Jupyter Notebooks are popular a development and training environment which have become the de-facto integrated development environment (IDE) for data science and machine learning.
Jupyter Notebooks are wildly popular but it's worth noting there are some drawbacks compared to working in a traditional IDE:
- Versioning notebooks is challenging. The code itself lives in the Notebook, not a source code management (SCM) system like Git/GitHub. This means you don’t get the benefits of merging, branching, and diffing code.
- Distributed training is not possible without a custom setup.
- Live collaboration is non-existent. Jupyter is not designed to have multiple users work in the same Notebook or on the same code concurrently. Notebooks may be forked but there is no off-the-shelf way to merge forks down the road.
As a result, some view Jupyter Notebooks solely as a tool for prototyping, analysis, and exploration.
There are, however a few examples of notebooks used in large-scale production pipelines such as those at Netflix.
Notebooks are a core component of the Gradient platform. Gradient offers a one-click Jupyter Notebook environment that is fully compatible with any existing Notebook and runs on a wide range of instances without any infrastructure management.
There is a free GPU and CPU instance available for Jupyter Notebooks which makes them very popular in the research community. Learn more here.
Notebooks can easily be shared publicly to collaborate on ML projects like GitHub repositories. The ML Showcase is a curated list of Jupyter Notebook-based projects that can be easily forked and edited.
Gradient supports both Jupyter Lab (the newest version) and Jupyter Notebooks (the older version).