![]() 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. 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. We also encourage you to join the Plotly Community Forum if you want help with anything related to plotly.JupyterLab, a flagship project from Jupyter, is one of the most popular and impactful open-source projects in Data Science. Once you've installed, you can use our documentation in three main ways: Note: This package is optional, and if it is not installed it is not possible for figures to be uploaded to the Chart Studio cloud service. Plotly may be installed using pip:$ pip install plotly=5.14.1 Make some changes to the figure, then use the file menu to save as otly. We also encourage you to join the Plotly Community Forum if you want help with anything related to plotly. plotly from the file menu and open with Plotly Editor.
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