You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Rithwik Ediga Lakhamsani (Jira)" <ji...@apache.org> on 2023/01/17 21:45:00 UTC
[jira] [Updated] (SPARK-41776) Implement support for PyTorch Lightning
[ https://issues.apache.org/jira/browse/SPARK-41776?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Rithwik Ediga Lakhamsani updated SPARK-41776:
---------------------------------------------
Description:
This requires us to just call train() on each spark task separately without much preprocessing or postprocessing because PyTorch Lightning handles that by itself.
Update: This was resolved by using `torch.distributed.run`
was:This requires us to just call train() on each spark task separately without much preprocessing or postprocessing because PyTorch Lightning handles that by itself.
> Implement support for PyTorch Lightning
> ---------------------------------------
>
> Key: SPARK-41776
> URL: https://issues.apache.org/jira/browse/SPARK-41776
> Project: Spark
> Issue Type: Sub-task
> Components: ML, PySpark
> Affects Versions: 3.4.0
> Reporter: Rithwik Ediga Lakhamsani
> Priority: Major
>
> This requires us to just call train() on each spark task separately without much preprocessing or postprocessing because PyTorch Lightning handles that by itself.
>
> Update: This was resolved by using `torch.distributed.run`
--
This message was sent by Atlassian Jira
(v8.20.10#820010)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org