def _fit_on_prepared_data(self,
backend,
train_rows,
val_rows,
metadata,
avg_row_size,
dataset_idx=None):
self._check_params(metadata)
keras_utils = self._get_keras_utils()
run_id = self.getRunId()
if run_id is None:
run_id = 'keras_' + str(int(time.time()))
if self._has_checkpoint(run_id):
serialized_model = self._load_model_from_checkpoint(run_id)
else:
serialized_model = self._compile_model(keras_utils)
# Workaround:
# https://stackoverflow.com/questions/50583056/is-there-any-way-to-set-java-opts-for-tensorflow-process/50615570
env = {'LIBHDFS_OPTS': '-Xms2048m -Xmx2048m'}
trainer = remote.RemoteTrainer(self, metadata, keras_utils, run_id,
dataset_idx)
handle = backend.run(trainer,
args=(serialized_model, train_rows, val_rows,
avg_row_size),
env=env)
return self._create_model(handle, run_id, metadata)
def _fit_on_prepared_data(self,
backend,
train_rows,
val_rows,
metadata,
avg_row_size,
dataset_idx=None):
self._check_params(metadata)
keras_utils = self._get_keras_utils()
run_id = self.getRunId()
if run_id is None:
run_id = 'keras_' + str(int(time.time()))
if self._has_checkpoint(run_id):
serialized_model = self._load_model_from_checkpoint(run_id)
else:
serialized_model = self._compile_model(keras_utils)
trainer = remote.RemoteTrainer(self, metadata, keras_utils, run_id,
dataset_idx)
handle = backend.run(trainer,
args=(serialized_model, train_rows, val_rows,
avg_row_size),
env=self.getBackendEnv())
return self._create_model(handle, run_id, metadata)