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added --autoreload flag to NotebookApp #4795

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merged 2 commits into from
May 23, 2020

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telamonian
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@telamonian telamonian commented Jul 31, 2019

When the --autoreload flag is passed, the webapp will watch for any changes to its Python source. On change, all changed packages will be reimported and the webapp will restart. Also works on Python source files in Jupyter server extensions. Implemented using the built in autoreload parameter in the constructor of tornado.web.Application, which NotebookApp.web_app is a subclass of.

While I was working on a server extension for Jupyterlab, I got pretty sick of having to kill and restart Jupyterlab every time I made changes to the Python source. This PR is an attempt to enable hot reloading for the sources of notebook and any extensions.

Though the autoreload behavior is currently a bit cumbersome (the Notebook server will reload completely from scratch, including (on Jupyterlab, at least) launching a new browser window with a new security token), I believe that it is useful as is. I also think that in the future it can definitely be improved, most obviously by recycling certain resources on reload, such as browser windows and security tokens.

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Nice, thanks!

@meeseeksmachine
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This pull request has been mentioned on Jupyter Community Forum. There might be relevant details there:

https://discourse.jupyter.org/t/reload-jupyter-serverextension/2200/3

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@telamonian, do you mind rebasing this one?

@blink1073 blink1073 added this to the 6.1 milestone May 22, 2020
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Can do

telamonian and others added 2 commits May 22, 2020 19:12
When passed, the webapp will watch for any changes to its Python source. On
change, all changed packages will be reimported and the webapp will restart.
Also works on Python source files in Jupyter server extensions. Implemented
using the built in `autoreload` parameter in the constructor of
`tornado.web.Application`.
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telamonian commented May 22, 2020

@blink1073 done!

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Thanks!

@blink1073 blink1073 merged commit 01e298f into jupyter:master May 23, 2020
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3 participants