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192 lines
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192 lines
5.5 KiB
Markdown
[#]: collector: (lujun9972)
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[#]: translator: (geekpi)
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[#]: reviewer: (wxy)
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[#]: publisher: (wxy)
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[#]: subject: (Create a containerized machine learning model)
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[#]: via: (https://fedoramagazine.org/create-containerized-machine-learning-model/)
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[#]: author: (Sven Bösiger)
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[#]: url: (https://linux.cn/article-10349-1.html)
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创建一个容器化的机器学习模型
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======
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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。
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### 准备
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首先，使用以下命令安装 Podman：
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```
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sudo dnf -y install podman
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```
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接下来，为容器创建一个新文件夹并切换到该目录。
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```
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mkdir deployment_container && cd deployment_container
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```
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### TensorFlow 模型的 REST API
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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。
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使用以下命令在 `deployment_container` 目录中克隆它：
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```
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git clone https://github.com/svenboesiger/titanic_tf_ml_model.git
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```
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#### prediction.py 和 ml_model/
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[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。
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#### swagger.yaml
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[swagger.yaml][12] 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。
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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。
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```
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swagger: "2.0"
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info:
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description: This is the swagger file that goes with our server code
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version: "1.0.0"
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title: Tensorflow Podman Article
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consumes:
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- "application/json"
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produces:
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- "application/json"
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basePath: "/"
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paths:
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/survival_probability:
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post:
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operationId: "prediction.post"
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tags:
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- "Prediction"
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summary: "The prediction data structure provided by the server application"
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description: "Retrieve the chance of surviving the titanic disaster"
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parameters:
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- in: body
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name: passenger
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required: true
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schema:
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$ref: '#/definitions/PredictionPost'
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responses:
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'201':
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description: 'Survival probability of an individual Titanic passenger'
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definitions:
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PredictionPost:
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type: object
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```
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#### server.py 和 requirements.txt
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[server.py][8] 定义了启动 Connexion 服务器的入口点。
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```
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import connexion
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app = connexion.App(__name__, specification_dir='./')
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app.add_api('swagger.yaml')
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if __name__ == '__main__':
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app.run(debug=True)
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```
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[requirements.txt][9] 定义了运行程序所需的 python 包。
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```
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connexion
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tensorflow
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pandas
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```
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### 容器化！
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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：
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```
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FROM fedora:28
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# File Author / Maintainer
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MAINTAINER Sven Boesiger <donotspam@ujelang.com>
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# Update the sources
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RUN dnf -y update --refresh
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# Install additional dependencies
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RUN dnf -y install libstdc++
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RUN dnf -y autoremove
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# Copy the application folder inside the container
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ADD /titanic_tf_ml_model /titanic_tf_ml_model
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# Get pip to download and install requirements:
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RUN pip3 install -r /titanic_tf_ml_model/requirements.txt
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# Expose ports
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EXPOSE 5000
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# Set the default directory where CMD will execute
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WORKDIR /titanic_tf_ml_model
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# Set the default command to execute
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# when creating a new container
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CMD python3 server.py
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```
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接下来，使用以下命令构建容器镜像：
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```
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podman build -t ml_deployment .
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```
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### 运行容器
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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：
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```
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podman run -p 5000:5000 ml_deployment
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```
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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：
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![][11]
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当然，你现在也可以在应用中通过 REST API 访问模型。
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--------------------------------------------------------------------------------
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via: https://fedoramagazine.org/create-containerized-machine-learning-model/
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作者：[Sven Bösiger][a]
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选题：[lujun9972][b]
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译者：[geekpi](https://github.com/geekpi)
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校对：[wxy](https://github.com/wxy)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://fedoramagazine.org/author/r00nz/
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[b]: https://github.com/lujun9972
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[1]: https://www.tensorflow.org
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[2]: https://connexion.readthedocs.io/en/latest/
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[3]: https://fedoramagazine.org/running-containers-with-podman/
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[4]: https://github.com/svenboesiger/titanic_tf_ml_model
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[5]: https://github.com/svenboesiger/titanic_tf_ml_model/blob/master/prediction.py
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[6]: https://github.com/svenboesiger/titanic_tf_ml_model/tree/master/ml_model/titanic
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[7]: https://github.com/OAI/OpenAPI-Specification/blob/master/versions/2.0.md
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[8]: https://github.com/svenboesiger/titanic_tf_ml_model/blob/master/server.py
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[9]: https://github.com/svenboesiger/titanic_tf_ml_model/blob/master/requirements.txt
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[10]: http://0.0.0.0:5000/
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[11]: https://fedoramagazine.org/wp-content/uploads/2018/10/Screenshot-from-2018-10-27-14-46-56-682x1024.png
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[12]: https://github.com/svenboesiger/titanic_tf_ml_model/blob/master/swagger.yaml
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