Source code for pytorch_lightning.callbacks.device_stats_monitor
# Copyright The PyTorch Lightning 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
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"""
Device Stats Monitor
====================
Monitors and logs device stats during training.
"""
from typing import Any, Dict, Optional
import pytorch_lightning as pl
from pytorch_lightning.callbacks.base import Callback
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.types import STEP_OUTPUT
[docs]class DeviceStatsMonitor(Callback):
r"""
Automatically monitors and logs device stats during training stage. ``DeviceStatsMonitor``
is a special callback as it requires a ``logger`` to passed as argument to the ``Trainer``.
Raises:
MisconfigurationException:
If ``Trainer`` has no logger.
Example:
>>> from pytorch_lightning import Trainer
>>> from pytorch_lightning.callbacks import DeviceStatsMonitor
>>> device_stats = DeviceStatsMonitor() # doctest: +SKIP
>>> trainer = Trainer(callbacks=[device_stats]) # doctest: +SKIP
"""
[docs] def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: Optional[str] = None) -> None:
if not trainer.logger:
raise MisconfigurationException("Cannot use DeviceStatsMonitor callback with Trainer that has no logger.")
[docs] def on_train_batch_start(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
batch: Any,
batch_idx: int,
unused: Optional[int] = 0,
) -> None:
if not trainer.logger_connector.should_update_logs:
return
device_stats = trainer.accelerator.get_device_stats(pl_module.device)
prefixed_device_stats = prefix_metrics_keys(device_stats, "on_train_batch_start")
assert trainer.logger is not None
trainer.logger.log_metrics(prefixed_device_stats, step=trainer.global_step)
[docs] def on_train_batch_end(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
outputs: STEP_OUTPUT,
batch: Any,
batch_idx: int,
unused: Optional[int] = 0,
) -> None:
if not trainer.logger_connector.should_update_logs:
return
device_stats = trainer.accelerator.get_device_stats(pl_module.device)
prefixed_device_stats = prefix_metrics_keys(device_stats, "on_train_batch_end")
assert trainer.logger is not None
trainer.logger.log_metrics(prefixed_device_stats, step=trainer.global_step)
def prefix_metrics_keys(metrics_dict: Dict[str, float], prefix: str) -> Dict[str, float]:
return {prefix + "." + k: v for k, v in metrics_dict.items()}