def read_serialized_keras_model(self, ckpt_path, model):
"""
Returns serialized keras model. On Databricks, only TFKeras is supported, not BareKeras.
The parameter `model` is for providing the model structure when the checkpoint file only
contains model weights.
"""
import tensorflow
from tensorflow import keras
from horovod.spark.keras.util import TFKerasUtil
if LooseVersion(tensorflow.__version__) < LooseVersion("2.0.0"):
model.load_weights(ckpt_path)
else:
model = keras.models.load_model(ckpt_path)
return TFKerasUtil.serialize_model(model)
def read_serialized_keras_model(self, ckpt_path, model, custom_objects):
"""
Returns serialized keras model.
The parameter `model` is for providing the model structure when the checkpoint file only
contains model weights.
"""
import tensorflow
from tensorflow import keras
from horovod.spark.keras.util import TFKerasUtil
if LooseVersion(tensorflow.__version__) < LooseVersion("2.0.0"):
model.load_weights(ckpt_path)
else:
with keras.utils.custom_object_scope(custom_objects):
model = keras.models.load_model(ckpt_path)
return TFKerasUtil.serialize_model(model)
def read_serialized_keras_model(self, ckpt_path, model, custom_objects):
"""Reads the checkpoint file of the keras model into model bytes and returns the base 64
encoded model bytes.
:param ckpt_path: A string of path to the checkpoint file.
:param model: A keras model. This parameter will be used in DBFSLocalStore\
.read_serialized_keras_model() when the ckpt_path only contains model weights.
:param custom_objects: This parameter will be used in DBFSLocalStore\
.read_serialized_keras_model() when loading the keras model.
:return: the base 64 encoded model bytes of the checkpoint model.
"""
from horovod.runner.common.util import codec
import tensorflow
from tensorflow import keras
from horovod.spark.keras.util import TFKerasUtil
if LooseVersion(tensorflow.__version__) < LooseVersion("2.0.0"):
model_bytes = self.read(ckpt_path)
return codec.dumps_base64(model_bytes)
else:
with keras.utils.custom_object_scope(custom_objects):
model = keras.models.load_model(ckpt_path)
return TFKerasUtil.serialize_model(model)