pull/13025))
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- Fixed `materialize_module` setting a module's child recursively ([#12870](https://github.com/PyTorchLightning/pytorch-lightning/pull/12870))
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- Fixed the number of references of `LightningModule` so it can be deleted ([#12897](https://github.com/PyTorchLightning/pytorch-lightning/pull/12897))
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- The model wrapper returned by `LightningLite.setup()` now properly supports pass-through when looking up attributes ([#12597](https://github.com/PyTorchLightning/pytorch-lightning/pull/12597))
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- Avoid redundant callback restore warning while tuning ([#13026](https://github.com/PyTorchLightning/pytorch-lightning/pull/13026))
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- Fixed torchelastic detection with non-distributed installations ([#13142](https://github.com/PyTorchLightning/pytorch-lightning/pull/13142))
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- Fixed an issue wrt unnecessary usage of habana mixed precision package for fp32 types ([#13028](https://github.com/PyTorchLightning/pytorch-lightning/pull/13028))
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- Fixed issue where the CLI could not pass a `Profiler` to the `Trainer` ([#13084](https://github.com/PyTorchLightning/pytorch-lightning/pull/13084))
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- Fixed issue where the CLI fails with certain torch objects ([#13153](https://github.com/PyTorchLightning/pytorch-lightning/pull/13153))
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- Fixed logging's step values when multiple dataloaders are used during evaluation ([#12184](https://github.com/PyTorchLightning/pytorch-lightning/pull/12184))
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-
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## [1.6.4] - 2022-06-01
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### Added
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- Added all DDP params to be exposed through hpu parallel strategy ([#13067](https://github.com/PyTorchLightning/pytorch-lightning/pull/13067))
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### Changed
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- Keep `torch.backends.cudnn.benchmark=False` by default (unlike in v1.6.{0-4}) after speed and memory problems depending on the data used. Please consider tuning `Trainer(benchmark)` manually. ([#13154](https://github.com/PyTorchLightning/pytorch-lightning/pull/13154))
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- Prevent modification of `torch.backends.cudnn.benchmark` when `Trainer(benchmark=...)` is not set ([#13154](https://github.com/PyTorchLightning/pytorch-lightning/pull/13154))
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### Fixed
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- Fixed an issue causing zero-division error for empty dataloaders ([#12885](https://github.com/PyTorchLightning/pytorch-lightning/pull/12885))
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- Fixed mismatching default values for the types of some arguments in the DeepSpeed and Fully-Sharded strategies which made the CLI unable to use them ([#12989](https://github.com/PyTorchLightning/pytorch-lightning/pull/12989))
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- Avoid redundant callback restore warning while tuning ([#13026](https://github.com/PyTorchLightning/pytorch-lightning/pull/13026))
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- Fixed `Trainer(precision=64)` during evaluation which now uses the wrapped precision module ([#12983](https://github.com/PyTorchLightning/pytorch-lightning/pull/12983))
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- Fixed an issue to use wrapped `LightningModule` for evaluation during `trainer.fit` for `BaguaStrategy` ([#12983](https://github.com/PyTorchLightning/pytorch-lightning/pull/12983))
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- Fixed an issue wrt unnecessary usage of habana mixed precision package for fp32 types ([#13028](https://github.com/PyTorchLightning/pytorch-lightning/pull/13028))
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- Fixed the number of references of `LightningModule` so it can be deleted ([#12897](https://github.com/PyTorchLightning/pytorch-lightning/pull/12897))
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- Fixed `materialize_module` setting a module's child recursively ([#12870](https://github.com/PyTorchLightning/pytorch-lightning/pull/12870))
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- Fixed issue where the CLI could not pass a `Profiler` to the `Trainer` ([#13084](https://github.com/PyTorchLightning/pytorch-lightning/pull/13084))
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- Fixed torchelastic detection with non-distributed installations ([#13142](https://github.com/PyTorchLightning/pytorch-lightning/pull/13142))
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- Fixed logging's step values when multiple dataloaders are used during evaluation ([#12184](https://github.com/PyTorchLightning/pytorch-lightning/pull/12184))
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- Fixed epoch logging on train epoch end ([#13025](https://github.com/PyTorchLightning/pytorch-lightning/pull/13025))
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- Fixed `DDPStrategy` and `DDPSpawnStrategy` to initialize optimizers only after moving the module to the device ([#11952](https://github.com/PyTorchLightning/pytorch-lightning/pull/11952))
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## [1.6.3] - 2022-05-03
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### Fixed
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