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Some feedback ref Chap 9 #4
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This will be fixed in a new version.
It's a bit beyond the scope of this book so I tried to avoid too much details.
I only see it in
Where do you see those references? Thank you for your feedback! It might be too late to update the content but we'll try get smaller fixes in before publication. |
Thank you! |
@terrytangyuan , as mentioned, for those looking to run the
workflow.yaml
end-to-end, there were a few infrastructure-related elements introduced in Chap 8 that might be worth recapping at the beginning of Chap 9, so as to ensure that someone focusing on that chapter has the possibility to run things. While you capture certain things in the sub README's on the repo, I have found that sometimes these aren't reflected back in the book .. when they probably should be.k3d cluster create distml --image v1.25.3+k3s1
.... I think you need the 'rancher' part too for this to workk3d cluster create distml --image rancher/k3s:v1.25.3-k3s1
.distributed-ml-patterns/code/project/manifests/kubeflow-training
are presumed to be in-place for Chap 9. I would maybe stick to usingkubectl
and not have expectations on readers to install other short-cut tooling likekns
to work with their cluster.inference-service
in 9.21, this gets replaced by an extended version in 9.28. Should the first one be explicitly deleted before the next one is created? In 9.37, the simpler version (9.21) withoutscaleTarget
andscaleMetric
is included as a manifest in the workflow. Is this intentional?training-operator
atpublic.ecr.aws/j1r0q0g6/training/training-operator
, but also athttps://github.com/orgs/kubeflow/packages/container/package/training%2Ftraining-operator
. These are discussed here. It might be worth explicitly mentioning where the canonical source will be, going forward.Overall, kudos for assembling this book and especially the end-to-end workflow in Chap 9. It offers a great blue-print for handling something 'real-world', which is rare to have in these sorts of text-books.
Best, Colum
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