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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Work logged] (BEAM-4833) Add support for users specifying a requirements.txt for their Python portable container
Date Mon, 06 Aug 2018 03:25:00 GMT

     [ https://issues.apache.org/jira/browse/BEAM-4833?focusedWorklogId=131301&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-131301
]

ASF GitHub Bot logged work on BEAM-4833:
----------------------------------------

                Author: ASF GitHub Bot
            Created on: 06/Aug/18 03:24
            Start Date: 06/Aug/18 03:24
    Worklog Time Spent: 10m 
      Work Description: holdenk commented on a change in pull request #6005: [BEAM-4833] Add
support for user req.txt for portable python
URL: https://github.com/apache/beam/pull/6005#discussion_r207766704
 
 

 ##########
 File path: sdks/python/container/Dockerfile
 ##########
 @@ -79,14 +80,18 @@ RUN \
     pip install "tensorflow == 1.4.0" && \
     pip install "protorpc == 0.11.1" && \
     pip install "python-gflags == 3.0.6" && \
-    # Remove pip cache.
 
 Review comment:
   This is addressed I believe, it's just moved to the end of the same RUN cmd

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Issue Time Tracking
-------------------

    Worklog Id:     (was: 131301)
    Time Spent: 2h 10m  (was: 2h)

> 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: 2h 10m
>  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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