[jira] [Resolved] (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 ] Vinod Kumar Vavilapalli resolved YARN-6223. --- Resolution: Fixed > [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: Wangda Tan >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. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: yarn-issues-unsubscr...@hadoop.apache.org For additional commands, e-mail: yarn-issues-h...@hadoop.apache.org
[jira] [Resolved] (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 ] Wangda Tan resolved YARN-6223. -- Resolution: Done Fix Version/s: 3.1.0 Closing as done since all sub tasks are done. > [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: Wangda Tan >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. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: yarn-issues-unsubscr...@hadoop.apache.org For additional commands, e-mail: yarn-issues-h...@hadoop.apache.org
