def __init__(self,
model,
loss,
optimizer,
metrics=None,
model_dir=None,
bigdl_type="float"):
from zoo.pipeline.api.torch import TorchModel, TorchLoss, TorchOptim
self.loss = loss
if self.loss is None:
self.loss = TorchLoss()
else:
self.loss = TorchLoss.from_pytorch(loss)
if optimizer is None:
from zoo.orca.learn.optimizers.schedule import Default
optimizer = SGD(learningrate_schedule=Default())
if isinstance(optimizer, TorchOptimizer):
optimizer = TorchOptim.from_pytorch(optimizer)
elif isinstance(optimizer, OrcaOptimizer):
optimizer = optimizer.get_optimizer()
else:
raise ValueError(
"Only PyTorch optimizer and orca optimizer are supported")
from zoo.orca.learn.metrics import Metric
self.metrics = Metric.convert_metrics_list(metrics)
self.log_dir = None
self.app_name = None
self.model_dir = model_dir
self.model = TorchModel.from_pytorch(model)
self.estimator = SparkEstimator(self.model,
optimizer,
model_dir,
bigdl_type=bigdl_type)
def __init__(self,
*,
model,
loss,
optimizer=None,
metrics=None,
feature_preprocessing=None,
label_preprocessing=None,
model_dir=None):
self.loss = loss
self.optimizer = optimizer
self.metrics = Metric.convert_metrics_list(metrics)
self.feature_preprocessing = feature_preprocessing
self.label_preprocessing = label_preprocessing
self.model_dir = model_dir
self.model = model
self.nn_model = NNModel(
self.model, feature_preprocessing=self.feature_preprocessing)
self.nn_estimator = NNEstimator(self.model, self.loss,
self.feature_preprocessing,
self.label_preprocessing)
if self.optimizer is None:
from bigdl.optim.optimizer import SGD
self.optimizer = SGD()
self.nn_estimator.setOptimMethod(self.optimizer)
self.estimator = SparkEstimator(self.model, self.optimizer,
self.model_dir)
self.log_dir = None
self.app_name = None
self.is_nnframe_fit = False