1# Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================== 15"""SavedModel simple save functionality.""" 16 17from __future__ import absolute_import 18from __future__ import division 19from __future__ import print_function 20 21from tensorflow.python.framework import ops 22from tensorflow.python.saved_model import builder 23from tensorflow.python.saved_model import signature_constants 24from tensorflow.python.saved_model import signature_def_utils 25from tensorflow.python.saved_model import tag_constants 26from tensorflow.python.util import deprecation 27from tensorflow.python.util.tf_export import tf_export 28 29 30@tf_export(v1=['saved_model.simple_save']) 31@deprecation.deprecated( 32 None, 33 'This function will only be available through the v1 compatibility ' 34 'library as tf.compat.v1.saved_model.simple_save.') 35def simple_save(session, export_dir, inputs, outputs, legacy_init_op=None): 36 """Convenience function to build a SavedModel suitable for serving. 37 38 In many common cases, saving models for serving will be as simple as: 39 40 simple_save(session, 41 export_dir, 42 inputs={"x": x, "y": y}, 43 outputs={"z": z}) 44 45 Although in many cases it's not necessary to understand all of the many ways 46 to configure a SavedModel, this method has a few practical implications: 47 - It will be treated as a graph for inference / serving (i.e. uses the tag 48 `tag_constants.SERVING`) 49 - The SavedModel will load in TensorFlow Serving and supports the 50 [Predict 51 API](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/predict.proto). 52 To use the Classify, Regress, or MultiInference APIs, please 53 use either 54 [tf.Estimator](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator) 55 or the lower level 56 [SavedModel 57 APIs](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md). 58 - Some TensorFlow ops depend on information on disk or other information 59 called "assets". These are generally handled automatically by adding the 60 assets to the `GraphKeys.ASSET_FILEPATHS` collection. Only assets in that 61 collection are exported; if you need more custom behavior, you'll need to 62 use the 63 [SavedModelBuilder](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/builder.py). 64 65 More information about SavedModel and signatures can be found here: 66 https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md. 67 68 Args: 69 session: The TensorFlow session from which to save the meta graph and 70 variables. 71 export_dir: The path to which the SavedModel will be stored. 72 inputs: dict mapping string input names to tensors. These are added 73 to the SignatureDef as the inputs. 74 outputs: dict mapping string output names to tensors. These are added 75 to the SignatureDef as the outputs. 76 legacy_init_op: Legacy support for op or group of ops to execute after the 77 restore op upon a load. 78 """ 79 signature_def_map = { 80 signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: 81 signature_def_utils.predict_signature_def(inputs, outputs) 82 } 83 b = builder.SavedModelBuilder(export_dir) 84 b.add_meta_graph_and_variables( 85 session, 86 tags=[tag_constants.SERVING], 87 signature_def_map=signature_def_map, 88 assets_collection=ops.get_collection(ops.GraphKeys.ASSET_FILEPATHS), 89 main_op=legacy_init_op, 90 clear_devices=True) 91 b.save() 92