def to_dataset(data, batch_size, batch_per_thread, validation_data,
feature_cols, labels_cols, hard_code_batch_size,
sequential_order, shuffle, auto_shard_files, memory_type="DRAM"):
# todo wrap argument into kwargs
if validation_data:
if isinstance(data, SparkXShards):
assert isinstance(validation_data, SparkXShards), \
"train data and validation data should be both SparkXShards"
if isinstance(data, Dataset):
assert isinstance(validation_data, Dataset), \
"train data and validation data should be both orca.data.tf.Dataset"
if isinstance(data, DataFrame):
assert isinstance(validation_data, DataFrame), \
"train data and validation data should be both Spark DataFrame"
if isinstance(data, tf.data.Dataset):
assert isinstance(validation_data, tf.data.Dataset), \
"train data and validation data should be both tf.data.Dataset"
if isinstance(data, SparkXShards):
dataset = xshards_to_tf_dataset(data,
batch_size,
batch_per_thread,
validation_data,
hard_code_batch_size=hard_code_batch_size,
memory_type=memory_type,
sequential_order=sequential_order,
shuffle=shuffle)
elif isinstance(data, Dataset):
dataset = TFDataDataset2(data, batch_size=batch_size,
batch_per_thread=batch_per_thread,
validation_dataset=validation_data)
elif isinstance(data, DataFrame):
dataset = TFDataset.from_dataframe(data, feature_cols, labels_cols,
batch_size,
batch_per_thread,
hard_code_batch_size,
validation_data,
memory_type,
sequential_order,
shuffle
)
elif is_tf_data_dataset(data):
dataset = TFDataset.from_tf_data_dataset(data,
batch_size,
batch_per_thread,
hard_code_batch_size,
validation_data,
sequential_order,
shuffle, auto_shard_files=auto_shard_files)
else:
raise ValueError("data must be SparkXShards or orca.data.tf.Dataset or "
"Spark DataFrame or tf.data.Dataset")
return dataset
def fit(self,
data,
steps,
batch_size=32,
validation_data=None,
feed_dict=None,
session_config=None):
assert self.labels is not None, \
"labels is None; it should not be None in training"
assert self.loss is not None, \
"loss is None; it should not be None in training"
assert self.optimizer is not None, \
"optimizer is None; it not None in training"
if isinstance(data, SparkXShards):
dataset = _xshards_to_tf_dataset(
data,
batch_size=batch_size,
validation_data_shard=validation_data)
elif isinstance(data, Dataset):
dataset = TFDataDataset2(data,
batch_size=batch_size,
batch_per_thread=-1,
validation_dataset=validation_data)
else:
raise ValueError("data type {} is not supported; "
"it must be created by zoo.orca.data.package")
if feed_dict is not None:
tensor_with_value = {
key: (value, value)
for key, value in feed_dict.items()
}
else:
tensor_with_value = None
optimizer = TFOptimizer.from_train_op(
train_op=self.train_op,
loss=self.loss,
inputs=self.inputs,
labels=self.labels,
dataset=dataset,
metrics=self.metrics,
updates=self.updates,
sess=self.sess,
tensor_with_value=tensor_with_value,
session_config=session_config,
model_dir=self.model_dir)
optimizer.optimize(end_trigger=MaxIteration(steps))
return self
def _to_dataset(data, batch_size, batch_per_thread):
if isinstance(data, SparkXShards):
dataset = _xshards_to_tf_dataset(data,
batch_size=batch_size,
batch_per_thread=batch_per_thread)
elif isinstance(data, Dataset):
dataset = TFDataDataset2(data,
batch_size=batch_size,
batch_per_thread=batch_per_thread)
else:
raise ValueError(
"data must be a SparkXShards or an orca.data.tf.Dataset")
return dataset
def predict(self, data, batch_size=32):
assert self.outputs is not None, \
"output is None, it should not be None in prediction"
if isinstance(data, SparkXShards):
dataset = _xshards_to_tf_dataset(data,
batch_per_thread=batch_size)
elif isinstance(data, Dataset):
dataset = TFDataDataset2(data, batch_size=-1,
batch_per_thread=batch_size)
else:
raise ValueError("data must be a SparkXShards or an orca.data.tf.Dataset")
flat_inputs = nest.flatten(self.inputs)
flat_outputs = nest.flatten(self.outputs)
tfnet = TFNet.from_session(sess=self.sess, inputs=flat_inputs, outputs=flat_outputs)
return tfnet.predict(dataset)
def to_dataset(data, batch_size, batch_per_thread, validation_data,
feature_cols, labels_cols, hard_code_batch_size,
sequential_order, shuffle):
if validation_data:
if isinstance(data, SparkXShards):
assert isinstance(validation_data, SparkXShards), \
"train data and validation data should be both SparkXShards"
if isinstance(data, Dataset):
assert isinstance(validation_data, Dataset), \
"train data and validation data should be both orca.data.tf.Dataset"
if isinstance(data, DataFrame):
assert isinstance(validation_data, DataFrame), \
"train data and validation data should be both Spark DataFrame"
if isinstance(data, SparkXShards):
dataset = xshards_to_tf_dataset(
data,
batch_size,
batch_per_thread,
validation_data,
hard_code_batch_size=hard_code_batch_size,
sequential_order=sequential_order,
shuffle=shuffle)
elif isinstance(data, Dataset):
dataset = TFDataDataset2(data,
batch_size=batch_size,
batch_per_thread=batch_per_thread,
validation_dataset=validation_data)
elif isinstance(data, DataFrame):
dataset = TFDataset.from_dataframe(data, feature_cols, labels_cols,
batch_size, batch_per_thread,
hard_code_batch_size,
validation_data, sequential_order,
shuffle)
else:
raise ValueError(
"data must be SparkXShards or orca.data.tf.Dataset or Spark DataFrame"
)
return dataset