# Jupyter Notebooks ![](https://2327526407-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LvBP1svpACTB1R1x_U4%2F-LvHJ32JL_4hyT9PVdc0%2F-LvHdWVlYJo3mLe1f-8y%2Fimage.png?alt=media\&token=2ff640d2-4f5a-42cc-ab02-a8928c78e15e) 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](https://medium.com/netflix-techblog/notebook-innovation-591ee3221233). ## Jupyter Notebooks + Gradient 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](https://gradient.paperspace.com/free-gpu) which makes them very popular in the research community. Learn more [here](https://gradient.paperspace.com/free-gpu). Notebooks can easily be shared publicly to collaborate on ML projects like GitHub repositories. The [ML Showcase](https://ml-showcase.paperspace.com/) 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). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://machine-learning.paperspace.com/wiki/jupyter-notebooks.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.