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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
# limitations under the License.
"""
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") 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") 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()}

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