Lightning

App

Added

- Added a possibility to set up basic authentication for Lightning apps (16105)

Changed

- The LoadBalancer now uses internal ip + port instead of URL exposed (16119)
- Added support for logging in different trainer stages with `DeviceStatsMonitor`
(16002)
- Changed `lightning_app.components.serve.gradio` to `lightning_app.components.serve.gradio_server` (16201)
- Made cluster creation/deletion async by default (16185)

Fixed

- Fixed not being able to run multiple lightning apps locally due to port collision (15819)
- Avoid `relpath` bug on Windows (16164)
- Avoid using the deprecated `LooseVersion` (16162)
- Porting fixes to autoscaler component (16249)
- Fixed a bug where `lightning login` with env variables would not correctly save the credentials (16339)

---

Fabric

Added

- Added `Fabric.launch()` to programmatically launch processes (e.g. in Jupyter notebook) (14992)
- Added the option to launch Fabric scripts from the CLI, without the need to wrap the code into the `run` method (14992)
- Added `Fabric.setup_module()` and `Fabric.setup_optimizers()` to support strategies that need to set up the model before an optimizer can be created (15185)
- Added support for Fully Sharded Data Parallel (FSDP) training in Lightning Lite (14967)
- Added `lightning_fabric.accelerators.find_usable_cuda_devices` utility function (16147)
- Added basic support for LightningModules (16048)
- Added support for managing callbacks via `Fabric(callbacks=...)` and emitting events through `Fabric.call()` (16074)
- Added Logger support (16121)
* Added `Fabric(loggers=...)` to support different Logger frameworks in Fabric
* Added `Fabric.log` for logging scalars using multiple loggers
* Added `Fabric.log_dict` for logging a dictionary of multiple metrics at once
* Added `Fabric.loggers` and `Fabric.logger` attributes to access the individual logger instances
* Added support for calling `self.log` and `self.log_dict` in a LightningModule when using Fabric
* Added access to `self.logger` and `self.loggers` in a LightningModule when using Fabric
- Added `lightning_fabric.loggers.TensorBoardLogger` (16121)
- Added `lightning_fabric.loggers.CSVLogger` (16346)
- Added support for a consistent `.zero_grad(set_to_none=...)` on the wrapped optimizer regardless of which strategy is used (16275)

Changed

- Renamed the class `LightningLite` to `Fabric` (15932, 15938)
- The `Fabric.run()` method is no longer abstract (14992)
- The `XLAStrategy` now inherits from `ParallelStrategy` instead of `DDPSpawnStrategy` (15838)
- Merged the implementation of `DDPSpawnStrategy` into `DDPStrategy` and removed `DDPSpawnStrategy` (14952)
- The dataloader wrapper returned from `.setup_dataloaders()` now calls `.set_epoch()` on the distributed sampler if one is used (16101)
- Renamed `Strategy.reduce` to `Strategy.all_reduce` in all strategies (16370)
- When using multiple devices, the strategy now defaults to "ddp" instead of "ddp_spawn" when none is set (16388)

Removed

- Removed support for FairScale's sharded training (`strategy='ddp_sharded'|'ddp_sharded_spawn'`). Use Fully-Sharded Data Parallel instead (`strategy='fsdp'`) (16329)

Fixed

- Restored sampling parity between PyTorch and Fabric dataloaders when using the `DistributedSampler` (16101)
- Fixes an issue where the error message wouldn't tell the user the real value that was passed through the CLI (16334)

---

PyTorch

Added
- Added support for native logging of `MetricCollection` with enabled compute groups (15580)
- Added support for custom artifact names in `pl.loggers.WandbLogger` (16173)
- Added support for DDP with `LRFinder` (15304)
- Added utilities to migrate checkpoints from one Lightning version to another (15237)
- Added support to upgrade all checkpoints in a folder using the `pl.utilities.upgrade_checkpoint` script (15333)
- Add an axes argument `ax` to the `.lr_find().plot()` to enable writing to a user-defined axes in a matplotlib figure (15652)
- Added `log_model` parameter to `MLFlowLogger` (9187)
- Added a check to validate that wrapped FSDP models are used while initializing optimizers (15301)
- Added a warning when `self.log(..., logger=True)` is called without a configured logger (15814)
- Added support for colossalai 0.1.11 (15888)
- Added `LightningCLI` support for optimizer and learning schedulers via callable type dependency injection (15869)
- Added support for activation checkpointing for the `DDPFullyShardedNativeStrategy` strategy (15826)
- Added the option to set `DDPFullyShardedNativeStrategy(cpu_offload=True|False)` via bool instead of needing to pass a configuration object (15832)
- Added info message for Ampere CUDA GPU users to enable tf32 matmul precision (16037)
- Added support for returning optimizer-like classes in `LightningModule.configure_optimizers` (16189)

