If you are interested in a generic solution, hibernating kernels could be
proposed in a Jupyter Enhancement Proposal[1]. However, supporting this
across operating systems may be a challenge (CryoPID or BLCR could work on
Linux, not sure about feasibility on Windows) and would require significant
investment and resources to maintain.

For now, you could either:
- use a kernel-specific solution such as dill's dump_session and
load_session for Python kernels (as discussed on SO [2]), or
- host your jupyter-server (or kernel) on a remote machine which would stay
on even when you turn your client/workstation off (of note JupyterLab
Desktop [3] exposes a friendly GUI for connecting to remote jupyter servers)

[1] https://jupyter.org/enhancement-proposals/README.html
[2]
https://stackoverflow.com/questions/34342155/how-to-pickle-or-store-jupyter-ipython-notebook-session-for-later
[3] https://github.com/jupyterlab/jupyterlab-desktop


Best wishes,
Michał Krassowski
https://github.com/krassowski

On Thu, 18 May 2023 at 09:49, Jason Moore <[email protected]> wrote:

> The basic idea is to save the results of a long computation to a file and
> then skip the computation if that file exists. You can then delete the file
> if you want the computation to run. Many people use the pickle module to
> save Python objects to disk but there are thousands of other ways to manage
> caching information to disk.
>
> On Thu, May 18, 2023 at 10:44 AM Luca Marconi <[email protected]>
> wrote:
>
>> Hi Jason,
>> thank you for your quick answer.
>>
>> Are there any simple ways to do what you suggest?
>>
>> Otherwise, could I open the Jupyter Notebook on Google cloud to prevent
>> the re-run everytime?
>>
>> Or, are there any other possibilities?
>>
>> Unfortunately, I have been working with Jupyter from few weeks, so I am
>> not expert in how to optimize this issue.
>>
>> Thanks for any help.
>>
>> Best regards,
>>
>> Luca
>>
>> Il giorno gio 18 mag 2023 alle ore 10:40 Jason Moore <
>> [email protected]> ha scritto:
>>
>>> I don't think this is something that jupyter can solve. You should
>>> likely investigate how to cache intermediate results to disk in your
>>> analysis pipelines if you want to skip repeating some computations.
>>>
>>> Jason
>>>
>>>
>>> On Thu, May 18, 2023 at 10:38 AM Luca Marconi <
>>> [email protected]> wrote:
>>>
>>>> Hi everybody,
>>>> I am working with Jupyter Notebook for big data analysis and machine
>>>> learning.
>>>>
>>>> I always do "save and checkpoint" before closing the file.
>>>>
>>>> Unfortunately, I always have to re-run all the cells (all the realized
>>>> code) when I re-open my file.
>>>>
>>>> Is there a quick way to avoid this necessity?
>>>>
>>>> I would need to open the notebook and start working from the last cell
>>>> onwards, since the time needed to re-run everything is a lot (I have to
>>>> work with very large datasets).
>>>>
>>>> Thanks for you support.
>>>>
>>>> Best regards,
>>>> Luca Marconi
>>>>
>>>> --
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>>>> .
>>>>
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>>> .
>>>
>>
>>
>> --
>> Cordiali saluti,
>>
>> Luca Marconi
>>
>> --
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-- 
Best regards,
Michał Krassowski

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