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+Create a containerized machine learning model
+======
+
+![](https://fedoramagazine.org/wp-content/uploads/2018/10/machinelearning-816x345.jpg)
+
+After data scientists have created a machine learning model, it has to be deployed into production. To run it on different infrastructures, using containers and exposing the model via a REST API is a common way to deploy a machine learning model. This article demonstrates how to roll out a [TensorFlow][1] machine learning model, with a REST API delivered by [Connexion][2] in a container with [Podman][3].
+
+### Preparation
+
+First, install Podman with the following command:
+
+```
+sudo dnf -y install podman
+```
+
+Next, create a new folder for the container and switch to that directory.
+
+```
+mkdir deployment_container && cd deployment_container
+```
+
+### REST API for the TensorFlow model
+
+The next step is to create the REST-API for the machine learning model. This [github repository][4] contains a pretrained model, and well as the setup already configured for getting the REST API working.
+
+Clone this in the deployment_container directory with the command:
+
+```
+git clone https://github.com/svenboesiger/titanic_tf_ml_model.git
+```
+
+#### prediction.py & ml_model/
+
+The [prediction.py][5] file allows for a Tensorflow prediction, while the weights for the 20x20x20 neural network are located in folder [ml_model/][6].
+
+#### swagger.yaml
+
+The file swagger.yaml defines the API for the Connexion library using the [Swagger specification][7]. This file contains all of the information necessary to configure your server to provide input parameter validation, output response data validation, URL endpoint definition.
+
+As a bonus Connexion will provide you also with a simple but useful single page web application that demonstrates using the API with JavaScript and updating the DOM with it.
+
+```
+swagger: "2.0"
+info:
+  description: This is the swagger file that goes with our server code
+  version: "1.0.0"
+  title: Tensorflow Podman Article
+consumes:
+  - "application/json"
+produces:
+  - "application/json"
+
+
+basePath: "/"
+
+paths:
+  /survival_probability:
+    post:
+      operationId: "prediction.post"
+      tags:
+        - "Prediction"
+      summary: "The prediction data structure provided by the server application"
+      description: "Retrieve the chance of surviving the titanic disaster"
+      parameters:
+        - in: body
+          name: passenger
+          required: true
+          schema:
+            $ref: '#/definitions/PredictionPost'
+      responses:
+        '201':
+          description: 'Survival probability of an individual Titanic passenger'
+
+definitions:
+  PredictionPost:
+    type: object
+```
+
+#### server.py & requirements.txt
+
+[server.py][8] defines an entry point to start the Connexion server.
+
+```
+import connexion
+
+app = connexion.App(__name__, specification_dir='./')
+
+app.add_api('swagger.yaml')
+
+if __name__ == '__main__':
+ app.run(debug=True)
+```
+
+[requirements.txt][9] defines the python requirements we need to run the program.
+
+```
+connexion
+tensorflow
+pandas
+```
+
+### Containerize!
+
+For Podman to be able to build an image, create a new file called “Dockerfile” in the **deployment_container** directory created in the preparation step above:
+
+```
+FROM fedora:28
+
+# File Author / Maintainer
+MAINTAINER Sven Boesiger <donotspam@ujelang.com>
+
+# Update the sources
+RUN dnf -y update --refresh
+
+# Install additional dependencies
+RUN dnf -y install libstdc++
+
+RUN dnf -y autoremove
+
+# Copy the application folder inside the container
+ADD /titanic_tf_ml_model /titanic_tf_ml_model
+
+# Get pip to download and install requirements:
+RUN pip3 install -r /titanic_tf_ml_model/requirements.txt
+
+# Expose ports
+EXPOSE 5000
+
+# Set the default directory where CMD will execute
+WORKDIR /titanic_tf_ml_model
+
+# Set the default command to execute
+# when creating a new container
+CMD python3 server.py
+```
+
+Next, build the container image with the command:
+
+```
+podman build -t ml_deployment .
+```
+
+### Run the container
+
+With the Container image built and ready to go, you can run it locally with the command:
+
+```
+podman run -p 5000:5000 ml_deployment
+```
+
+Navigate to [http://0.0.0.0:5000/ui][10] in your web browser to access the Swagger/Connexion UI and to test-drive the model:
+
+![][11]
+
+Of course you can now also access the model with your application via the REST-API.
+
+
+--------------------------------------------------------------------------------
+
+via: https://fedoramagazine.org/create-containerized-machine-learning-model/
+
+作者：[Sven Bösiger][a]
+选题：[lujun9972][b]
+译者：[译者ID](https://github.com/译者ID)
+校对：[校对者ID](https://github.com/校对者ID)
+
+本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
+
+[a]: https://fedoramagazine.org/author/r00nz/
+[b]: https://github.com/lujun9972
+[1]: https://www.tensorflow.org
+[2]: https://connexion.readthedocs.io/en/latest/
+[3]: https://fedoramagazine.org/running-containers-with-podman/
+[4]: https://github.com/svenboesiger/titanic_tf_ml_model
+[5]: https://github.com/svenboesiger/titanic_tf_ml_model/blob/master/prediction.py
+[6]: https://github.com/svenboesiger/titanic_tf_ml_model/tree/master/ml_model/titanic
+[7]: https://github.com/OAI/OpenAPI-Specification/blob/master/versions/2.0.md
+[8]: https://github.com/svenboesiger/titanic_tf_ml_model/blob/master/server.py
+[9]: https://github.com/svenboesiger/titanic_tf_ml_model/blob/master/requirements.txt
+[10]: http://0.0.0.0:5000/
+[11]: https://fedoramagazine.org/wp-content/uploads/2018/10/Screenshot-from-2018-10-27-14-46-56-682x1024.png