Shortcuts

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.
import torch

from pytorch_lightning.accelerators.accelerator import Accelerator
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=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 training_step(self, args): if self.trainer.amp_backend == AMPType.NATIVE: with torch.cuda.amp.autocast(): output = self.trainer.model.training_step(*args) else: output = self.trainer.model.training_step(*args) return output def validation_step(self, args): if self.trainer.amp_backend == AMPType.NATIVE: with torch.cuda.amp.autocast(): output = self.trainer.model.validation_step(*args) else: output = self.trainer.model.validation_step(*args) return output def test_step(self, args): if self.trainer.amp_backend == AMPType.NATIVE: with torch.cuda.amp.autocast(): output = self.trainer.model.test_step(*args) else: output = self.trainer.model.test_step(*args) return output

© Copyright Copyright (c) 2018-2020, William Falcon et al... Revision 5df40a4a.

Built with Sphinx using a theme provided by Read the Docs.
Read the Docs v: 1.0.6
Versions
latest
stable
1.0.6
1.0.5
1.0.4
1.0.3
1.0.2
1.0.1
1.0.0
0.10.0
0.9.0
0.8.5
0.8.4
0.8.3
0.8.2
0.8.1
0.8.0
0.7.6
0.7.5
0.7.4
0.7.3
0.7.2
0.7.1
0.7.0
0.6.0
0.5.3.2
0.5.3
0.4.9
Downloads
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.