C-6, Harish Kumar 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.
>
> Fitting a linear mixed effects model only uses multiple threads for the
> BLAS (Basic Linear A
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 wrote:
> On Sat, 2016-11-19 at 20:48, Harish Kumar
> wrote:
> > Thank you. I agree on python.. but my question was did they update the
> > Pyjulia li
PM, Mauro wrote:
> On Sat, 2016-11-19 at 18:36, Harish Kumar
> 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. S
Will it support Python 3.4 ? I am calling this from pyjulia interface
On Nov 19, 2016 4:58 PM, "Mauro" 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
> wrote:
> > I am using V
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(modelR
Use pyjulia package. Convert your data set to list of list and pass to Julia
On Wednesday, November 2, 2016 at 1:43:27 PM UTC-5, Alexei Serdiuk wrote:
>
> Hi,
>
> I'm new to Julia and, unfortunately, I'm almost zero to Python.
> I need to call Julia code from Python. This code must do some operat
I have a RDD with 10K columns and 70 million rows, 70 MM rows will be
grouped into 2000-3000 groups based on a key attribute. I followed below
steps
1. Julia and Pyspark linked using pyjulia package
2. 70 MM rd is groupByKey
def juliaCall(x):
<>
j = julia.Julia()
jcode
