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 Metrics
self.metrics = Metrics.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 = Metrics.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
def evaluate(self,
data,
batch_size=32,
feature_cols=None,
labels_cols=None,
validation_metrics=None):
from zoo.orca.data.utils import to_sample
from zoo.orca.learn.metrics import Metrics
assert data is not None, "validation data shouldn't be None"
validation_metrics = Metrics.convert_metrics_list(validation_metrics)
if isinstance(data, SparkXShards):
val_feature_set = FeatureSet.sample_rdd(
data.rdd.flatMap(to_sample))
return self.estimator.evaluate(val_feature_set, validation_metrics,
batch_size)
elif isinstance(data, DataLoader) or callable(data):
val_feature_set = FeatureSet.pytorch_dataloader(data)
return self.estimator.evaluate_minibatch(val_feature_set,
validation_metrics)
else:
raise ValueError(
"Data should be a SparkXShards, a DataLoader or a callable "
"data_creator, but get " + data.__class__.__name__)
def fit(self,
data,
epochs=1,
batch_size=32,
feature_cols=None,
labels_cols=None,
validation_data=None,
validation_metrics=None,
checkpoint_trigger=None):
from zoo.orca.data.utils import to_sample
from zoo.orca.learn.metrics import Metrics
from zoo.orca.learn.trigger import Trigger
end_trigger = MaxEpoch(epochs)
assert batch_size > 0, "batch_size should be greater than 0"
validation_metrics = Metrics.convert_metrics_list(validation_metrics)
checkpoint_trigger = Trigger.convert_trigger(checkpoint_trigger)
if self.log_dir is not None and self.app_name is not None:
self.estimator.set_tensorboard(self.log_dir, self.app_name)
if isinstance(data, SparkXShards):
train_rdd = data.rdd.flatMap(to_sample)
train_feature_set = FeatureSet.sample_rdd(train_rdd)
if validation_data is None:
val_feature_set = None
else:
assert isinstance(validation_data, SparkXShards), "validation_data should be a " \
"SparkXShards"
val_feature_set = FeatureSet.sample_rdd(
validation_data.rdd.flatMap(to_sample))
self.estimator.train(train_feature_set, self.loss, end_trigger,
checkpoint_trigger, val_feature_set,
validation_metrics, batch_size)
elif isinstance(data, DataLoader) or callable(data):
train_feature_set = FeatureSet.pytorch_dataloader(data, "", "")
if validation_data is None:
val_feature_set = None
else:
assert isinstance(validation_data, DataLoader) or callable(data), \
"validation_data should be a pytorch DataLoader or a callable data_creator"
val_feature_set = FeatureSet.pytorch_dataloader(
validation_data)
self.estimator.train_minibatch(train_feature_set, self.loss,
end_trigger, checkpoint_trigger,
val_feature_set, validation_metrics)
else:
raise ValueError(
"Data and validation data should be SparkXShards, DataLoaders or "
"callable data_creators but get " + data.__class__.__name__)
return self
def evaluate(self, data, validation_methods=None, batch_size=32):
assert data is not None, "validation data shouldn't be None"
if isinstance(data, DataFrame):
raise NotImplementedError
elif isinstance(data, SparkXShards):
from zoo.orca.data.utils import to_sample
from zoo.orca.learn.metrics import Metrics
validation_methods = Metrics.convert_metrics_list(
validation_methods)
val_feature_set = FeatureSet.sample_rdd(
data.rdd.flatMap(to_sample))
return self.estimator.evaluate(val_feature_set, validation_methods,
batch_size)
else:
raise ValueError(
"Data should be XShards or Spark DataFrame, but get " +
data.__class__.__name__)
def fit(self,
data,
epochs,
feature_cols="features",
labels_cols="label",
batch_size=32,
caching_sample=True,
val_data=None,
val_trigger=None,
val_methods=None,
checkpoint_trigger=None):
from zoo.orca.learn.metrics import Metrics
from zoo.orca.learn.trigger import Trigger
assert batch_size > 0, "batch_size should be greater than 0"
if isinstance(data, DataFrame):
if isinstance(feature_cols, list):
data, val_data, feature_cols = \
BigDLEstimatorWrapper._combine_cols(data, feature_cols, col_name="features",
val_data=val_data)
if isinstance(labels_cols, list):
data, val_data, labels_cols = \
BigDLEstimatorWrapper._combine_cols(data, labels_cols, col_name="label",
val_data=val_data)
self.nn_estimator.setBatchSize(batch_size).setMaxEpoch(epochs)\
.setCachingSample(caching_sample).setFeaturesCol(feature_cols)\
.setLabelCol(labels_cols)
if val_data is not None:
assert isinstance(
val_data,
DataFrame), "val_data should be a spark DataFrame."
assert val_trigger is not None and val_methods is not None, \
"You should provide val_trigger and val_methods if you provide val_data."
val_trigger = Trigger.convert_trigger(val_trigger)
val_methods = Metrics.convert_metrics_list(val_methods)
self.nn_estimator.setValidation(val_trigger, val_data,
val_methods, batch_size)
if self.log_dir is not None and self.app_name is not None:
from bigdl.optim.optimizer import TrainSummary
from bigdl.optim.optimizer import ValidationSummary
train_summary = TrainSummary(log_dir=self.log_dir,
app_name=self.app_name)
self.nn_estimator.setTrainSummary(train_summary)
val_summary = ValidationSummary(log_dir=self.log_dir,
app_name=self.log_dir)
self.nn_estimator.setValidationSummary(val_summary)
if self.model_dir is not None and checkpoint_trigger is not None:
checkpoint_trigger = Trigger.convert_trigger(
checkpoint_trigger)
self.nn_estimator.setCheckpoint(self.model_dir,
checkpoint_trigger)
self.nn_model = self.nn_estimator.fit(data)
self.is_nnframe_fit = True
elif isinstance(data, SparkXShards):
from zoo.orca.data.utils import to_sample
end_trigger = MaxEpoch(epochs)
val_methods = Metrics.convert_metrics_list(val_methods)
checkpoint_trigger = Trigger.convert_trigger(checkpoint_trigger)
if isinstance(data, SparkXShards):
train_rdd = data.rdd.flatMap(to_sample)
train_feature_set = FeatureSet.sample_rdd(train_rdd)
if val_data is None:
val_feature_set = None
else:
assert isinstance(
val_data, SparkXShards), "val_data should be a XShards"
val_feature_set = FeatureSet.sample_rdd(
val_data.rdd.flatMap(to_sample))
if self.log_dir is not None and self.app_name is not None:
self.estimator.set_tensorboard(self.log_dir, self.app_name)
self.estimator.train(train_feature_set, self.loss, end_trigger,
checkpoint_trigger, val_feature_set,
val_methods, batch_size)
self.is_nnframe_fit = False
else:
raise ValueError(
"Data and validation data should be XShards, but get " +
data.__class__.__name__)
else:
raise ValueError(
"Data should be XShards or Spark DataFrame, but get " +
data.__class__.__name__)
return self
