Hi, I am using pio V0.12.0 (Hbase 1.2.6, Elasticsearch 5.2.1, Spark 2.6.1).
I am using template
>
https://github.com/EmergentOrder/template-scala-probabilistic-classifier-batch-lbfgs
I spawned two servers each having configuration(244 GB RAM, 16 Cores). On 1
server I uploaded 1 million events wit
Howdy Donald,
Indeed this PR trades batch prediction scalability (via Spark) for
compatibility (with all models). I'm not convinced this is good trade-off.
I also had a variation of the PR working that simply reuses the model's
SparkContext for queries, if it hasn't already been stopped. That's w
Thank you
_
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I’ve seen this happen on my dev machine (a mac laptop) when I use
`pio-start-all` which I never use in production.
My test for everything running includes
pio status: this only tests the metadata service and the main store
(Elasticsearch and HBase in my case) It by no means examines all service
Hey Noelia,
What event storage backend are you using? Is the backend storage stuck?
This could happen very often with a local, single-node HBase installation.
Regards,
Donald
On Mon, Nov 13, 2017 at 7:51 AM, Noelia Osés Fernández
wrote:
> I forgot to mention that *pio status* reports my system
Hi Rasna,
Sorry for the late reply. The event server models log data as a series of
immutable events and thus do not support updates (except deletes) of entity
IDs. The event server itself does not maintain any cache between its REST
API to the backend storage (HBase, etc). It is simply an adapter
Hi Sachin,
1. I would highly encourage you to adopt the template, and upgrade and
maintain it to track future PIO releases if that's something you like to
do. Otherwise, you may want to consider following http://predictionio.
apache.org/templates/classification/quickstart/ and see if your use case
Hi Mars,
Thanks for the PR! I am still reviewing the code change, but at the high
level it will take away the ability to run "batchpredict" remotely on a
Spark cluster + HDFS/S3 setup, and requires extra steps of downloading
input and uploading output files for such setup. It will unlikely scale t
No PtP non-zero elements have LLR calculated. The highest scores in the row are
kept, or ones above some threshold hte resst are removeda as “noise". These are
put into the Elasticsearch model without scores.
Elasticsearch compares the similarity of the user history to each item in the
model t
Pat,
If I understood your explanation correctly, you say that some elements of
PtP are removed by the LLR (set to zero, to be precise). But the elements
that survive are calculated by matrix multiplication. The final PtP is put
into EleasticSearc and when we query for user recommendations ES uses
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