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https://issues.apache.org/jira/browse/BEAM-4391?focusedWorklogId=124751&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-124751
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ASF GitHub Bot logged work on BEAM-4391:
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Author: ASF GitHub Bot
Created on: 19/Jul/18 02:09
Start Date: 19/Jul/18 02:09
Worklog Time Spent: 10m
Work Description: aaltay commented on issue #5736: [BEAM-4391] Example of
distributed optimization
URL: https://github.com/apache/beam/pull/5736#issuecomment-406131616
Thank you @javdrher. This looks much better, I don't have any more file
level comments.
To answer your questions:
> Bringing the imports inline to the functions and mock them during testing?
Sounds like a good idea to me.
We recently added numpy to the setup.py (test section) for performance
tests. If you can mock only scipy related parts it should be enough.
> Regarding the fusion: ...
This explanation is grid, please add it to the example with a link to that
blog post. It would serve as a good learning opportunity.
> Were you suggesting I drop the PTransforms and put the construction of the
pipeline in one function? If so, I'll adjust the code.
I think the current organization looks clean. You can keep it as is.
I could not review the simulation parts of the code as I am not familiar
with this simulation. If you have a colleague who is familiar please ask them
to take a quick look at those parts.
Does it answer your questions? Do you have a reasonable path to make
progress?
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Issue Time Tracking
-------------------
Worklog Id: (was: 124751)
Time Spent: 2.5h (was: 2h 20m)
> Example of distributed optimization
> -----------------------------------
>
> Key: BEAM-4391
> URL: https://issues.apache.org/jira/browse/BEAM-4391
> Project: Beam
> Issue Type: New Feature
> Components: examples-python
> Reporter: Joachim van der Herten
> Assignee: Joachim van der Herten
> Priority: Minor
> Time Spent: 2.5h
> Remaining Estimate: 0h
>
> Currently, we are writing a blogpost on using the Beam Python SDK for solving
> distributed optimization tasks. It will include an example of a optimization
> problem with both discrete and continuous parameters, which is then solved
> using Apache Beam.
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