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Posted to yarn-issues@hadoop.apache.org by "Antal Bálint Steinbach (JIRA)" <ji...@apache.org> on 2018/11/14 14:38:02 UTC
[jira] [Assigned] (YARN-6223) [Umbrella] Natively support GPU
configuration/discovery/scheduling/isolation on YARN
[ https://issues.apache.org/jira/browse/YARN-6223?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Antal Bálint Steinbach reassigned YARN-6223:
--------------------------------------------
Assignee: Antal Bálint Steinbach (was: Wangda Tan)
> [Umbrella] Natively support GPU configuration/discovery/scheduling/isolation on YARN
> ------------------------------------------------------------------------------------
>
> Key: YARN-6223
> URL: https://issues.apache.org/jira/browse/YARN-6223
> Project: Hadoop YARN
> Issue Type: New Feature
> Reporter: Wangda Tan
> Assignee: Antal Bálint Steinbach
> Priority: Major
> Fix For: 3.1.0
>
> Attachments: YARN-6223.Natively-support-GPU-on-YARN-v1.pdf, YARN-6223.wip.1.patch, YARN-6223.wip.2.patch, YARN-6223.wip.3.patch
>
>
> As varieties of workloads are moving to YARN, including machine learning / deep learning which can speed up by leveraging GPU computation power. Workloads should be able to request GPU from YARN as simple as CPU and memory.
> *To make a complete GPU story, we should support following pieces:*
> 1) GPU discovery/configuration: Admin can either config GPU resources and architectures on each node, or more advanced, NodeManager can automatically discover GPU resources and architectures and report to ResourceManager
> 2) GPU scheduling: YARN scheduler should account GPU as a resource type just like CPU and memory.
> 3) GPU isolation/monitoring: once launch a task with GPU resources, NodeManager should properly isolate and monitor task's resource usage.
> For #2, YARN-3926 can support it natively. For #3, YARN-3611 has introduced an extensible framework to support isolation for different resource types and different runtimes.
> *Related JIRAs:*
> There're a couple of JIRAs (YARN-4122/YARN-5517) filed with similar goals but different solutions:
> For scheduling:
> - YARN-4122/YARN-5517 are all adding a new GPU resource type to Resource protocol instead of leveraging YARN-3926.
> For isolation:
> - And YARN-4122 proposed to use CGroups to do isolation which cannot solve the problem listed at https://github.com/NVIDIA/nvidia-docker/wiki/GPU-isolation#challenges such as minor device number mapping; load nvidia_uvm module; mismatch of CUDA/driver versions, etc.
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