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Fig logging with log_gpu_memory='min_max' #9013

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merged 6 commits into from
Aug 24, 2021
Merged

Fig logging with log_gpu_memory='min_max' #9013

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fix
SkafteNicki Aug 20, 2021
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[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Aug 20, 2021
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SkafteNicki Aug 20, 2021
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Merge branch 'log_gpu_mem_fix' of https://github.com/SkafteNicki/pyto…
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[pre-commit.ci] auto fixes from pre-commit.com hooks
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2 changes: 2 additions & 0 deletions CHANGELOG.md
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Expand Up @@ -206,6 +206,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
- Fixed a bug in the binary search mode of auto batch size scaling where exception was thrown if the first trainer run resulted in OOM ([#8954](https://github.com/PyTorchLightning/pytorch-lightning/pull/8954))


- Fixed a bug causing logging with `log_gpu_memory='min_max'` not working ([#9013](https://github.com/PyTorchLightning/pytorch-lightning/pull/9013))

## [1.4.0] - 2021-07-27

### Added
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11 changes: 8 additions & 3 deletions pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py
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Expand Up @@ -224,9 +224,14 @@ def update_train_epoch_metrics(self) -> None:

def _log_gpus_metrics(self):
for key, mem in self.gpus_metrics.items():
gpu_id = int(key.split("/")[0].split(":")[1])
if gpu_id in self.trainer.accelerator_connector.parallel_device_ids:
self.trainer.lightning_module.log(key, mem, prog_bar=False, logger=True, on_step=True, on_epoch=False)
if self.log_gpu_memory == "min_max":
self.trainer.lightning_module.log(key, mem, prog_bar=False, logger=True)
else:
gpu_id = int(key.split("/")[0].split(":")[1])
if gpu_id in self.trainer.accelerator_connector.parallel_device_ids:
self.trainer.lightning_module.log(
key, mem, prog_bar=False, logger=True, on_step=True, on_epoch=False
)

"""
Utilities and properties
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5 changes: 3 additions & 2 deletions tests/trainer/logging_/test_train_loop_logging.py
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Expand Up @@ -645,15 +645,16 @@ def training_step(self, batch, batch_idx):


@RunIf(min_gpus=2)
def test_log_gpu_memory_without_logging_on_step(tmpdir):
@pytest.mark.parametrize("log_gpu_memory", ["all", "min_max"])
def test_log_gpu_memory_without_logging_on_step(tmpdir, log_gpu_memory):

model = BoringModel()
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
limit_train_batches=1,
limit_val_batches=0,
log_gpu_memory="all",
log_gpu_memory=log_gpu_memory,
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log_every_n_steps=1,
gpus=[1],
)
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