创建基于云的数据科学环境以进行更快的分析 (Creating a cloud-based data science environment for faster analysis)

There are times when working on data science problems with your local machine just doesn’t cut it anymore. Maybe your computer is old, and can’t work with larger datasets. Or maybe you want to be able to access your work from anywhere, and collaborate with others. Or maybe you have an analysis that will take a long time to run, and you don’t want to tie up your own computer. In these cases, it is useful to run Jupyter on a server, so you can access it through a browser.

有时，使用本地计算机解决数据科学问题时，根本无法解决。 也许您的计算机是旧的，并且无法使用较大的数据集。 或者，也许您希望能够从任何地方访问您的工作并与他人合作。 或者，也许您的分析需要很长时间才能运行，并且您不想捆绑自己的计算机。 在这些情况下，在服务器上运行Jupyter很有用，因此您可以通过浏览器访问它。

We can do this easily by using . See on how to setup a data science environment using Docker for background. This post builds on that one, and sets up Docker and Jupyter on a server.

我们可以使用轻松地做到这一点。 请参阅 ，了解如何使用Docker作为背景来设置数据科学环境。 这篇文章以此为基础，并在服务器上设置了Docker和Jupyter。

云托管 (Cloud hosting)

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