Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
rm
: Delete files from your Cloud Platform Filesystemlightning connect data
to register data connection to private s3 buckets (#16738)min_epochs
or min_steps
(#16719)@akihironitta, @awaelchli, @borda, @tchaton
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lightning open
command (#16482)ls
: List files from your Cloud Platform Filesystemcd
: Change the current directory within your Cloud Platform filesystem (terminal session based)pwd
: Return the current folder in your Cloud Platform Filesystemcp
: Copy files between your Cloud Platform Filesystem and local filesystemcd
into non-existent folders (#16645)cp
(upload) at project level (#16631)ls
and cp
(download) at project level (#16622)lightning connect data
to register data connection to s3 buckets (#16670)LightningClient(retry=False)
to retry=True
(#16382)lightning.app.components.LiteMultiNode
to lightning.app.components.FabricMultiNode
(#16505)lightning connect
to lightning connect app
for consistency (#16670)lightning cp
(#16626)accelerator="mps"
and ddp
strategy pairing (#16455)torch_xla
requirement (#16476)torch.distributed
is not available (#16658)save_hyperparameters
on mixin classes that don't subclass LightningModule
/LightningDataModule
(#16369)MLFlowLogger
logging the wrong keys with .log_hyperparams()
(#16418)MLFlowLogger
and long values are truncated (#16451)torch_xla
requirement (#16476)torch.distributed
is not available (#16658)@akihironitta, @awaelchli, @borda, @BrianPulfer, @ethanwharris, @hhsecond, @justusschock, @Liyang90, @RuRo, @senarvi, @shenoynikhil, @tchaton
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DeviceStatsMonitor
(#16002)lightning_app.components.serve.gradio
to lightning_app.components.serve.gradio_server
(#16201)relpath
bug on Windows (#16164)LooseVersion
(#16162)lightning login
with env variables would not correctly save the credentials (#16339)Fabric.launch()
to programmatically launch processes (e.g. in Jupyter notebook) (#14992)run
method (#14992)Fabric.setup_module()
and Fabric.setup_optimizers()
to support strategies that need to set up the model before an optimizer can be created (#15185)lightning_fabric.accelerators.find_usable_cuda_devices
utility function (#16147)Fabric(callbacks=...)
and emitting events through Fabric.call()
(#16074)Fabric(loggers=...)
to support different Logger frameworks in FabricFabric.log
for logging scalars using multiple loggersFabric.log_dict
for logging a dictionary of multiple metrics at onceFabric.loggers
and Fabric.logger
attributes to access the individual logger instancesself.log
and self.log_dict
in a LightningModule when using Fabricself.logger
and self.loggers
in a LightningModule when using Fabriclightning_fabric.loggers.TensorBoardLogger
(#16121)lightning_fabric.loggers.CSVLogger
(#16346).zero_grad(set_to_none=...)
on the wrapped optimizer regardless of which strategy is used (#16275)LightningLite
to Fabric
(#15932, #15938)Fabric.run()
method is no longer abstract (#14992)XLAStrategy
now inherits from ParallelStrategy
instead of DDPSpawnStrategy
(#15838)DDPSpawnStrategy
into DDPStrategy
and removed DDPSpawnStrategy
(#14952).setup_dataloaders()
now calls .set_epoch()
on the distributed sampler if one is used (#16101)Strategy.reduce
to Strategy.all_reduce
in all strategies (#16370)strategy='ddp_sharded'|'ddp_sharded_spawn'
). Use Fully-Sharded Data Parallel instead (strategy='fsdp'
) (#16329)DistributedSampler
(#16101)MetricCollection
with enabled compute groups (#15580)pl.loggers.WandbLogger
(#16173)LRFinder
(#15304)pl.utilities.upgrade_checkpoint
script (#15333)ax
to the .lr_find().plot()
to enable writing to a user-defined axes in a matplotlib figure (#15652)log_model
parameter to MLFlowLogger
(#9187)self.log(..., logger=True)
is called without a configured logger (#15814)LightningCLI
support for optimizer and learning schedulers via callable type dependency injection (#15869)DDPFullyShardedNativeStrategy
strategy (#15826)DDPFullyShardedNativeStrategy(cpu_offload=True|False)
via bool instead of needing to pass a configuration object (#15832)LightningModule.configure_optimizers
(#16189)tensorboard
to tensorboardx
in TensorBoardLogger
(#15728)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)ModelCheckpoint.save_on_train_epoch_end
attribute is now computed dynamically every epoch, accounting for changes to the validation dataloaders (#15300)MLFlowLogger
now logs hyperparameters and metrics in batched API calls (#15915)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)tensorboardX
to extra dependencies. Use the CSVLogger
by default (#16349)description
, env_prefix
and env_parse
parameters in LightningCLI.__init__
in favour of giving them through parser_kwargs
(#15651)pytorch_lightning.profiler
in favor of pytorch_lightning.profilers
(#16059)Trainer(auto_select_gpus=...)
in favor of pytorch_lightning.accelerators.find_usable_cuda_devices
(#16147)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)
pytorch_lightning.plugins.NativeMixedPrecisionPlugin
in favor of pytorch_lightning.plugins.MixedPrecisionPlugin
LightningModule.optimizer_step(using_native_amp=...)
argumentTrainer(amp_backend=...)
argumentTrainer.amp_backend
propertyTrainer(amp_level=...)
