2.0.x

Regular User

If	Then	Ref
used PyTorch 1.11	upgrade to PyTorch 2.1 or higher	PR18691
called `self.trainer.model.parameters()` in `LightningModule.configure_optimizers()` when using FSDP	On PyTorch 2.0+, call `self.parameters()` from now on	PR17309
used `Trainer(accelerator="tpu", devices=[i])"` to select the 1-based TPU core index	the index is now 0-based	PR17227
used `torch_xla < 1.13`	upgrade to `torch_xla >= 1.13`	PR17368
used `trainer.num_val_batches` to get the total size of all validation dataloaders	use `sum(trainer.num_val_batches)`	PR18441
used `trainer.num_test_batches` to get the total size of all test dataloaders	use `sum(trainer.num_test_batches)`	PR18441
used `trainer.num_sanity_val_batches` to get the total size of all validation dataloaders for sanity checking	use `sum(trainer.num_sanity_val_batches)`	PR18441
used `Trainer(devices="auto")` to auto-select all available GPUs in a Jupyter notebook	use `Trainer(devices=-1)`	PR18291
used `Trainer(devices="auto")` to auto-select all available GPUs in a Jupyter notebook	use `Trainer(devices=-1)`	PR18291
`pip install lightning` to install `lightning.app` dependencies	use `pip install lightning[app]` if you need `lightning.app`	PR18386

Advanced User

If	Then	Ref
used the `torchdistx` package and integration in Trainer	materialize the model weights manually, or follow our guide for initializing large models	PR17995
defined `def training_step(self, dataloader_iter, batch_idx)` in LightningModule	remove `batch_idx` from the signature and expect `dataloader_iter` to return a triplet `(batch, batch_idx, dataloader_idx)`	PR18390
defined `def validation_step(self, dataloader_iter, batch_idx)` in LightningModule	remove `batch_idx` from the signature and expect `dataloader_iter` to return a triplet `(batch, batch_idx, dataloader_idx)`	PR18390
defined `def test_step(self, dataloader_iter, batch_idx)` in LightningModule	remove `batch_idx` from the signature and expect `dataloader_iter` to return a triplet `(batch, batch_idx, dataloader_idx)`	PR18390
defined `def predict_step(self, dataloader_iter, batch_idx)` in LightningModule	remove `batch_idx` from the signature and expect `dataloader_iter` to return a triplet `(batch, batch_idx, dataloader_idx)`	PR18390
used `batch = next(dataloader_iter)` in LightningModule `*_step` hooks	use `batch, batch_idx, dataloader_idx = next(dataloader_iter)`	PR18390
relied on automatic detection of Kubeflow environment	use `Trainer(plugins=KubeflowEnvironment())` to explicitly set it on a Kubeflow cluster	PR18137

Developer

If	Then	Ref
used the `XLAStrategy.is_distributed` property	it was removed because it was always True	PR17381
used the `SingleTPUStrategy.is_distributed` property	it was removed because it was always False	PR17381

Was this page helpful?