These platforms have certain challenges (notebook diffs, commenting) when it comes to version control for notebooks. They provide tooling to compare versions of your notebooks, return to older versions, and collaboratively review notebooks changes. These platforms host your notebooks and store the changes you make to your notebooks over time. Some widely used online version control platforms are GitHub, Gitlab, and Bitbucket. Jupyter notebooks can be version controlled using the same version-control systems used for software development. How to version control Jupyter notebooks? JupyterHub is an open-source multiuser platform for Jupyter notebooks. Web-based and desktop versions of JupyterLab are available. JupyterLab is an IDE that supports Jupyter notebooks, code, data, and images. Jupyter is the overarching project whereas JupyterLab and JupyterHub are both Jupyter subprojects. Jupyter is a non-profit, open-source project that builds interactive tooling for science and analytics. What is the difference between Jupyter, JupyterLab, and JupyterHub? In this post, we’ll take a look at some tips that can help you become more efficient and productive when using Jupyter Notebook whether you’re a notebook newb or an experienced user. While it’s a user-friendly platform, there are plenty of tips and tricks that can help you make the most out of your Jupyter Notebook experience. Jupyter Notebook is a powerful tool used by data scientists, researchers, and analysts to write, visualize, and share their code and findings. How to share Jupyter notebooks with others?.How do you split cells in Jupyter notebooks?.How do you import and use Plotly in Jupyter notebooks?.Why would I convert my notebooks to Python modules? And how?.What are some good online Jupyter Notebook platforms?.How do I use Docker with Jupyter notebooks?.How to version control Jupyter notebooks?.What is the difference between Jupyter, JupyterLab, and JupyterHub?.That is especially true if you want to go beyond watching your learning curve and want to see additional information like performance charts, or prediction visualizations after every epoch.Top 10 tips for working efficiently with Jupyter Notebook Monitoring ML experiments with dedicated tools gives you the comfort of knowing what is going on with your training runs. Especially if you don’t have access to the machine (computational cluster at University, VPN at work, Cloud server you’re using somewhere, or when you’re on a bus :)). Sometimes you can’t even access the model training environment.Īnd that’s where tools come in handy! You can use them to flexibly monitor your ML experiments and look at model training information whenever you need to. When you look at logs you don’t see the change over time immediately (think learning curve vs losses on epoch 10), You cannot look at your console logs all the time, Monitoring machine learning experiment runs is an important and healthy practice but it can be a challenge. There are a ton of JupyterLab extensions that you may want to use.Įxtension Manager (little puzzle icon in the command palette) lets you install and disable extensions directly from JupyterLab. If you would like to see how to create your own extension read this guide. Technically JupyterLab extension is a JavaScript package that can add all sorts of interactive features to the JupyterLab interface. JupyterLab extension is simply a plug-and-play add-on that makes more of the things you need possible. “JupyterLab is designed as an extensible environment”. In this article, we’ll talk about JupyterLab extensions that can make your machine learning workflows better. One of the great things about Jupyter ecosystem is that if there is something you are missing, there is either an open-source extension for that or you can create it yourself. JupyterLab, a flagship project from Jupyter, is one of the most popular and impactful open-source projects in Data Science.
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