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Fix bash command listing
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tensorflow_serving/g3doc/docker.md

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One of the easiest ways to get started using TensorFlow Serving is with
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[Docker](http://www.docker.com/).
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<pre class="prettyprint lang-bsh">
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```shell
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# Download the TensorFlow Serving Docker image and repo
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<code class="devsite-terminal">docker pull tensorflow/serving</code><br/>
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<code class="devsite-terminal">git clone https://github.com/tensorflow/serving</code>
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docker pull tensorflow/serving
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git clone https://github.com/tensorflow/serving
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# Location of demo models
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<code class="devsite-terminal">TESTDATA="$(pwd)/serving/tensorflow_serving/servables/tensorflow/testdata"</code>
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TESTDATA="$(pwd)/serving/tensorflow_serving/servables/tensorflow/testdata"
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# Start TensorFlow Serving container and open the REST API port
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<code class="devsite-terminal">docker run -t --rm -p 8501:8501 \
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docker run -t --rm -p 8501:8501 \
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-v "$TESTDATA/saved_model_half_plus_two_cpu:/models/half_plus_two" \
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-e MODEL_NAME=half_plus_two \
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tensorflow/serving &</code>
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tensorflow/serving &
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# Query the model using the predict API
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<code class="devsite-terminal">curl -d '{"instances": [1.0, 2.0, 5.0]}' \
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-X POST http://localhost:8501/v1/models/half_plus_two:predict</code><br/>
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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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# Returns => { "predictions": [2.5, 3.0, 4.5] }
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</pre>
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```
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For additional serving endpoints, see the <a href="./api_rest.md">Client REST API</a>.
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For additional serving endpoints, see the [Client REST API](api_rest.md).
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## Install Docker
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