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https://issues.apache.org/jira/browse/BEAM-4833?focusedWorklogId=125388&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-125388
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ASF GitHub Bot logged work on BEAM-4833:
----------------------------------------
Author: ASF GitHub Bot
Created on: 20/Jul/18 08:12
Start Date: 20/Jul/18 08:12
Worklog Time Spent: 10m
Work Description: herohde commented on a change in pull request #6005:
[BEAM-4833][WIP] Add support for user req.txt for portable python
URL: https://github.com/apache/beam/pull/6005#discussion_r203968007
##########
File path: sdks/python/container/build.gradle
##########
@@ -59,7 +59,15 @@ golang {
docker {
name containerImageName(name: "python")
- files "./build/"
+ files "./build"
+ // If the user has specified additional python requirements, copy them
otherwise make an empty file
+ if (project.hasProperty("python_requirements")) {
Review comment:
Suggestion: instead of making the current python Dockerfile more involved,
perhaps it would be preferable to add a 2nd Dockerfile and target FROM an
existing image. Then users who just want to pre-install various dependencies
can extend even released images. It would be faster and more robust than
rebuilding the whole thing.
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Issue Time Tracking
-------------------
Worklog Id: (was: 125388)
Time Spent: 0.5h (was: 20m)
> Add support for users specifying a requirements.txt for their Python portable
> container
> ---------------------------------------------------------------------------------------
>
> Key: BEAM-4833
> URL: https://issues.apache.org/jira/browse/BEAM-4833
> Project: Beam
> Issue Type: Improvement
> Components: sdk-py-core
> Reporter: holdenk
> Assignee: holdenk
> Priority: Minor
> Time Spent: 0.5h
> Remaining Estimate: 0h
>
> It's pretty common that Python scripts require extra dependencies, even the
> tensorflow model analysis TFMA example requires a different version of TF
> than the one we install by default. While users can roll their own container
> or edit the Dockerfile, it would probably be useful to provide an easier path
> to integrating their dependencies.
> While we support automatically installing the dependencies at runtime on the
> workers, this can be very slow, especially for things like tensorflow, arrow,
> or other numeric heavy code.
> Another alternative could be a simple script to augment the existing base
> image.
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