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 tensorflow.python.framework import ops 18from tensorflow.python.saved_model import builder 19from tensorflow.python.saved_model import signature_constants 20from tensorflow.python.saved_model import signature_def_utils 21from tensorflow.python.saved_model import tag_constants 22from tensorflow.python.util import deprecation 23from tensorflow.python.util.tf_export import tf_export 24 25 26@tf_export(v1=['saved_model.simple_save']) 27@deprecation.deprecated( 28 None, 29 'This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate ' 30 'for instructions on how to migrate your code to TensorFlow v2.') 31def simple_save(session, export_dir, inputs, outputs, legacy_init_op=None): 32 """Convenience function to build a SavedModel suitable for serving. 33 34 In many common cases, saving models for serving will be as simple as: 35 36 simple_save(session, 37 export_dir, 38 inputs={"x": x, "y": y}, 39 outputs={"z": z}) 40 41 Although in many cases it's not necessary to understand all of the many ways 42 to configure a SavedModel, this method has a few practical implications: 43 - It will be treated as a graph for inference / serving (i.e. uses the tag 44 `saved_model.SERVING`) 45 - The SavedModel will load in TensorFlow Serving and supports the 46 [Predict 47 API](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/predict.proto). 48 To use the Classify, Regress, or MultiInference APIs, please 49 use either 50 [tf.Estimator](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator) 51 or the lower level 52 [SavedModel 53 APIs](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md). 54 - Some TensorFlow ops depend on information on disk or other information 55 called "assets". These are generally handled automatically by adding the 56 assets to the `GraphKeys.ASSET_FILEPATHS` collection. Only assets in that 57 collection are exported; if you need more custom behavior, you'll need to 58 use the 59 [SavedModelBuilder](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/builder.py). 60 61 More information about SavedModel and signatures can be found here: 62 https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md. 63 64 Args: 65 session: The TensorFlow session from which to save the meta graph and 66 variables. 67 export_dir: The path to which the SavedModel will be stored. 68 inputs: dict mapping string input names to tensors. These are added 69 to the SignatureDef as the inputs. 70 outputs: dict mapping string output names to tensors. These are added 71 to the SignatureDef as the outputs. 72 legacy_init_op: Legacy support for op or group of ops to execute after the 73 restore op upon a load. 74 """ 75 signature_def_map = { 76 signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: 77 signature_def_utils.predict_signature_def(inputs, outputs) 78 } 79 b = builder.SavedModelBuilder(export_dir) 80 b.add_meta_graph_and_variables( 81 session, 82 tags=[tag_constants.SERVING], 83 signature_def_map=signature_def_map, 84 assets_collection=ops.get_collection(ops.GraphKeys.ASSET_FILEPATHS), 85 main_op=legacy_init_op, 86 clear_devices=True) 87 b.save() 88