Hi, Min
      Thanks for the proposed possible solution. And you mean it may can be 
implemented at the library level, could you help give more details about 
the library level that I can dig into.
        I am wondering if I can implement a plugin based on things like 
KernelManager to achieve this, like opening another configurable parameter 
in jupyter_notebook_config.py to help me mange the maximum kernel instances 
number. Any advice about this is appreciated. 
   Thanks again.

在 2017年2月7日星期二 UTC+8下午7:03:08,Min RK写道:
>
>
>
> 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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