83 lines
3.0 KiB
Python
83 lines
3.0 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Device Stats Monitor
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====================
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Monitors and logs device stats during training.
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"""
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from typing import Any, Dict, Optional
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import pytorch_lightning as pl
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from pytorch_lightning.callbacks.base import Callback
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.types import STEP_OUTPUT
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class DeviceStatsMonitor(Callback):
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r"""
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Automatically monitors and logs device stats during training stage. ``DeviceStatsMonitor``
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is a special callback as it requires a ``logger`` to passed as argument to the ``Trainer``.
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Raises:
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MisconfigurationException:
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If ``Trainer`` has no logger.
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Example:
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>>> from pytorch_lightning import Trainer
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>>> from pytorch_lightning.callbacks import DeviceStatsMonitor
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>>> device_stats = DeviceStatsMonitor() # doctest: +SKIP
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>>> trainer = Trainer(callbacks=[device_stats]) # doctest: +SKIP
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"""
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def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: Optional[str] = None) -> None:
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if not trainer.logger:
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raise MisconfigurationException("Cannot use DeviceStatsMonitor callback with Trainer that has no logger.")
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def on_train_batch_start(
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self,
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trainer: "pl.Trainer",
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pl_module: "pl.LightningModule",
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batch: Any,
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batch_idx: int,
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unused: Optional[int] = 0,
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) -> None:
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if not trainer.logger_connector.should_update_logs:
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return
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device_stats = trainer.accelerator.get_device_stats(pl_module.device)
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prefixed_device_stats = prefix_metrics_keys(device_stats, "on_train_batch_start")
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trainer.logger.log_metrics(prefixed_device_stats, step=trainer.global_step)
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def on_train_batch_end(
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self,
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trainer: "pl.Trainer",
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pl_module: "pl.LightningModule",
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outputs: STEP_OUTPUT,
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batch: Any,
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batch_idx: int,
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unused: Optional[int] = 0,
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) -> None:
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if not trainer.logger_connector.should_update_logs:
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return
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device_stats = trainer.accelerator.get_device_stats(pl_module.device)
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prefixed_device_stats = prefix_metrics_keys(device_stats, "on_train_batch_end")
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trainer.logger.log_metrics(prefixed_device_stats, step=trainer.global_step)
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def prefix_metrics_keys(metrics_dict: Dict[str, float], prefix: str) -> Dict[str, float]:
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return {prefix + "." + k: v for k, v in metrics_dict.items()}
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