Source code for lightning.pytorch.callbacks.device_stats_monitor
# Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Device Stats Monitor
====================
Monitors and logs device stats during training.
"""
from typing import Any, Dict, Optional
import lightning.pytorch as pl
from lightning.pytorch.accelerators.cpu import _PSUTIL_AVAILABLE
from lightning.pytorch.callbacks.callback import Callback
from lightning.pytorch.utilities.exceptions import MisconfigurationException
from lightning.pytorch.utilities.types import STEP_OUTPUT
[docs]class DeviceStatsMonitor(Callback):
r"""
Automatically monitors and logs device stats during training, validation and testing stage.
``DeviceStatsMonitor`` is a special callback as it requires a ``logger`` to passed as argument
to the ``Trainer``.
Args:
cpu_stats: if ``None``, it will log CPU stats only if the accelerator is CPU.
If ``True``, it will log CPU stats regardless of the accelerator.
If ``False``, it will not log CPU stats regardless of the accelerator.
Raises:
MisconfigurationException:
If ``Trainer`` has no logger.
ModuleNotFoundError:
If ``psutil`` is not installed and CPU stats are monitored.
Example::
from lightning import Trainer
from lightning.pytorch.callbacks import DeviceStatsMonitor
device_stats = DeviceStatsMonitor()
trainer = Trainer(callbacks=[device_stats])
"""
def __init__(self, cpu_stats: Optional[bool] = None) -> None:
self._cpu_stats = cpu_stats
[docs] def setup(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
stage: str,
) -> None:
if stage != "fit":
return
if not trainer.loggers:
raise MisconfigurationException("Cannot use `DeviceStatsMonitor` callback with `Trainer(logger=False)`.")
# warn in setup to warn once
device = trainer.strategy.root_device
if self._cpu_stats is None and device.type == "cpu" and not _PSUTIL_AVAILABLE:
raise ModuleNotFoundError(
f"`DeviceStatsMonitor` cannot log CPU stats as `psutil` is not installed. {str(_PSUTIL_AVAILABLE)} "
)
def _get_and_log_device_stats(self, trainer: "pl.Trainer", key: str) -> None:
if not trainer._logger_connector.should_update_logs:
return
device = trainer.strategy.root_device
if self._cpu_stats is False and device.type == "cpu":
# cpu stats are disabled
return
device_stats = trainer.accelerator.get_device_stats(device)
if self._cpu_stats and device.type != "cpu":
# Don't query CPU stats twice if CPU is accelerator
from lightning.pytorch.accelerators.cpu import get_cpu_stats
device_stats.update(get_cpu_stats())
for logger in trainer.loggers:
separator = logger.group_separator
prefixed_device_stats = _prefix_metric_keys(device_stats, f"{self.__class__.__qualname__}.{key}", separator)
logger.log_metrics(prefixed_device_stats, step=trainer.fit_loop.epoch_loop._batches_that_stepped)
[docs] def on_train_batch_start(
self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int
) -> None:
self._get_and_log_device_stats(trainer, "on_train_batch_start")
[docs] def on_train_batch_end(
self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: STEP_OUTPUT, batch: Any, batch_idx: int
) -> None:
self._get_and_log_device_stats(trainer, "on_train_batch_end")
[docs] def on_validation_batch_start(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
self._get_and_log_device_stats(trainer, "on_validation_batch_start")
[docs] def on_validation_batch_end(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
outputs: Optional[STEP_OUTPUT],
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
self._get_and_log_device_stats(trainer, "on_validation_batch_end")
[docs] def on_test_batch_start(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
self._get_and_log_device_stats(trainer, "on_test_batch_start")
[docs] def on_test_batch_end(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
outputs: Optional[STEP_OUTPUT],
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
self._get_and_log_device_stats(trainer, "on_test_batch_end")
def _prefix_metric_keys(metrics_dict: Dict[str, float], prefix: str, separator: str) -> Dict[str, float]:
return {prefix + separator + k: v for k, v in metrics_dict.items()}