Thanks Takowl,
Systemdspawner really seems a good tool, however, we just using
Jupyter notebook, that is what we integrated into our product, we can not
change to JupyterHub.
Still appreciate your help, and maybe I can reference its
implementation to reach my goal.
在 2017年2月7日星期二 UTC+8下午8:11:31，takowl写道：
>
> If you're using Jupyterhub, however, then there are spawners which have
> some ways to control the resources available to each user. For example,
> Systemdspawner:
>
> https://github.com/jupyterhub/systemdspawner
>
> On 7 February 2017 at 11:02, MinRK <[email protected] <javascript:>>
> wrote:
>
>>
>>
>> On Mon, Feb 6, 2017 at 3:54 PM, <[email protected] <javascript:>>
>> wrote:
>>
>>> Hi,
>>> We know user can create as many notebook instances as they can
>>> in one jupyter notebook server client, and when we want to integrate
>>> spark's pyspark into jupyter notebook, which means using the ipykernel,
>>> when every each notebook instance is created, then a pyspark shell(or to
>>> say a driver) is initialized, since we run that in spark client mode, so
>>> all the started drivers would run on same host. And when some user in crazy
>>> mode, like creating many many notebook instances, then many drivers would
>>> all start in one host, which will lead to being lack of available resource
>>> easily.
>>> So I am wondering if Jupyter notebook has some mechanism to deal
>>> with or avoid such kind of that issue?
>>>
>>
>> Jupyter itself has no mechanism to deal with this. It would have to be
>> done at the library level - i.e. check for other instances and do one of:
>>
>> - connect to running instances if possible
>> - refuse to start if too many are running
>> - etc.
>>
>> -Min
>>
>>
>>>
>>> Many thanks in advance!
>>>
>>> Best Regards
>>> Sherry
>>>
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