argumentpytorch_lightning.plugins.ApexMixedPrecisionPlugin
classpytorch_lightning.utilities.enums.AMPType
enumDeepSpeedPrecisionPlugin(amp_type=..., amp_level=...)
argumentshorovod
deprecation (#16141)
Trainer(strategy="horovod")
HorovodStrategy
classpytorch_lightning.lite.LightningLite
in favor of lightning.fabric.Fabric
(#16314)FairScale
deprecation (in favor of PyTorch's FSDP implementation) (#16353)
pytorch_lightning.overrides.fairscale.LightningShardedDataParallel
classpytorch_lightning.plugins.precision.fully_sharded_native_amp.FullyShardedNativeMixedPrecisionPlugin
classpytorch_lightning.plugins.precision.sharded_native_amp.ShardedNativeMixedPrecisionPlugin
classpytorch_lightning.strategies.fully_sharded.DDPFullyShardedStrategy
classpytorch_lightning.strategies.sharded.DDPShardedStrategy
classpytorch_lightning.strategies.sharded_spawn.DDPSpawnShardedStrategy
classpytorch_lightning.utilities.memory.get_gpu_memory_map
in favor of pytorch_lightning.accelerators.cuda.get_nvidia_gpu_stats
(#15617)pytorch_lightning.profiler.base.AbstractProfiler
in favor of pytorch_lightning.profilers.profiler.Profiler
(#15637)pytorch_lightning.profiler.base.BaseProfiler
in favor of pytorch_lightning.profilers.profiler.Profiler
(#15637)pytorch_lightning.utilities.meta
(#16038)LightningDeepSpeedModule
(#16041)pytorch_lightning.accelerators.GPUAccelerator
in favor of pytorch_lightning.accelerators.CUDAAccelerator
(#16050)pytorch_lightning.profiler.*
classes in favor of pytorch_lightning.profilers
(#16059)pytorch_lightning.utilities.cli
module in favor of pytorch_lightning.cli
(#16116)pytorch_lightning.loggers.base
module in favor of pytorch_lightning.loggers.logger
(#16120)pytorch_lightning.loops.base
module in favor of pytorch_lightning.loops.loop
(#16142)pytorch_lightning.core.lightning
module in favor of pytorch_lightning.core.module
(#16318)pytorch_lightning.callbacks.base
module in favor of pytorch_lightning.callbacks.callback
(#16319)Trainer.reset_train_val_dataloaders()
in favor of Trainer.reset_{train,val}_dataloader
(#16131)LightningCLI(seed_everything_default=None)
(#16131)strategy='ddp_sharded'|'ddp_sharded_spawn'
). Use Fully-Sharded Data Parallel instead (strategy='fsdp'
) (#16329)reduce_boolean_decision
to accommodate any
-analogous semantics expected by the EarlyStopping
callback (#15253)interval
key of the scheduler would be ignored during manual optimization, making the LearningRateMonitor callback fail to log the learning rate (#16308)MLFlowLogger
not finalizing correctly when status code 'finished' was passed (#16340)@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 :]
Request
annotation in configure_api
handlers (#16047)work.delete
method to delete the work (#16103)display_name
property to LightningWork for the cloud (#16095)ColdStartProxy
to the AutoScaler (#16094)ready
(#16075)ready
for components (#16129)start_method
for creating Work processes locally on macOS is now 'spawn' (previously 'fork') (#16089)lightning.app.utilities.cloud.is_running_in_cloud
now returns True
during the loading of the app locally when running with --cloud
(#16045)True
(#16009)PythonServer
messaging "Your app has started" (#15989)AutoScaler
would fail with min_replica=0 (#16092AutoScaler
UI (#16128)streamlit
(#16139)Full Changelog: https://github.com/Lightning-AI/lightning/compare/1.8.5.post0...1.8.6
self.lightningignore
(#16080)Full Changelog: https://github.com/Lightning-AI/lightning/compare/1.8.5...1.8.5.post0
Lightning{Flow,Work}.lightningignores
attributes to programmatically ignore files before uploading to the cloud (#15818).lightningignore
that ignores venv
(#16056)DDPStrategy
import in app framework (#16029)AutoScaler
raising an exception when non-default cloud compute is specified (#15991)Full Changelog: https://github.com/Lightning-AI/lightning/compare/1.8.4.post0...1.8.5
L.app.structures
(#15964)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
:robot:
code_dir
argument to tracer run (#15771)lightning run model
to launch a LightningLite
accelerated script (#15506)lightning delete app
to delete a lightning app on the cloud (#15783)AutoScaler
component (#15769)ready
of the LightningFlow to inform when the Open App
should be visible (#15921)_start_method
to customize how to start the works (#15923)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)lightning run app organization/name
(#15941)MultiNode
components now warn the user when running with num_nodes > 1
locally (#15806)BuildConfig(requirements=[...])
is passed but a requirements.txt
file is already present in the Work (#15799)BuildConfig(dockerfile="...")
is passed but a Dockerfile
file is already present in the Work (#15799)SingleProcessRuntime
(#15933)enable_spawn
method of the WorkRunExecutor
(#15812)L.app.structures
would cause multiple apps to be opened and fail with an error in the cloud (#15911)ImportError
on Multinode if package not present (#15963)shuffle=False
having no effect when using DDP/DistributedSampler (#15931)fit_loop.restarting
to be False
for lr finder (#15620)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
Full Changelog: https://github.com/Lightning-AI/lightning/compare/1.8.3...1.8.3