cloud 部署_使用Google Cloud AI平台开发，训练和部署TensorFlow模型

cloud 部署

实用指南 (A Practical Guide)

The TensorFlow ecosystem has become very popular for developing applications involving deep learning. One of the reasons is that it has a strong community and a lot of tools have been developed around the core library to support developers. In this tutorial, I will guide you through how to prototype models in google colab, train it on Google Cloud AI Platform, and deploy the finalized model on Google Cloud AI Platform for production. I will include the working Google colab notebooks to recreate the work.

TensorFlow生态系统已经非常流行用于开发涉及深度学习的应用程序。 原因之一是它具有强大的社区，并且围绕核心库开发了许多工具来支持开发人员。 在本教程中，我将指导您完成如何在google colab中对模型进行原型制作，如何在Google Cloud AI Platform上进行训练以及如何在Google Cloud AI Platform上部署最终模型进行生产。 我将包括工作中的Google colab笔记本以重新创建工作。

Google colab is a free resource for prototyping models in TensorFlow and comes with various runtime. Preparing a machine with GPU or TPU could be costly to start with however users can start with free GPU with google colab. Bear in mind, colab has limited resources and might not be suitable for properly training models requiring large compute resources. Nonetheless, colab is a perfect tool for prototyping your models and some initial experimentation.

Google colab是TensorFlow中用于模型原型的免费资源，并带有各种运行时。 首先，准备一台配备GPU或TPU的机器可能会很昂贵，但是用户可以使用带有Google colab的免费GPU来开始。 请记住，colab资源有限，可能不适用于需要大量计算资源的正确培训模型。 尽管如此，colab是用于制作模型原型和进行一些初始实验的理想工具。

A block diagram to visualize the workflow

可视化工作流程的框图

Google Cloud Platform上的培训模型 (Training Model on Google Cloud Platform)

Once you are satisfied with your model pipeline, it is time to train the model with the proper number of EPOCHS and full datasets. As you might know, training deep learning models requires a long time and a large cluster of CPU’s GPUs or TPU’s. One option is that users set up their own computing cluster which is costly and time-consuming most of the time. Another option is to use cloud computing to training the model and pay as you go. TensorFlow team has released a package called Tensorflow Cloud to let users train the models on the Google Cloud platform without any hassle. I have followed steps from Train your TensorFlow model on Google Cloud using TensorFlow Cloud blog and will share some issues I have faced to make it work. Some of the pre-requisites are ad defined in the project guidelines for submitting a training job to the GCP platform.

对模型管道感到满意之后，就该使用适当数量的EPOCHS和完整数据集来训练模型。 如您所知，训练深度学习模型需要很长时间，并且需要大量的CPU GPU或TPU。 一种选择是用户设置自己的计算集群，这在大多数情况下既昂贵又耗时。 另一种选择是使用云计算来训练模型并按需付费。 TensorFlow团队发布了一个名为Tensorflow Cloud的软件包，使用户可以轻松地在Google Cloud平台上训练模型。 我已按照使用TensorFlow Cloud博客在Google Cloud上训练您的TensorFlow模型的步骤进行了操作 ，并将分享我为使其工作而面临的一些问题。 在项目准则中已定义了一些先决条件，以便向G