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auto
partition.
#778
Comments
This should be an easy feature. I would support its addition. We need the appropriate underscored attributes in the environment classes where we set it to _default_partitions = {"gpu-shared": "gpu",
"cpu-shared": "shared",
"cpu": "standard",
"gpu": "gpu"} |
Yes, with that it may be possible to implement the auto selection in the base class this. |
Some systems use separate accounts for CPU and GPU: #703. These would not be able to use the auto partition. |
Could we make that a config option, where users can set a default account and a GPU account? |
@tcmoore3 theoretically yes, but then I wonder if we are getting too niche with that. I would rather something more future proof or less logic on our side like an |
Feature description
I would find it more convenient to use flow if the partition were automatically selected based on the job resource request. Many clusters have separate CPU and GPU partitions, or separate shared and whole node partitions. In a workflow with mixed CPU/GPU jobs (and/or jobs of different sizes), the user must manually run (e.g.):
Some operations may auto-scale depending on the number of jobs left to execute. Until the user runs the submission command, they don't know whether
shared
orwholenode
is the appropriate partition.Proposed solution
The user should be able to make one submission:
Additional context
auto
would select from one of the "standard" partitions (e.g. not the debug or high memory partitions) based on the job request:Any partition will remain settable explicitly on request.
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