GCP中的AI Notebook和Cloud Datalab有什么区别?

问题描述

我一直在寻找这个问题的答案，这个问题是重复的，但是当我在两个不同的地方查看时，我需要澄清一下，答案有点相反.

I have searched for an answer to this question and this question is duplicate but I need clarification as I looked at two different places and answers are a bit opposite.

以下堆栈溢出 answer 提到 Google Cloud AI Platform Notebooks是Google Cloud Datalab的升级版.在以下Quora的页面，其中一位架构师提到 Cloud Datalab是在Jypyter Notebook的基础上构建的.

The following Stack Overflow answer mentions that Google Cloud AI Platform Notebooks is an upgraded version of Google Cloud Datalab. On the following Quora page, one of the architects mentions that Cloud Datalab is built on top of Jypyter Notebook.

Cloud Datalab正在添加自己的新网络.AI笔记本保留在现有网络中.在我当前环境的设置下，我不想增加维护额外网络和安全性来进行监视的开销，因此AI笔记本电脑是最直接的解决方案.但是我也想了解Cloud Datalab提供的好处.

Cloud Datalab is adding a new network of its own. AI Notebooks remains within an existing network. With the current setup of my environment, I do not want to add overhead of maintaining extra network and security to watch over, and so AI Notebooks is the immediate solution. But I would also want to understand the benefits that Cloud Datalab provides.

• 在AI Notebook和Cloud Datalab之间，应使用哪个版本，
  场景?

• Between AI Notebook and Cloud Datalab, which should be used and in which
  scenario?

Cloud Datalab是否还提供了预安装的Python软件包，Tensorflow还是AI笔记本电脑这样的R环境?

Is Cloud Datalab also providing pre-installed packages of Python,Tensorflow or R environment like AI Notebooks?

推荐答案

在任何情况下，都应在新项目上使用AI笔记本，因为Cloud Datalab的提早提倡.

You should use AI notebooks on new projects in any case since Cloud Datalab would be deprecated sooner than later.

是的.

这两种产品之间的差异概述.

1. DataLab

1. DataLab

• 与最新的JupyterLab扩展不兼容的自定义UI.
• 使用自DataLab发行以来的旧PyDatalab SDK，没有适用于许多GCP服务的官方SDK.
• 路线图没有重大变化.
• 需要具有端口映射的SSH才能使用

笔记本:

• 使用JupyterLab UI.
• 使用正式的SDK(例如BigQuery Python SDK)，因此可以实现更好的集成.
• 由于UI(JupyterLab)是社区驱动的，因此迅速发布了新更改.
• 访问UI很简单，不需要SSH，也不需要使用CLI.
• 笔记本API
• Terraform 支持
• 客户端库( Python ， Java ， NodeJS )来管理笔记本

• Using JupyterLab UI.
• Using official SDKs (like BigQuery Python SDK), therefore better integration.
• Since UI (JupyterLab) is community driven releasing new changes rapidly.
• Access to UI is simple, no SSH, no CLI usage is required.
• Notebooks API
• Terraform support
• Client libraries (Python, Java, NodeJS) to manage Notebooks

这篇关于GCP中的AI Notebook和Cloud Datalab有什么区别?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！