I found the cause for this ... When i run julia 0.3.2 or 0.5 as standalone (mix model) it uses all the available cores from my server, so it was fast.
If i call Julia from Python (Pyjulia), i see only one core is busy with python process (100% cpu) and all other cores are free. Can you help me how can i force Pyjulia/python to use available cores from my server? Regards, Harish On Sat, Nov 19, 2016 at 8:32 PM, Mauro <mauro...@runbox.com> wrote: > On Sat, 2016-11-19 at 20:48, Harish Kumar <harish.kuma...@gmail.com> > wrote: > > Thank you. I agree on python.. but my question was did they update the > > Pyjulia libraries for latest Julia version? . We tried with 0.4.3 which > > failed 6 months back. So we revered to 0.3.4. Or is this library remain > > same for all Julia versions? > > > > Any suggestion on this? > > They are testing against the latest release, i.e. 0.5: > https://github.com/JuliaPy/pyjulia/blob/master/.travis.yml > > You should try and file an issue if it doesn't work. 6 months are a > long time at the current julia development pace. > > > > > On Sat, Nov 19, 2016 at 7:38 PM, Mauro <mauro...@runbox.com> wrote: > > > >> On Sat, 2016-11-19 at 18:36, Harish Kumar <harish.kuma...@gmail.com> > >> wrote: > >> > Will it support Python 3.4 ? I am calling this from pyjulia interface > >> > >> https://github.com/JuliaPy/pyjulia says that it is tested against 3.5, > >> but it doesn't say that 3.4 is not supported. So you should try. > >> > >> > On Nov 19, 2016 4:58 PM, "Mauro" <mauro...@runbox.com> wrote: > >> > > >> >> Julia 0.3.12, that's a stone-age version of Julia. You should move > to > >> 0.5! > >> >> > >> >> On Sat, 2016-11-19 at 16:42, Harish Kumar <harish.kuma...@gmail.com> > >> >> wrote: > >> >> > I am using Version 0.3.12 calling from python (pyjulia). I do LME > fit > >> >> with > >> >> > 2.8 M rows and 60-70 Variables. It is taking 2 hours just to model > (+ > >> >> data > >> >> > transfer time). Any tips? > >> >> > using MixedModels > >> >> > modelREML = lmm({formula}, dataset) > >> >> > reml!(modelREML,true) > >> >> > lmeModel = fit(modelREML) > >> >> > fixedDF = DataFrame(fixedEffVar = coeftable(lmeModel).rownms, > >> >> estimate > >> >> > = coeftable(lmeModel).mat[:,1], > >> >> > stdError = coeftable(lmeModel).mat[:,2],zVal > = > >> >> > coeftable(lmeModel).mat[:,3]) > >> >> > > >> >> > On Tuesday, February 23, 2016 at 9:16:47 AM UTC-6, Stefan Karpinski > >> >> wrote: > >> >> >> > >> >> >> I'm glad that particular slow case got faster! If you want to > submit > >> >> some > >> >> >> reduced version of it as a performance test, we could still > include > >> it > >> >> in > >> >> >> our perf suite. And of course, if you find that anything else has > >> ever > >> >> >> slowed down, please don't hesitate to file an issue. > >> >> >> > >> >> >> On Tue, Feb 23, 2016 at 9:55 AM, Jonathan Goldfarb < > >> jgol...@gmail.com > >> >> >> <javascript:>> wrote: > >> >> >> > >> >> >>> Yes, understood about difficulty keeping track of regressions. I > was > >> >> >>> originally going to send a message relating up to 2x longer test > >> time > >> >> on > >> >> >>> the same code on Travis, but it appears as though something has > >> >> changed in > >> >> >>> the nightly build available to CI that now gives significantly > >> faster > >> >> >>> builds, even though the previous poor performance had been > >> >> dependable... > >> >> >>> Evidently that build is not as up-to-date as I thought. Our code > is > >> >> >>> currently not open source, but should be soon after which I can > >> share > >> >> an > >> >> >>> example. > >> >> >>> > >> >> >>> Thanks