71 lines
2.5 KiB
Python
71 lines
2.5 KiB
Python
# 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 pytorch_lightning.tuner.batch_size_scaling import scale_batch_size
|
|
from pytorch_lightning.tuner.auto_gpu_select import pick_multiple_gpus
|
|
from pytorch_lightning.tuner.lr_finder import _run_lr_finder_internally, lr_find
|
|
from pytorch_lightning.core.lightning import LightningModule
|
|
from typing import Optional, List, Union
|
|
from torch.utils.data import DataLoader
|
|
|
|
|
|
class Tuner:
|
|
|
|
def __init__(self, trainer):
|
|
self.trainer = trainer
|
|
|
|
def on_trainer_init(self, auto_lr_find, auto_scale_batch_size):
|
|
self.trainer.auto_lr_find = auto_lr_find
|
|
self.trainer.auto_scale_batch_size = auto_scale_batch_size
|
|
|
|
def scale_batch_size(self,
|
|
model,
|
|
mode: str = 'power',
|
|
steps_per_trial: int = 3,
|
|
init_val: int = 2,
|
|
max_trials: int = 25,
|
|
batch_arg_name: str = 'batch_size',
|
|
**fit_kwargs):
|
|
return scale_batch_size(
|
|
self.trainer, model, mode, steps_per_trial, init_val, max_trials, batch_arg_name, **fit_kwargs
|
|
)
|
|
|
|
def lr_find(
|
|
self,
|
|
model: LightningModule,
|
|
train_dataloader: Optional[DataLoader] = None,
|
|
val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None,
|
|
min_lr: float = 1e-8,
|
|
max_lr: float = 1,
|
|
num_training: int = 100,
|
|
mode: str = 'exponential',
|
|
early_stop_threshold: float = 4.0,
|
|
):
|
|
return lr_find(
|
|
self.trainer,
|
|
model,
|
|
train_dataloader,
|
|
val_dataloaders,
|
|
min_lr,
|
|
max_lr,
|
|
num_training,
|
|
mode,
|
|
early_stop_threshold
|
|
)
|
|
|
|
def internal_find_lr(self, trainer, model: LightningModule):
|
|
return _run_lr_finder_internally(trainer, model)
|
|
|
|
def pick_multiple_gpus(self, num_gpus: int):
|
|
return pick_multiple_gpus(num_gpus)
|