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More formatting issues (tensorflow#551)
* Add title to pick up TOC * Fix linebreaks in links * Fix some formatting issues; slight reordering
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@@ -7,4 +7,5 @@ serving_advanced.md
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serving_inception.md
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custom_servable.md
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custom_source.md
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signature_defs.md
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docker.md

serving_basic.md

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@@ -112,35 +112,36 @@ You can add meta graph and variables to the builder using
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As an example for how `predict_signature` is defined, the util takes the
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following arguments:
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* `inputs={'images': tensor_info_x}` specifies the input tensor info.
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* `outputs={'scores': tensor_info_y}` specifies the scores tensor info.
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Note that `tensor_info_x` and `tensor_info_y` have the structure of
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`tensorflow::TensorInfo` protocol buffer defined [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/meta_graph.proto).
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To easily build tensor infos, the TensorFlow SavedModel API also provides
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[utils.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/utils.py),
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with [related TensorFlow 1.0 API documentation](https://www.tensorflow.org/api_docs/python/tf/saved_model/utils).
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Also, note that `images` and `scores` are tensor alias names. They can be
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whatever unique strings you want, and they will become the logical names
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of tensor `x` and `y` that you refer to for tensor binding when sending
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prediction requests later.
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For instance, if `x` refers to the tensor with name 'long_tensor_name_foo'
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and `y` refers to the tensor with name 'generated_tensor_name_bar',
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`builder` will store tensor logical name to real name mapping
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('images' -> 'long_tensor_name_foo') and ('scores' -> 'generated_tensor_name_bar').
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This allows the user to refer to these tensors with their logical names
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when running inference.
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* `method_name` is the method used for the inference. For Prediction
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requests, it should be set to `tensorflow/serving/predict`. For other
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method names, see [signature_constants.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/signature_constants.py)
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and related [TensorFlow 1.0 API documentation](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants).
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In addition to the description above, documentation related to signature def
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structure and how to set up them up can be found [here](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/g3doc/signature_defs.md).
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* `inputs={'images': tensor_info_x}` specifies the input tensor info.
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* `outputs={'scores': tensor_info_y}` specifies the scores tensor info.
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* `method_name` is the method used for the inference. For Prediction
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requests, it should be set to `tensorflow/serving/predict`. For other
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method names, see [signature_constants.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/signature_constants.py)
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and related [TensorFlow 1.0 API documentation](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants).
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Note that `tensor_info_x` and `tensor_info_y` have the structure of
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`tensorflow::TensorInfo` protocol buffer defined [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/meta_graph.proto).
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To easily build tensor infos, the TensorFlow SavedModel API also provides
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[utils.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/utils.py),
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with [related TensorFlow 1.0 API documentation](https://www.tensorflow.org/api_docs/python/tf/saved_model/utils).
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Also, note that `images` and `scores` are tensor alias names. They can be
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whatever unique strings you want, and they will become the logical names
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of tensor `x` and `y` that you refer to for tensor binding when sending
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prediction requests later.
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For instance, if `x` refers to the tensor with name 'long_tensor_name_foo' and
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`y` refers to the tensor with name 'generated_tensor_name_bar', `builder` will
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store tensor logical name to real name mapping ('images' ->
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'long_tensor_name_foo') and ('scores' -> 'generated_tensor_name_bar'). This
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allows the user to refer to these tensors with their logical names when
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running inference.
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Note: In addition to the description above, documentation related to signature
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def structure and how to set up them up can be found [here](signature_defs.md).
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Let's run it!
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