Is there a possibility to open the same notebook multiple times but each time with another kernel?
I have a problem where I open and run a notebook and whilst I do some calculations another user opens the same notebook. Changes to variables I made will have an effect on that users output and vice versa.
Is there a way to stop this from happening (eg. can you configure a notebook so that it always opens a new kernel?)
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My desired outcome is to be able to modify an existing Jupyter notebook running in the browser, live, from another program.
The other program (say a local editor) that has access to the kernel/back end of notebook should be able to call an api to append a cell with specified contents, execute the cell, and show the output. This would allow me to implement "send to notebook" functionality in an editor.
The examples for programmatically doing seem to require running in the notebook itself. I would like to be able to just send code to be executed to the kernel, and have it output the result in the already running jupyter notebook. This should not require refreshing the page or anything like that, ideally.
As the title says, is there a way to run a Jupyter Notebook cell in the background (in Python)?
For example, is there a hypothetical magic command %%run_in_background that allows me to kick off a cell to execute in the background without blocking other cells from executing.
Technically you can’t do that, each kernel is like an instance of python, you can only run a script at a time.
You could run the cell, then refresh the browser and run the notebook fresh. The previous command will continue running but there is no way of accessing it in the notebook anymore. You won’t get the result you want, the output from the previous run won’t appear in this new instance.
Your best bet is to use 2 different notebooks, or 2 python scripts or use multiprocessing modules.
I'm using Jupyter notebook to write my code, but I'm facing a problem
that each time I open the notebook I find that all the cells are run.
This causes problems when I want to add some new cells in between.
So I am obliged to rerun the code from the beginning to get the right results.
Is there a way I can start from where I stopped running to save time? Especially since my code takes around 4 hours to run.
Don't shut down the computer that runs the notebook ex.: in windows
"Lock" option
Run the notebook in cloud (AWS, Azure, Google Cloud, Free option Google Colab runs for a while) where you don't need to shut
down the computer
Save down calculated results to files like.:
txt, csv
Save down models with pickle
It is also possible that you leave the computer stays on but the notebook gets disconnected from the environment in this case just pick your already running environment reconnect and it will have all your previous runtime results
I just started using Python 3 on Jupyter so I'm not really confortable with it. When I open a file with some commands, if I try to run it, the screen will give me back errors saying that the variables are not defined.
If I try to run directly filename.find("2019") it gives an error back. So when I open a file should, as first step, run all the cells?
Yes, generally speaking, when you open an existing notebook and want to add some code to it at the end, you should first run all the existing cells. You can do this from the menu: Cell -> Run All. Otherwise you would have no proper way of testing your additional code, since it may depend on changes to the namespace in the preceding code.
If the notebook wasn't active so far in that Jupyter session, there is no need to restart the kernel. Jupyter starts a separate kernel instance for every notebook you open.
So I found this great visualization of Newton's unconstrained optimization on a Jupyter Notebook within Louis Tiao's public account, and I want to run it on my laptop.
With other platforms, I'd be able to just copy and paste (including the annotations), and get it ready to "play". But with Notebook, I have to deal with multiple cells, and copy and paste each one separately, and in order.
Is there a more expeditious way of transferring the code?
A jupyter notebook is stored in a file with a .ipynb extension. The internals of the file a specially formatted text called json (or similar to it).
Right click the link and save as name.ipynb (it defaults to this in windows on chrome) and choose a location to save it. The best location is one where you have all your notebooks by default.
Then run jupyter and open the file.