for your comments, and thanks again for your work on > Julia. > >> >> >>> > >> >> >>> -Max > >> >> >>> > >> >> >>> > >> >> >>> On Monday, February 22, 2016 at 11:12:58 AM UTC-5, Stefan > Karpinski > >> >> wrote: > >> >> >>>> > >> >> >>>> Yes, ideally code should not get slower with new releases – > >> >> >>>> unfortunately, keeping track of performance regressions can be a > >> bit > >> >> of a > >> >> >>>> game of whack-a-mole. Having examples of code whose speed has > >> >> regressed is > >> >> >>>> very helpful. Thanks to Jarrett Revels excellent work, we now > have > >> >> some > >> >> >>>> great performance regression tracking infrastructure, but of > >> course we > >> >> >>>> always need more things to test! > >> >> >>>> > >> >> >>>> On Mon, Feb 22, 2016 at 9:58 AM, Milan Bouchet-Valat < > >> nali...@club.fr > >> >> > > >> >> >>>> wrote: > >> >> >>>> > >> >> >>>>> Le lundi 22 février 2016 à 06:27 -0800, Jonathan Goldfarb a > écrit > >> : > >> >> >>>>> > I've really been enjoying writing Julia code as a user, and > >> >> following > >> >> >>>>> > the language as it develops, but I have noticed that over > time, > >> >> >>>>> > previously fast code sometimes gets slower, and > (impressively) > >> >> >>>>> > previously slow code will sometimes get faster, with updates > to > >> the > >> >> >>>>> > Julia codebase. > >> >> >>>>> Code is not supposed to get slower with newer releases. If this > >> >> >>>>> happens, please report the problem here or on GitHub (if > possible > >> >> with > >> >> >>>>> a reproducible example). This will be very helpful to help > >> avoiding > >> >> >>>>> regressions. > >> >> >>>>> > >> >> >>>>> > No complaint here in general; I really appreciate the work > all > >> of > >> >> the > >> >> >>>>> > Core and package developers do, and variations in > performance of > >> >> >>>>> > different codes it to be expected. > >> >> >>>>> > My question is this: has anyone in the Julia community > thought > >> >> about > >> >> >>>>> > updated performance tips for writing high performance code? > >> >> >>>>> > Obviously, using the profiler, along with many of the tips > >> >> >>>>> > at https://github.com/JuliaLang/julia/commits/master/doc/ > >> >> manual/perfo > >> >> >>>>> > rmance-tips.rst still apply, but I am wondering more about > >> >> >>>>> > general/structural ideas to keep in mind in Julia v0.4, as > well > >> as > >> >> >>>>> > guidance on how best to take advantage of recent changes on > >> >> master. I > >> >> >>>>> > know that document hasn't been stagnant in any sense, but > >> >> relatively > >> >> >>>>> > "big in any case, I'd be happy to help make some updates in a > >> PR if > >> >> >>>>> > there's anything we come up with. > >> >> >>>>> I've just skimmed through this page, and I don't think any of > the > >> >> >>>>> advice given there is outdated. What's new in master is that > >> >> anonymous > >> >> >>>>> functions (and therefore map) are now fast, but that wasn't > >> >> previously > >> >> >>>>> mentioned in the tips as a performance issue anyway. > >> >> >>>>> > >> >> >>>>> The only small sentence which should likely be removed is "for > >> >> example, > >> >> >>>>> currently it’s not possible to infer the return type of an > >> anonymous > >> >> >>>>> function". Type inference seems to work fine now on master with > >> >> >>>>> anonymous functions. I'll leave others confirm this. > >> >> >>>>> > >> >> >>>>> Anyway, do you have any specific points in mind? > >> >> >>>>> > >> >> >>>>> > >> >> >>>>> Regards > >> >> >>>>> > >> >> >>>> > >> >> >>>> > >> >> >> > >> >> > >> >
