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 >> >>>>> >> >>>> >> >>>> >> >> >>
