在Google Cloud Compute Engine上部署自定义datalab vm的最简单方法是什么？(What is the simplest approach to deploying a

在Google Cloud Compute Engine上部署自定义datalab vm的最简单方法是什么？(What is the simplest approach to deploying a custom datalab vm on Google Cloud Compute Engine)

这个wiki非常有助于提供有关在GCE中部署自定义datalab vm的过程的大量细节。

特别是， “释放构建”页面记录了最新的Datalab Docker容器的以下位置：gcr.io/cloud_datalab/datalab:latest。

最终，对于生产，我将遵守所有发布步骤，特别是在本地测试。 但是，由于我在Sandbox工作，我想相信我可以简化这个过程。 也就是说，我想简单地克隆datalab：latest，进行更改，将其保存到我的Git仓库，然后使用Deployer App创建一个新版本（具有适当的名称）并设置container = deployer URL参数到我的自定义图像（即Docker文件）。

我的问题是：

这看起来像是一种正确而合理的方法吗？ 这是datalab：最新的Docker文件吗？ 是dockerfile.in我需要克隆然后进行更改吗？

This wiki was very helpful in providing a lot of detail about the process of deploying a custom datalab vm in the GCE.

In particular, the 'Releasing A Build' page documented the following location for the most current Datalab Docker container: gcr.io/cloud_datalab/datalab:latest.

Ultimately, for production, I'll conform to all of the release steps, particularly testing locally. Since I'm working in a Sandbox, however, I'd like to believe that I can streamline the process. That is, I'd like to simply clone the datalab:latest, make my changes, save it to my Git repo, and then use the Deployer App to create a new version (with an appropriate name) and set the container = deployer URL parameter to my customized image (i.e. Docker file).

My questions are:

Does this seem like a correct and reasonable approach ? Is this the datalab:latest Docker file? Is the dockerfile.in what I need to clone and then make changes to ?

最满意答案

是。 是。 不，你应该克隆所有项目，因为Dockerfile.in引用了项目。 例如config / ipython.py。 你可以在116行看到这一点。 （＃添加构建工件） Yes. Yes. No, you should clone all project, because Dockerfile.in has references to projects. For example config/ipython.py. You can see this in the 116 line. (# Add build artifacts)