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remove deprecated sync step argument from WandbLogger (Lightning-AI#8763)
* remove deprecated sync step * update chlog
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CHANGELOG.md

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@@ -102,6 +102,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
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- Removed the deprecated `model` argument from `ModelCheckpoint.save_checkpoint` ([#8688](https://github.com/PyTorchLightning/pytorch-lightning/pull/8688))
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- Removed the deprecated `sync_step` argument from `WandbLogger` ([#8763](https://github.com/PyTorchLightning/pytorch-lightning/pull/8763))
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### Fixed
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- Fixed `trainer.fit_loop.split_idx` always returning `None` ([#8601](https://github.com/PyTorchLightning/pytorch-lightning/pull/8601))

pytorch_lightning/loggers/wandb.py

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@@ -113,7 +113,6 @@ def __init__(
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log_model: Optional[bool] = False,
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experiment=None,
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prefix: Optional[str] = "",
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sync_step: Optional[bool] = None,
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**kwargs,
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):
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if wandb is None:
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"Hint: Upgrade with `pip install --ugrade wandb`."
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)
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if sync_step is not None:
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warning_cache.deprecation(
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"`WandbLogger(sync_step=(True|False))` is deprecated in v1.2.1 and will be removed in v1.5."
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" Metrics are now logged separately and automatically synchronized."
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)
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super().__init__()
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self._offline = offline
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self._log_model = log_model

tests/deprecated_api/test_remove_1-5.py

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from pytorch_lightning import Callback, Trainer
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from pytorch_lightning.callbacks import ModelCheckpoint
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from pytorch_lightning.core.decorators import auto_move_data
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from pytorch_lightning.loggers import WandbLogger
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from pytorch_lightning.plugins import DeepSpeedPlugin
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from pytorch_lightning.profiler import AdvancedProfiler, BaseProfiler, PyTorchProfiler, SimpleProfiler
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from tests.deprecated_api import no_deprecated_call
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from tests.helpers.utils import no_warning_call
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@mock.patch("pytorch_lightning.loggers.wandb.wandb")
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def test_v1_5_0_wandb_unused_sync_step(_):
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with pytest.deprecated_call(match=r"v1.2.1 and will be removed in v1.5"):
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WandbLogger(sync_step=True)
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def test_v1_5_0_old_callback_on_save_checkpoint(tmpdir):
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class OldSignature(Callback):
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def on_save_checkpoint(self, trainer, pl_module):

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