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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