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Source code for pytorch_lightning.accelerators.cpu_accelerator

# 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.
from typing import Any, Callable, Optional, Union

import torch

from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
from pytorch_lightning.cluster_environments import ClusterEnvironment
from pytorch_lightning.utilities import AMPType, rank_zero_warn
from pytorch_lightning.utilities.exceptions import MisconfigurationException


[docs]class CPUAccelerator(Accelerator): def __init__(self, trainer, cluster_environment: Optional[ClusterEnvironment] = None): """ Runs training on CPU Example:: # default trainer = Trainer(accelerator=CPUAccelerator()) """ super().__init__(trainer, cluster_environment) self.nickname = None def setup(self, model): # run through amp wrapper if self.trainer.amp_backend: raise MisconfigurationException('amp + cpu is not supported. Please use a GPU option') # call setup after the ddp process has connected self.trainer.call_setup_hook(model) # CHOOSE OPTIMIZER # allow for lr schedulers as well self.setup_optimizers(model) self.trainer.model = model def train(self): model = self.trainer.model # set up training routine self.trainer.train_loop.setup_training(model) # train or test results = self.train_or_test() return results def _step(self, model_step: Callable, args): if self.trainer.amp_backend == AMPType.NATIVE: with torch.cuda.amp.autocast(): output = model_step(*args) else: output = model_step(*args) return output def training_step(self, args): return self._step(self.trainer.model.training_step, args) def validation_step(self, args): return self._step(self.trainer.model.validation_step, args) def test_step(self, args): return self._step(self.trainer.model.test_step, args)
[docs] def sync_tensor(self, tensor: Union[torch.Tensor], group: Optional[Any] = None, reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor: return tensor
@property def require_distributed_sampler(self): return False

© Copyright Copyright (c) 2018-2021, William Falcon et al... Revision 652df188.

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