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https://issues.apache.org/jira/browse/SPARK-24374?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16728020#comment-16728020
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Debasish Das commented on SPARK-24374:
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Hi [~mengxr] with barrier mode available is it not possible to use native TF
parameter server in place of using MPI ? Although we are offloading compute
from spark to tf workers/ps, still if there is an exception that comes out,
tracking it with native TF API might be easier than MPI exception...great work
by the way...I was looking for a cloud-ml alternative using spark over
aws/azure/gcp and looks like barrier should help a lot although I am still not
clear on the limitations of TensorflowOnSpark project from Yahoo
[https://github.com/yahoo/TensorFlowOnSpark] which tried to put barrier like
syntax but not sure if few partitions fails on some tfrecord read /
communication exceptions whether it can re-run full job or it will only re-run
the failed partition...I guess the exception from few partitions can be thrown
back to spark driver and driver can take the action for re-run..when multiple
tf training jobs get scheduled on the same spark cluster I suspect TFoS might
have issues as well...
> SPIP: Support Barrier Execution Mode in Apache Spark
> ----------------------------------------------------
>
> Key: SPARK-24374
> URL: https://issues.apache.org/jira/browse/SPARK-24374
> Project: Spark
> Issue Type: Epic
> Components: ML, Spark Core
> Affects Versions: 2.4.0
> Reporter: Xiangrui Meng
> Assignee: Xiangrui Meng
> Priority: Major
> Labels: Hydrogen, SPIP
> Attachments: SPIP_ Support Barrier Scheduling in Apache Spark.pdf
>
>
> (See details in the linked/attached SPIP doc.)
> {quote}
> The proposal here is to add a new scheduling model to Apache Spark so users
> can properly embed distributed DL training as a Spark stage to simplify the
> distributed training workflow. For example, Horovod uses MPI to implement
> all-reduce to accelerate distributed TensorFlow training. The computation
> model is different from MapReduce used by Spark. In Spark, a task in a stage
> doesn’t depend on any other tasks in the same stage, and hence it can be
> scheduled independently. In MPI, all workers start at the same time and pass
> messages around. To embed this workload in Spark, we need to introduce a new
> scheduling model, tentatively named “barrier scheduling”, which launches
> tasks at the same time and provides users enough information and tooling to
> embed distributed DL training. Spark can also provide an extra layer of fault
> tolerance in case some tasks failed in the middle, where Spark would abort
> all tasks and restart the stage.
> {quote}
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