Changed

- Switch from `tensorboard` to `tensorboardx` in `TensorBoardLogger` (15728)
- From now on, Lightning Trainer and `LightningModule.load_from_checkpoint` automatically upgrade the loaded checkpoint if it was produced in an old version of Lightning (15237)
- `Trainer.{validate,test,predict}(ckpt_path=...)` no longer restores the `Trainer.global_step` and `trainer.current_epoch` value from the checkpoints - From now on, only `Trainer.fit` will restore this value (15532)
- The `ModelCheckpoint.save_on_train_epoch_end` attribute is now computed dynamically every epoch, accounting for changes to the validation dataloaders (15300)
- The Trainer now raises an error if it is given multiple stateful callbacks of the same time with colliding state keys (15634)
- `MLFlowLogger` now logs hyperparameters and metrics in batched API calls (15915)
- Overriding the `on_train_batch_{start,end}` hooks in conjunction with taking a `dataloader_iter` in the `training_step` no longer errors out and instead shows a warning (16062)
- Move `tensorboardX` to extra dependencies. Use the `CSVLogger` by default (16349)
- Drop PyTorch 1.9 support (15347)

Deprecated

- Deprecated `description`, `env_prefix` and `env_parse` parameters in `LightningCLI.__init__` in favour of giving them through `parser_kwargs` (15651)
- Deprecated `pytorch_lightning.profiler` in favor of `pytorch_lightning.profilers` (16059)
- Deprecated `Trainer(auto_select_gpus=...)` in favor of `pytorch_lightning.accelerators.find_usable_cuda_devices` (16147)
- Deprecated `pytorch_lightning.tuner.auto_gpu_select.{pick_single_gpu,pick_multiple_gpus}` in favor of `pytorch_lightning.accelerators.find_usable_cuda_devices` (16147)
- `nvidia/apex` deprecation (16039)
* Deprecated `pytorch_lightning.plugins.NativeMixedPrecisionPlugin` in favor of `pytorch_lightning.plugins.MixedPrecisionPlugin`
* Deprecated the `LightningModule.optimizer_step(using_native_amp=...)` argument
* Deprecated the `Trainer(amp_backend=...)` argument
* Deprecated the `Trainer.amp_backend` property
* Deprecated the `Trainer(amp_level=...)` argument
* Deprecated the `pytorch_lightning.plugins.ApexMixedPrecisionPlugin` class
* Deprecates the `pytorch_lightning.utilities.enums.AMPType` enum
* Deprecates the `DeepSpeedPrecisionPlugin(amp_type=..., amp_level=...)` arguments
- `horovod` deprecation (16141)
* Deprecated `Trainer(strategy="horovod")`
* Deprecated the `HorovodStrategy` class
- Deprecated `pytorch_lightning.lite.LightningLite` in favor of `lightning.fabric.Fabric` (16314)
- `FairScale` deprecation (in favor of PyTorch's FSDP implementation) (16353)
* Deprecated the `pytorch_lightning.overrides.fairscale.LightningShardedDataParallel` class
* Deprecated the `pytorch_lightning.plugins.precision.fully_sharded_native_amp.FullyShardedNativeMixedPrecisionPlugin` class
* Deprecated the `pytorch_lightning.plugins.precision.sharded_native_amp.ShardedNativeMixedPrecisionPlugin` class
* Deprecated the `pytorch_lightning.strategies.fully_sharded.DDPFullyShardedStrategy` class
* Deprecated the `pytorch_lightning.strategies.sharded.DDPShardedStrategy` class
* Deprecated the `pytorch_lightning.strategies.sharded_spawn.DDPSpawnShardedStrategy` class

