[
https://issues.apache.org/jira/browse/IGNITE-10133?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Alexey Zinoviev updated IGNITE-10133:
-------------------------------------
Flags: Important
Ignite Flags: Docs Required,Release Notes Required
Release Note: Switch to per-node TensorFlow worker strategy
> ML: Switch to per-node TensorFlow worker strategy
> -------------------------------------------------
>
> Key: IGNITE-10133
> URL: https://issues.apache.org/jira/browse/IGNITE-10133
> Project: Ignite
> Issue Type: Improvement
> Components: ml
> Affects Versions: 2.8
> Reporter: Anton Dmitriev
> Assignee: Anton Dmitriev
> Priority: Major
> Fix For: 2.8
>
>
> Currently we start TensorFlow worker process per every cache partition. In
> case node is equipped by GPU and TensorFlow uses this GPU it acquires all GPU
> memory. If two worker processes try to acquire all GPU memory they will fail.
> To eliminate this problem and allow users utilizing GPU during the training
> we need to switch to per-node strategy. It means we need to start one
> TensorFlow worker process per node, not per partition.
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
