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KEP-2401: Kubeflow LLM Trainer V2 #2410
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@Electronic-Waste: GitHub didn't allow me to request PR reviews from the following users: saileshd1402, varshaprasad96, truc0, astefanutti, seanlaii. Note that only kubeflow members and repo collaborators can review this PR, and authors cannot review their own PRs. In response to this:
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Pull Request Test Coverage Report for Build 13089269276Details
💛 - Coveralls |
Should security, so hard multi-tenancy, istio support and Podsecuritystandards restricted be part of the KEP? |
@juliusvonkohout We haven't considered it yet. Our initial goal is to introduce simple approaches to see how users will use this feature, and make it as easy as possible to use. Maybe we could add them as the tasks for the next stage. WDYT @franciscojavierarceo @kubeflow/wg-training-leads |
I would probably leave that out of scope. Not to say that it's not important of course. |
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Hi Folks, just a friendly reminder that this Wednesday at 5pm UTC, we will discuss the cc @kubeflow/wg-training-leads @Electronic-Waste @franciscojavierarceo @joecummings @astefanutti @akshaychitneni @shravan-achar @janeyx99 @bigsur0 |
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
…tives. Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
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Thanks for updating KEP @Electronic-Waste!
I left a few comments.
| quantize_base | Whether to enable model quantization | | ||
| use_dora | Whether to enable DoRA | | ||
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It would be nice if you could also add the section of how we are going to structure these recipes.
And how we are going to select the default resources, number of PyTorch nodes.
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Got it! Thanks!
@andreyvelich Amazing! Huge thanks to your detailed reviews! |
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
Signed-off-by: Electronic-Waste <[email protected]>
This is the Kubeflow Enhancement Proposal for Kubeflow LLM Trainer V2: http://bit.ly/4gp8JGd
Related: #2401 #2170
We are collecting the final community feedback and any suggestions are welcome!
Open Questions
tune run
CLI to enable distributed training, instead of passing distributed parameters begins withPET_
to env variables. Do you prefer reusing thetorch
runtime plugin or creating a new one?/cc @kubeflow/wg-training-leads @deepanker13 @saileshd1402 @seanlaii @helenxie-bit @astefanutti @varshaprasad96 @franciscojavierarceo @thesuperzapper @rimolive @juliusvonkohout @jbottum @varodrig @Doris-xm @truc0