On Tue, 2016-11-22 at 14:12, Harish Kumar <harish.kuma...@gmail.com> wrote: > 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?
I can't help you there. If no-one answers here try posting a new question on discourse.julialang.org > 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 >> >> >> >>>>> >> >> >> >>>> >> >> >> >>>> >> >> >> >> >> >> >> >> >> >>
