Hi,
the iteration looks like:
DataSet gmms = getInitialGMMDataSet(env);
IterativeDataSet loop = gmms.iterate(50);
DataSet newGMMs = features.map(new
Estep_ExpectationMaximisation()).withBroadcastSet(loop, "gmms")
.reduceGroup(new
Mstep_ExpectationMaximisation()).withBr
Yes, env.setParallelism(1) fixes the parallelism of all operators to 1
(unless an operator overrides this setting).
Can you identify at which position in the data flow the results start to
diverge?
Best, Fabian
2016-02-29 17:57 GMT+01:00 Marcela Charfuelan
:
> Thanks Fabian,
> I am using in bot
Thanks Fabian,
I am using in both default options, since I am not testing in a cluster
yet, just local in ubuntu, I am not specifying any parallelism.
just to test I set in the program env.setParallelism(1) and running with
-p 1 (which I guess I would not need) but I am still getting the same is
Hi Marcela,
do you run the algorithm in both setups with the same parallelism?
Best, Fabian
2016-02-26 16:52 GMT+01:00 Marcela Charfuelan
:
> Hello,
>
> I implemented an algorithm that includes iterations (EM algorithm) and I
> am getting different results when running in eclipse (Luna Release
