Console would not automatically run code typed in notebook/kernel

From the console function instruction for Jupyter Lab, it looks like that when you start a new console for a notebook/kernal file, and when you select and run the code contained in the notebook/kernal, you can automatically get the computation result in the console. However, for me, when I ran my code in the file, I didn’t get any result back in the console. I have to hand select and click the button from run section that says “run selected text or current line in console”, which slows down the computation speed if I want to see running result of codes immediately from the notebook in the console. Could someone please help? Thanks a lot

It seems you are right. This video is somewhat outdated.
To get the effect you see in the video, you need to first right-click on console and toggle on ‘Show All Kernel Activity’. Then as you run the code in the notebook, it will show up in the console.
This is the text accompanying the video referenced in the documentation and it neglects pointing this out:

" You can connect a code console to a notebook kernel to have a log of computations done in the kernel, in the order in which they were done. The attached code console also provides a place to interactively inspect kernel state without changing the notebook. Right-click on a notebook and select “New Console for Notebook”"

Good news though @Heibear, you can fork the repo for the documentation and edit the text to better reflect the current needs and submit a pull request. That way you can help others not miss that toggle option.
And maybe in your pull request you can suggest the video be updated. A lot has changed in the UI as well since 2018 when that video was made, and so I bet a lot of the documentation videos need updating to better reflect the current state.


Thank you very much for your detailed and informative explanation :+1:. It worked out well after I followed your solution. You also gave me a way to report the issue so that this kind of negelction could be avoided in the future. Thank you again. Now I also know the background context of the posted videos of jupyter lab from your expeienced usage of this coding environment, so I would be prepared to bump into similar problems like this. Really helpful and I’m very thankful for your keen explanation. Thank you.

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