Removed

- Removed deprecated `pytorch_lightning.utilities.memory.get_gpu_memory_map` in favor of `pytorch_lightning.accelerators.cuda.get_nvidia_gpu_stats` (15617)
- Temporarily removed support for Hydra multi-run (15737)
- Removed deprecated `pytorch_lightning.profiler.base.AbstractProfiler` in favor of `pytorch_lightning.profilers.profiler.Profiler` (15637)
- Removed deprecated `pytorch_lightning.profiler.base.BaseProfiler` in favor of `pytorch_lightning.profilers.profiler.Profiler` (15637)
- Removed deprecated code in `pytorch_lightning.utilities.meta` (16038)
- Removed the deprecated `LightningDeepSpeedModule` (16041)
- Removed the deprecated `pytorch_lightning.accelerators.GPUAccelerator` in favor of `pytorch_lightning.accelerators.CUDAAccelerator` (16050)
- Removed the deprecated `pytorch_lightning.profiler.*` classes in favor of `pytorch_lightning.profilers` (16059)
- Removed the deprecated `pytorch_lightning.utilities.cli` module in favor of `pytorch_lightning.cli` (16116)
- Removed the deprecated `pytorch_lightning.loggers.base` module in favor of `pytorch_lightning.loggers.logger` (16120)
- Removed the deprecated `pytorch_lightning.loops.base` module in favor of `pytorch_lightning.loops.loop` (16142)
- Removed the deprecated `pytorch_lightning.core.lightning` module in favor of `pytorch_lightning.core.module` (16318)
- Removed the deprecated `pytorch_lightning.callbacks.base` module in favor of `pytorch_lightning.callbacks.callback` (16319)
- Removed the deprecated `Trainer.reset_train_val_dataloaders()` in favor of `Trainer.reset_{train,val}_dataloader` (16131)
- Removed support for `LightningCLI(seed_everything_default=None)` (16131)
- Removed support in LightningLite for FairScale's sharded training (`strategy='ddp_sharded'|'ddp_sharded_spawn'`). Use Fully-Sharded Data Parallel instead (`strategy='fsdp'`) (16329)

Fixed

- Enhanced `reduce_boolean_decision` to accommodate `any`-analogous semantics expected by the `EarlyStopping` callback (15253)
- Fixed the incorrect optimizer step synchronization when running across multiple TPU devices (16020)
- Fixed a type error when dividing the chunk size in the ColossalAI strategy (16212)
- Fixed bug where the ``interval`` key of the scheduler would be ignored during manual optimization, making the LearningRateMonitor callback fail to log the learning rate (16308)
- Fixed an issue with `MLFlowLogger` not finalizing correctly when status code 'finished' was passed (16340)

---

Contributors

1SAA, akihironitta, AlessioQuercia, awaelchli, bipinKrishnan, Borda, carmocca, dmitsf, erhoo82, ethanwharris, Forbu, hhsecond, justusschock, lantiga, lightningforever, Liyang90, manangoel99, mauvilsa, nicolai86, nohalon, rohitgr7, schmidt-jake, speediedan, yMayanand

_If we forgot someone due to not matching commit email with GitHub account, let us know :]_

App

Added

- Added partial support for fastapi `Request` annotation in `configure_api` handlers (16047)
- Added a nicer UI with URL and examples for the autoscaler component (16063)
- Enabled users to have more control over scaling out/in intervals (16093)
- Added more datatypes to the serving component (16018)
- Added `work.delete` method to delete the work (16103)
- Added `display_name` property to LightningWork for the cloud (16095)
- Added `ColdStartProxy` to the AutoScaler (16094)
- Added status endpoint, enable `ready` (16075)
- Implemented `ready` for components (16129)

Changed

- The default `start_method` for creating Work processes locally on macOS is now 'spawn' (previously 'fork') (16089)
- The utility `lightning.app.utilities.cloud.is_running_in_cloud` now returns `True` during the loading of the app locally when running with `--cloud` (16045)
- Updated Multinode Warning (16091)
- Updated app testing (16000)
- Changed overwrite to `True` (16009)
- Simplified messaging in cloud dispatch (16160)
- Added annotations endpoint (16159)

Fixed

- Fixed `PythonServer` messaging "Your app has started" (15989)
- Fixed auto-batching to enable batching for requests coming even after the batch interval but is in the queue (16110)
- Fixed a bug where `AutoScaler` would fail with min_replica=0 (16092
- Fixed a non-thread safe deepcopy in the scheduler (16114)
- Fixed HTTP Queue sleeping for 1 sec by default if no delta was found (16114)
- Fixed the endpoint info tab not showing up in the `AutoScaler` UI (16128)
- Fixed an issue where an exception would be raised in the logs when using a recent version of `streamlit` (16139)
- Fixed e2e tests (16146)


---

**Full Changelog**: https://github.com/Lightning-AI/lightning/compare/1.8.5.post0...1.8.6

App

- Fixed install/upgrade - removing single quote (16079)
- Fixed bug where components that are re-instantiated several times failed to initialize if they were modifying `self.lightningignore` (16080)
- Fixed a bug where apps that had previously been deleted could not be run again from the CLI (16082)


Pytorch

- Add function to remove checkpoint to allow override for extended classes (16067)


