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tf_savedmodel_artifact.py
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tf_savedmodel_artifact.py
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# Copyright 2019 Atalaya Tech, Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import shutil
import logging
from bentoml.utils import pathlib
from bentoml.artifact import BentoServiceArtifact, BentoServiceArtifactWrapper
logger = logging.getLogger(__name__)
def _is_path_like(p):
return isinstance(p, (str, bytes, pathlib.PurePath, os.PathLike))
def _load_tf_saved_model(path):
try:
import tensorflow as tf
TF2 = tf.__version__.startswith('2')
except ImportError:
raise ImportError("Tensorflow package is required to use TfSavedModelArtifact")
if TF2:
return tf.saved_model.load(path)
else:
return tf.compat.v2.saved_model.load(path)
class TensorflowSavedModelArtifact(BentoServiceArtifact):
"""
Abstraction for saving/loading Tensorflow model in tf.saved_model format
Args:
name (string): name of the artifact
Raises:
ImportError: tensorflow package is required for TensorflowSavedModelArtifact
Example usage:
>>> import tensorflow as tf
>>> class Adder(tf.Module):
>>> @tf.function(input_signature=[tf.TensorSpec(shape=None, dtype=tf.float32)])
>>> def add(self, x):
>>> return x + x + 1.
>>> to_export = Adder()
>>>
>>> import bentoml
>>> from bentoml.handlers import JsonHandler
>>> from bentoml.artifact import TensorflowSavedModelArtifact
>>>
>>> @bentoml.env(pip_dependencies=["tensorflow"])
>>> @bentoml.artifacts([TensorflowSavedModelArtifact('model')])
>>> class TfModelService(bentoml.BentoService):
>>>
>>> @bentoml.api(JsonHandler)
>>> def predict(self, json):
>>> input_data = json['input']
>>> prediction = self.artifacts.model.add(input_data)
>>> return prediction.numpy()
>>>
>>> svc = TfModelService()
>>>
>>> # Option 1: pack directly with Tensorflow trackable object
>>> svc.pack('model', to_export)
>>>
>>> # Option 2: save to file path then pack
>>> tf.saved_model.save(to_export, '/tmp/adder/1')
>>> svc.pack('model', '/tmp/adder/1')
"""
def _saved_model_path(self, base_path):
return os.path.join(base_path, self.name + '_saved_model')
def pack(
self, obj, signatures=None, options=None
): # pylint:disable=arguments-differ
"""
Args:
obj: Either a path(str/byte/os.PathLike) containing exported
`tf.saved_model` files, or a Trackable object mapping to the `obj`
parameter of `tf.saved_model.save`
signatures:
options:
"""
if _is_path_like(obj):
return _ExportedTensorflowSavedModelArtifactWrapper(self, obj)
return _TensorflowSavedModelArtifactWrapper(self, obj, signatures, options)
def load(self, path):
saved_model_path = self._saved_model_path(path)
loaded_model = _load_tf_saved_model(saved_model_path)
return self.pack(loaded_model)
class _ExportedTensorflowSavedModelArtifactWrapper(BentoServiceArtifactWrapper):
def __init__(self, spec, path):
super(_ExportedTensorflowSavedModelArtifactWrapper, self).__init__(spec)
self.path = path
self.model = None
def save(self, dst):
# Copy exported SavedModel model directory to BentoML saved artifact directory
shutil.copytree(self.path, self.spec._saved_model_path(dst))
def get(self):
if not self.model:
self.model = _load_tf_saved_model(self.path)
return self.model
class _TensorflowSavedModelArtifactWrapper(BentoServiceArtifactWrapper):
def __init__(self, spec, obj, signatures=None, options=None):
super(_TensorflowSavedModelArtifactWrapper, self).__init__(spec)
self.obj = obj
self.signatures = signatures
self.options = options
def save(self, dst):
try:
import tensorflow as tf
TF2 = tf.__version__.startswith('2')
except ImportError:
raise ImportError(
"Tensorflow package is required to use TfSavedModelArtifact."
)
if TF2:
return tf.saved_model.save(
self.obj,
self.spec._saved_model_path(dst),
signatures=self.signatures,
options=self.options,
)
else:
if self.options:
logger.warning(
"Parameter 'options: %s' is ignored when using Tensorflow "
"version 1",
str(self.options),
)
return tf.saved_model.save(
self.obj, self.spec._saved_model_path(dst), signatures=self.signatures
)
def get(self):
return self.obj