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https://issues.apache.org/jira/browse/SAMOA-49?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15338044#comment-15338044
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ASF GitHub Bot commented on SAMOA-49:
-------------------------------------
Github user nicolas-kourtellis commented on the issue:
https://github.com/apache/incubator-samoa/pull/55
Hi bhupeshchawda,
I was checking the adapter on apex local mode and I had some initial
comments:
1) The execution of the classification on both a fixed arff file
(covTypeNorm) and the random tree generator are much slower (even 5-10 times
slower) in comparison to the samoa-local (the basic local mode of Samoa for
testing purposes). Can you check if this is a matter of the necessary overhead
for the distributed execution or something else is causing the delay? I tested
with various numbers for parallelism hint, but the observation holds.
2) The results on covtypenorm should have been deterministic. That is,
given that the file is the same used in repetitive runs, the results should be
identical across runs (for sure given that parallelism is 1). However, when I
run the VHT repetitively on the same arff file, the results are not the same
across runs. Is there any fundamental reason why this would be happening?
(Again, you can check for this using the local mode for testing).
3) The results with the random tree generator show a good accuracy and
similar to the one received with basic local mode of Samoa for testing. But the
results on the arff file degrade in performance, lower than the basic local
mode of Samoa. Any reason why this is happening? Maybe it has to do with the
problem in comment 2?
4) I noticed there are some changes in your PR regarding the samoa-api in
about 10 files. These changes seem to be mostly related to Kryo serialization.
Can you explain why you needed these done now? Also, do they affect the
execution with the other adaptors and DSPEs? Also, maybe it would have made
sense to push them in a different PR? Or do they need to be there for Apex to
play along in the first place?
Thanks!
> Add an Adapter for Apache Apex
> ------------------------------
>
> Key: SAMOA-49
> URL: https://issues.apache.org/jira/browse/SAMOA-49
> Project: SAMOA
> Issue Type: New Feature
> Reporter: Bhupesh Chawda
>
> Apache Apex is a new data-in-motion platform that unifies stream processing
> as well as batch processing. An Apache Apex adapter for Samoa would allow
> users to run streaming machine learning algorithms built on Apache Samoa, on
> Apache Apex platform. This adapter should be able to translate the Apache
> Samoa topologies into Apache Apex DAGs in order to run them on the Apex
> platform.
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