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[#]: author: (Sven Bösiger) [#]: author: (Sven Bösiger)
[#]: url: ( ) [#]: url: ( )
Create a containerized machine learning model 创建一个容器化的机器学习模型
====== ======
![](https://fedoramagazine.org/wp-content/uploads/2018/10/machinelearning-816x345.jpg) ![](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]. 数据科学家在创建机器学习模型后，必须将其部署到生产中。要在不同的基础架构上运行它，使用容器并通过 REST API 公开模型是部署机器学习模型的常用方法。本文演示了如何在 [Podman][3] 容器中使用 [Connexion][2] 推出使用 REST API 的 [TensorFlow][1] 机器学习模型。
### Preparation ### 准备
First, install Podman with the following command: 首先，使用以下命令安装 Podman：
``` ```
sudo dnf -y install podman sudo dnf -y install podman
``` ```
Next, create a new folder for the container and switch to that directory. 接下来，为容器创建一个新文件夹并切换到该目录。
``` ```
mkdir deployment_container && cd deployment_container mkdir deployment_container && cd deployment_container
``` ```
### REST API for the TensorFlow model ### TensorFlow 模型的 REST API
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. 下一步是为机器学习模型创建 REST API。这个 [github 仓库][4]包含一个预训练模型，以及能让 REST API 工作的设置。
Clone this in the deployment_container directory with the command: 使用以下命令在 deployment_container 目录中克隆它：
``` ```
git clone https://github.com/svenboesiger/titanic_tf_ml_model.git git clone https://github.com/svenboesiger/titanic_tf_ml_model.git
``` ```
#### prediction.py & ml_model/ #### prediction.py 和 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]. [prediction.py][5] 能进行 Tensorflow 预测，而 20x20x20 神经网络的权重位于文件夹 [ml_model/][6] 中。
#### swagger.yaml #### 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. swagger.yaml 使用 [Swagger规范][7] 定义 Connexion 库的 API。此文件包含让你的服务器提供输入参数验证、输出响应数据验证、URL 端点定义所需的所有信息。
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. 额外地，Connexion 还将给你提供一个简单但有用的单页 Web 应用，它演示了如何使用 Javascript 调用 API 和更新 DOM。
``` ```
swagger: "2.0" swagger: "2.0"
@ -85,9 +85,9 @@ definitions:
type: object type: object
``` ```
#### server.py & requirements.txt #### server.py 和 requirements.txt
[server.py][8] defines an entry point to start the Connexion server. [server.py][8] 定义了启动 Connexion 服务器的入口点。
``` ```
import connexion import connexion
@ -100,7 +100,7 @@ if __name__ == '__main__':
app.run(debug=True) app.run(debug=True)
``` ```
[requirements.txt][9] defines the python requirements we need to run the program. [requirements.txt][9] 定义了运行程序所需的 python 包。
``` ```
connexion connexion
@ -108,9 +108,9 @@ tensorflow
pandas pandas
``` ```
### Containerize! ### 容器化！
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: 为了让 Podman 构建映像，请在上面的准备步骤中创建的 **deployment_container** 目录中创建一个名为 “Dockerfile” 的新文件：
``` ```
FROM fedora:28 FROM fedora:28
@ -143,25 +143,25 @@ WORKDIR /titanic_tf_ml_model
CMD python3 server.py CMD python3 server.py
``` ```
Next, build the container image with the command: 接下来，使用以下命令构建容器镜像：
``` ```
podman build -t ml_deployment . podman build -t ml_deployment .
``` ```
### Run the container ### 运行容器
With the Container image built and ready to go, you can run it locally with the command: 随着容器镜像的构建和准备就绪，你可以使用以下命令在本地运行它：
``` ```
podman run -p 5000:5000 ml_deployment 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: 在 Web 浏览器中输入 [http://0.0.0.0:5000/ui][10] 访问 Swagger/Connexion UI 并测试模型：
![][11] ![][11]
Of course you can now also access the model with your application via the REST-API. 当然，你现在也可以在应用中通过 REST API 访问模型。
-------------------------------------------------------------------------------- --------------------------------------------------------------------------------
@ -170,7 +170,7 @@ via: https://fedoramagazine.org/create-containerized-machine-learning-model/
作者：[Sven Bösiger][a] 作者：[Sven Bösiger][a]
选题：[lujun9972][b] 选题：[lujun9972][b]
译者：[译者ID](https://github.com/译者ID) 译者：[geekpi](https://github.com/geekpi)
校对：[校对者ID](https://github.com/校对者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 本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出