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Fix admonitions
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‎tensorflow_serving/g3doc/guide/custom_op.md

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@@ -11,9 +11,10 @@ explicitly:
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[this guide](https://github.com/tensorflow/custom-op))
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* You are using an already implemented op that is not shipped with TensorFlow
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Note: Starting in version 2.0, TensorFlow no longer distributes the contrib
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module; if you are serving a TensorFlow program using contrib ops, use this
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guide to link these ops into ModelServer explicitly.
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!!! Note
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Starting in version 2.0, TensorFlow no longer distributes the contrib
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module; if you are serving a TensorFlow program using contrib ops, use this
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guide to link these ops into ModelServer explicitly.
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Regardless of whether you implemented the op or not, in order to serve a model
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with custom ops, you need access to the source of the op. This guide walks you

‎tensorflow_serving/g3doc/guide/docker.md

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@@ -200,8 +200,9 @@ curl -d '{"instances": [1.0, 2.0, 5.0]}' \
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-X POST http://localhost:8501/v1/models/half_plus_two:predict
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```
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NOTE: Older versions of Windows and other systems without curl can download it
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[here](https://curl.haxx.se/download.html).
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!!! NOTE
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Older versions of Windows and other systems without curl can download it
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[here](https://curl.haxx.se/download.html).
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This should return a set of values:
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also pass the name of the model as an environment variable, which will be
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important when we query the model.
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TIP: Before querying the model, be sure to wait till you see a message like the
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following, indicating that the server is ready to receive requests:
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!!! TIP
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Before querying the model, be sure to wait till you see a message like the
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following, indicating that the server is ready to receive requests:
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```shell
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2018-07-27 00:07:20.773693: I tensorflow_serving/model_servers/main.cc:333]
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Exporting HTTP/REST API at:localhost:8501 ...
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```
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```shell
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2018-07-27 00:07:20.773693: I tensorflow_serving/model_servers/main.cc:333]
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Exporting HTTP/REST API at:localhost:8501 ...
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```
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To query the model using the predict API, you can run
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-X POST http://localhost:8501/v1/models/half_plus_two:predict
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```
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NOTE: Older versions of Windows and other systems without curl can download it
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[here](https://curl.haxx.se/download.html).
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!!! NOTE
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Older versions of Windows and other systems without curl can download it
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[here](https://curl.haxx.se/download.html).
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This should return a set of values:
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```json
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{ "predictions": [2.5, 3.0, 4.5] }
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```
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TIP: Trying to run the GPU model on a machine without a GPU or without a working
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GPU build of TensorFlow Model Server will result in an error that looks like:
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!!! TIP
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Trying to run the GPU model on a machine without a GPU or without a working
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GPU build of TensorFlow Model Server will result in an error that looks like:
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```shell
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Cannot assign a device for operation 'a': Operation was explicitly assigned to /device:GPU:0
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```
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```shell
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Cannot assign a device for operation 'a': Operation was explicitly assigned to /device:GPU:0
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```
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More information on using the RESTful API can be found [here](../api/api_rest.md).
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‎tensorflow_serving/g3doc/guide/serving_basic.md

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@@ -140,8 +140,9 @@ store tensor logical name to real name mapping ('images' ->
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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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!!! Note
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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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‎tensorflow_serving/g3doc/guide/setup.md

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@@ -8,8 +8,9 @@ The easiest and most straight-forward way of using TensorFlow Serving is with
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[Docker images](docker.md). We highly recommend this route unless you have
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specific needs that are not addressed by running in a container.
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TIP: This is also the easiest way to get TensorFlow Serving working with [GPU
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support](docker.md#serving-with-docker-using-your-gpu).
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!!! TIP
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This is also the easiest way to get TensorFlow Serving working with [GPU
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support](docker.md#serving-with-docker-using-your-gpu).
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### Installing using APT
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<!-- common_typos_enable -->
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Note: In the above commands, replace tensorflow-model-server with
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tensorflow-model-server-universal if your processor does not support AVX
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instructions.
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!!! Note
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In the above commands, replace tensorflow-model-server with
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tensorflow-model-server-universal if your processor does not support AVX
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instructions.
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## Building from source
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[[CPU](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/tools/docker/Dockerfile.devel),
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[GPU](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/tools/docker/Dockerfile.devel-gpu)].
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Note: Currently we only support building binaries that run on Linux.
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!!! Note
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Currently we only support building binaries that run on Linux.
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#### Installing Docker
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tools/run_in_docker.sh bazel build --copt=-mavx2 tensorflow_serving/...
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```
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Note: These instruction sets are not available on all machines, especially with
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older processors. Use the default `--config=nativeopt` to build an optimized
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version of TensorFlow Serving for your processor if you are in doubt.
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!!! Note
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These instruction sets are not available on all machines, especially with
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older processors. Use the default `--config=nativeopt` to build an optimized
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version of TensorFlow Serving for your processor if you are in doubt.
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##### Building with GPU Support

‎tensorflow_serving/g3doc/tutorials/building_with_docker.md

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@@ -90,9 +90,10 @@ docker build --pull -t $USER/tensorflow-serving-devel -f Dockerfile.devel .
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docker build --pull -t $USER/tensorflow-serving-devel-gpu -f Dockerfile.devel-gpu .
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```
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TIP: Before attempting to build an image, check the Docker Hub
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[tensorflow/serving repo](http://hub.docker.com/r/tensorflow/serving/tags/) to
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make sure an image that meets your needs doesn't already exist.
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!!! TIP
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Before attempting to build an image, check the Docker Hub
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[tensorflow/serving repo](http://hub.docker.com/r/tensorflow/serving/tags/) to
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make sure an image that meets your needs doesn't already exist.
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Building from sources consumes a lot of RAM. If RAM is an issue on your system,
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you may limit RAM usage by specifying `--local_ram_resources=2048` while
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docker run -it -p 8500:8500 $USER/tensorflow-serving-devel
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TIP: If you're running a GPU image, be sure to run using the NVIDIA runtime
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[`--runtime=nvidia`](https://github.com/NVIDIA/nvidia-docker#quick-start).
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!!! TIP
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If you're running a GPU image, be sure to run using the NVIDIA runtime
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[`--runtime=nvidia`](https://github.com/NVIDIA/nvidia-docker#quick-start).
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From here, you can follow the instructions for
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[testing a development environment](#testing-the-development-environment).

‎tensorflow_serving/g3doc/tutorials/performance.md

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@@ -12,9 +12,10 @@ Please use the [Profile Inference Requests with TensorBoard](tensorboard.md)
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guide to understand the underlying behavior of your model's computation on
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inference requests, and use this guide to iteratively improve its performance.
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Note: If the following quick tips do not solve your problem, please read the
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longer discussion to develop a deep understanding of what affects TensorFlow
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Serving's performance.
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!!! Note
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If the following quick tips do not solve your problem, please read the
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longer discussion to develop a deep understanding of what affects TensorFlow
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Serving's performance.
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## Quick Tips
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