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https://issues.apache.org/jira/browse/SPARK-13587?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15647331#comment-15647331
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Prasanna Santhanam commented on SPARK-13587:
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Thanks for this JIRA and the work related to it - I have been testing this
patch a little with conda environments.
Previously, I have had reasonable success with zipping the contents of my conda
environment in the gateway/driver node and submitting the zip file as an
argument to {{--archives}} in the {{spark-submit}} command line. This approach
works perfectly because it uses the existing spark infrastructure to distribute
dependencies through to the workers. You actually don't even need anaconda
installed on the workers since the zip can package the entire python
installation within it. The downside of it being that conda zip files can bloat
up quickly in a production spark application.
[~zjffdu] In your approach I find that the driver program still executes on the
native python installation and only the workers run within conda (virtualenv)
environments. Would it not be possible to use the same conda environment
throughout? ie. setup once on gateway node and propagate over the distributed
cache as mentioned in a related PR comment.
I can always force the driver python to be conda using `PYSPARK_PYTHON` and
`PYSPARK_DRIVER_PYTHON` but that is not the same conda environment as the one
created by your PythonWorkerFactory. Or is it not your intention to make it
work this way?
> Support virtualenv in PySpark
> -----------------------------
>
> Key: SPARK-13587
> URL: https://issues.apache.org/jira/browse/SPARK-13587
> Project: Spark
> Issue Type: New Feature
> Components: PySpark
> Reporter: Jeff Zhang
>
> Currently, it's not easy for user to add third party python packages in
> pyspark.
> * One way is to using --py-files (suitable for simple dependency, but not
> suitable for complicated dependency, especially with transitive dependency)
> * Another way is install packages manually on each node (time wasting, and
> not easy to switch to different environment)
> Python has now 2 different virtualenv implementation. One is native
> virtualenv another is through conda. This jira is trying to migrate these 2
> tools to distributed environment
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