def __init__(self, model_creator, optimizer_creator, loss_creator):
from zoo.orca.automl.pytorch_utils import validate_pytorch_loss, validate_pytorch_optim
self.model_creator = model_creator
optimizer = validate_pytorch_optim(optimizer_creator)
self.optimizer_creator = optimizer
loss = validate_pytorch_loss(loss_creator)
self.loss_creator = loss
def from_torch(
*,
model_creator,
optimizer,
loss,
logs_dir="/tmp/auto_estimator_logs",
resources_per_trial=None,
name=None,
):
"""
Create an AutoEstimator for torch.
:param model_creator: PyTorch model creator function.
:param optimizer: PyTorch optimizer creator function or pytorch optimizer name (string).
Note that you should specify learning rate search space with key as "lr" or LR_NAME
(from zoo.orca.automl.pytorch_utils import LR_NAME) if input optimizer name.
Without learning rate search space specified, the default learning rate value of 1e-3
will be used for all estimators.
:param loss: PyTorch loss instance or PyTorch loss creator function
or pytorch loss name (string).
:param logs_dir: Local directory to save logs and results. It defaults to
"/tmp/auto_estimator_logs"
:param resources_per_trial: Dict. resources for each trial. e.g. {"cpu": 2}.
:param name: Name of the auto estimator.
:return: an AutoEstimator object.
"""
from zoo.orca.automl.pytorch_utils import validate_pytorch_loss, \
validate_pytorch_optim
from zoo.automl.model import PytorchModelBuilder
loss = validate_pytorch_loss(loss)
optimizer = validate_pytorch_optim(optimizer)
model_builder = PytorchModelBuilder(model_creator=model_creator,
optimizer_creator=optimizer,
loss_creator=loss)
return AutoEstimator(model_builder=model_builder,
logs_dir=logs_dir,
resources_per_trial=resources_per_trial,
name=name)
