[Beowulf] Jupyter and EP HPC

Fred Youhanaie fly at anydata.co.uk
Fri Jul 27 14:16:17 PDT 2018


Jim

I'm not a jupyter user, yet, however, out of curiosity I just googled for what I think you're looking for. Is this any good?

https://ipyparallel.readthedocs.io/en/stable/

I have now bookmarked it for my own future use!

Cheers,
Fred

On 27/07/18 21:56, Lux, Jim (337K) wrote:
> 
> -----Original Message-----
> From: Beowulf [mailto:beowulf-bounces at beowulf.org] On Behalf Of Joe Landman
> Sent: Friday, July 27, 2018 11:54 AM
> To: beowulf at beowulf.org
> Subject: Re: [Beowulf] Jupyter and EP HPC
> 
> 
> 
> On 07/27/2018 02:47 PM, Lux, Jim (337K) wrote:
>>
>> I’ve just started using Jupyter to organize my Pythonic ramblings..
>>
>> What would be kind of cool is to have a high level way to do some
>> embarrassingly parallel python stuff, and I’m sure it’s been done, but
>> my google skills appear to be lacking (for all I know there’s someone
>> at JPL who is doing this, among the 6000 people doing stuff here).
>>
>> What I’m thinking is this:
>>
>> I have a high level python script that iterates through a set of data
>> values for some model parameter, and farms out running the model to
>> nodes on a cluster, but then gathers the results back.
>>
>> So, I’d have N copies of the python model script on the nodes.
>>
>> Almost like a pythonic version of pdsh.
>>
>> Yeah, I’m sure I could use lots of subprocess() and execute() stuff
>> (heck, I could shell pdsh), but like with all things python, someone
>> has probably already done it before and has all the nice hooks into
>> the Ipython kernel.
>>
> 
> I didn't do this with ipython or python ... but this was effectively the way I parallelized NCBI BLAST in 1998-1999 or so.  Wrote a perl script to parse args, construct jobs, move data, submit/manage jobs, recover results, reassemble output.  SGI turned that into a product.
> 
> 
> -- yes.. but I was hoping someone had done that for Jupyter..
> 
>>>> for parametervalue in parametervaluelist:
> ....          result = simulation(parametervalue)
>                 Results.append(result)
> 
> 
> 
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