Jupyter notebooks6/12/2023 Just as an Android Developer would need to follow the above best practices to build a scalable and successful mobile app, a Jupyter Notebook focused on sustainable data science should follow them, too. unitests, doctests) for your Jupyter Notebooks.Ĭontinuous integration (CI) for faster development: automate the compilation and testing of Jupyter notebooks every time a team member commits changes to version control. Separate environments: split production and development artifacts.Ī comprehensive test suite (e.g. Version control and code review systems (e.g. All the best practices of software development should still apply: Jupyter Notebook is just a new development environment for writing code. AI Platform Notebooks is a managed service that offers an integrated JupyterLab environment, in which you can create instances running JupyterLab that come pre-installed with the latest data science and machine learning frameworks in a single click.įollow established software development best practices On Google Cloud Platform (GCP), we just launched a new service called AI Platform Notebooks. PrinciplesĬreating and using a new Jupyter Notebook instance should be very easy. In a future blog we will cover best practices for secure software development with Jupyter Notebooks, currently this topic is not covered in this blog post, but is something critical you must consider. Note: Security is a critical part of Software Development practices. You should institute version control for your Notebooksīy following the above guidelines in this manifesto, we want to help you to achieve this outcome: Follow established software development best practices: OOP, style guides, documentationĢ. There should be an easy way to use Jupyter Notebooks in your organization, where you can “just write code” within seconds.ġ. Our Jupyter Notebooks development manifestoĠ. Here is a list of best practices adopted by those communities, with a focus on those that still apply today: Logically, data scientists, ML researchers, and developers using Jupyter Notebooks should carry over the best practices already established by the older fields of computer science and scientific research. It’s often possible to share best practices across multiple industries, since the fundamentals remain the same. To set up a more robust environment, we established a manifesto that incorporates best practices that can help simplify and improve the life of any developer who uses Jupyter tools. However, if you’re using Jupyter Notebooks in a larger project that involves many engineers, you may soon find it challenging to scale your environment, or deploy to production. ![]() ![]() For example, your team may have the odd Python or Shell script that has neither test coverage nor any CI/CD integration. Because Jupyter Notebooks are a relatively recently-developed tool, they don’t (yet) follow or encourage consensus-based software development best practices.ĭata scientists, typically collaborating on a small project that involves experimentation, often feel they don’t need to adhere to any engineering best practices. ![]() And notebooks are quickly becoming the common platform for the data science community, whether in the form of AI Platform Notebooks, Kaggle Kernels, Colab, or the notebook that started it all, Jupyter.Ī Jupyter Notebook is an open-source web application that helps you create and share documents that contain live code, equations, visualizations, and narrative text. Many data science teams, both inside and outside of Google, find that it’s easiest to build accurate models when teammates can collaborate and suggest new hyperparameters, layers, and other optimizations.
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