pytorch-lightning 2.0.8 on Python PyPI

App

On XLA, avoid setting the global rank before processes have been launched as this will initialize the PJRT computation client in the main process (#16966)

Fixed model parameters getting shared between processes when running with strategy="ddp_spawn" and accelerator="cpu"; this has a necessary memory impact, as parameters are replicated for each process now (#18238)
Removed false positive warning when using fabric.no_backward_sync with XLA strategies (#17761)
Fixed issue where Fabric would not initialize the global rank, world size, and rank-zero-only rank after initialization and before launch (#16966)
Fixed FSDP full-precision param_dtype training (16-mixed, bf16-mixed and 32-true configurations) to avoid FSDP assertion errors with PyTorch < 2.0 (#18278)

On XLA, avoid setting the global rank before processes have been launched as this will initialize the PJRT computation client in the main process (#16966)
Fix inefficiency in rich progress bar (#18369)

Fixed FSDP full-precision param_dtype training (16-mixed and bf16-mixed configurations) to avoid FSDP assertion errors with PyTorch < 2.0 (#18278)
Fixed an issue that prevented the use of custom logger classes without an experiment property defined (#18093)
Fixed setting the tracking uri in MLFlowLogger for logging artifacts to the MLFlow server (#18395)
Fixed redundant iter() call to dataloader when checking dataloading configuration (#18415)
Fixed model parameters getting shared between processes when running with strategy="ddp_spawn" and accelerator="cpu"; this has a necessary memory impact, as parameters are replicated for each process now (#18238)
Properly manage fetcher.done with dataloader_iter (#18376)

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