---

**Full Changelog**: https://github.com/Lightning-AI/lightning/compare/1.8.5...1.8.5.post0

App

Added

- Added `Lightning{Flow,Work}.lightningignores` attributes to programmatically ignore files before uploading to the cloud (15818)
- Added a progress bar while connecting to an app through the CLI (16035)
- Support running on multiple clusters (16016)
- Added guards to cluster deletion from cli (16053)
- Added creation of the default `.lightningignore` that ignores `venv` (16056)

Changed

- Cleanup cluster waiting (16054)

Fixed

- Fixed `DDPStrategy` import in app framework (16029)
- Fixed `AutoScaler` raising an exception when non-default cloud compute is specified (15991)
- Fixed and improvements of login flow (16052)
- Fixed the debugger detection mechanism for the lightning App in VSCode (16068)


Pytorch

- some minor cleaning


---

**Full Changelog**: https://github.com/Lightning-AI/lightning/compare/1.8.4.post0...1.8.5

App

- Fixed MultiNode Component to use separate cloud computes (15965)
- Fixed Registration for CloudComputes of Works in `L.app.structures` (15964)
- Fixed a bug where auto-upgrading to the latest lightning via the CLI could get stuck in a loop (15984)


Pytorch

- Fixed the `XLAProfiler` not recording anything due to mismatching of action names (15885)


---

**Full Changelog**: https://github.com/Lightning-AI/lightning/compare/1.8.4...1.8.4.post0

App

Added

- Add `code_dir` argument to tracer run (15771)
- Added the CLI command `lightning run model` to launch a `LightningLite` accelerated script (15506)
- Added the CLI command `lightning delete app` to delete a lightning app on the cloud (15783)
- Added a CloudMultiProcessBackend which enables running a child App from within the Flow in the cloud (15800)
- Utility for pickling work object safely even from a child process (15836)
- Added `AutoScaler` component (15769)
- Added the property `ready` of the LightningFlow to inform when the `Open App` should be visible (15921)
- Added private work attributed `_start_method` to customize how to start the works (15923)
- Added a `configure_layout` method to the `LightningWork` which can be used to control how the work is handled in the layout of a parent flow (15926)
- Added the ability to run a Lightning App or Component directly from the Gallery using `lightning run app organization/name` (15941)
- Added automatic conversion of list and dict of works and flows to structures (15961)

Changed

- The `MultiNode` components now warn the user when running with `num_nodes > 1` locally (15806)
- Cluster creation and deletion now waits by default [15458
- Running an app without a UI locally no longer opens the browser (15875)
- Show a message when `BuildConfig(requirements=[...])` is passed but a `requirements.txt` file is already present in the Work (15799)
- Show a message when `BuildConfig(dockerfile="...")` is passed but a `Dockerfile` file is already present in the Work (15799)
- Dropped name column from cluster list (15721)
- Apps without UIs no longer activate the "Open App" button when running in the cloud (15875)
- Wait for full file to be transferred in Path / Payload (15934)

Removed

- Removed the `SingleProcessRuntime` (15933)

Fixed

- Fixed SSH CLI command listing stopped components (15810)
- Fixed bug when launching apps on multiple clusters (15484)
- Fixed Sigterm Handler causing thread lock which caused KeyboardInterrupt to hang (15881)
- Fixed MPS error for multinode component (defaults to cpu on mps devices now as distributed operations are not supported by pytorch on mps) (15748)
- Fixed the work not stopped when successful when passed directly to the LightningApp (15801)
- Fixed the PyTorch Inference locally on GPU (15813)
- Fixed the `enable_spawn` method of the `WorkRunExecutor` (15812)
- Fixed require/import decorator (15849)
- Fixed a bug where using `L.app.structures` would cause multiple apps to be opened and fail with an error in the cloud (15911)
- Fixed PythonServer generating noise on M1 (15949)
- Fixed multiprocessing breakpoint (15950)
- Fixed detection of a Lightning App running in debug mode (15951)
- Fixed `ImportError` on Multinode if package not present (15963)

Lite

- Fixed `shuffle=False` having no effect when using DDP/DistributedSampler (15931)

Pytorch

Changed

- Direct support for compiled models (15922)

Fixed

- Fixed issue with unsupported torch.inference_mode() on hpu backends (15918)
- Fixed LRScheduler import for PyTorch 2.0 (15940)
- Fixed `fit_loop.restarting` to be `False` for lr finder (15620)
- Fixed `torch.jit.script`-ing a LightningModule causing an unintended error message about deprecated `use_amp` property (15947)

---

**Full Changelog**: https://github.com/Lightning-AI/lightning/compare/1.8.3...1.8.4