[jira] [Resolved] (SPARK-41775) Implement training functions as input

["Hyukjin Kwon (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-41775?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-41775.
----------------------------------
    Fix Version/s: 3.4.0
       Resolution: Fixed

Issue resolved by pull request 39369
[https://github.com/apache/spark/pull/39369]

> Implement training functions as input
> -------------------------------------
>
>                 Key: SPARK-41775
>                 URL: https://issues.apache.org/jira/browse/SPARK-41775
>             Project: Spark
>          Issue Type: Sub-task
>          Components: ML, PySpark
>    Affects Versions: 3.4.0
>            Reporter: Rithwik Ediga Lakhamsani
>            Assignee: Rithwik Ediga Lakhamsani
>            Priority: Major
>             Fix For: 3.4.0
>
>
> Sidenote: make formatting updates described in https://github.com/apache/spark/pull/39188
>  
> Currently, `Distributor().run(...)` takes only files as input. Now we will add in additional functionality to take in functions as well. This will require us to go through the following process on each task in the executor nodes:
> 1. take the input function and args and pickle them
> 2. Create a temp train.py file that looks like
> {code:java}
> import cloudpickle
> import os
> if _name_ == "_main_":
>     train, args = cloudpickle.load(f"{tempdir}/train_input.pkl")
>     output = train(*args)
>     if output and os.environ.get("RANK", "") == "0": # this is for partitionId == 0
>         cloudpickle.dump(f"{tempdir}/train_output.pkl") {code}
> 3. Run that train.py file with `torchrun`
> 4. Check if `train_output.pkl` has been created on process on partitionId == 0, if it has, then deserialize it and return that output through `.collect()`



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