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Should workflow templates pre-exist prior to running kubectl create -f workflow.yaml
?
#3
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Thanks! That is fixed in the book and I just fixed them in the README file in the repo as well.
It seems like the template does not exist, could you run a |
Actually can you try the lastest version in main branch? That data ingestion template should already exist in https://github.com/terrytangyuan/distributed-ml-patterns/blob/main/code/project/code/workflow.yaml#L26 |
@terrytangyuan .. thanks for the prompt response. I have been using the latest code as of today from this repo. Yes, those I also hadn't focused on the README at distributed-ml-patterns/tree/main/code/project/code, which contains some stuff that hasn't made it into the book version (7) I had been following. Maybe that has since been addressed. Chapter 9 is full of great content, but I'm sometimes struggling in getting the code-snippets from the book working ... as you explain how each function works ... it seems they need to be run as the full python script (all functions together) ... like https://github.com/terrytangyuan/distributed-ml-patterns/blob/main/code/project/code/multi-worker-distributed-training.py. Getting things working in Chapter 9 requires you to have run certain steps in Chapter 8 of the book. If it's not too late, it might be worth recapping those required steps at the beginning of Chapter 9, since some readers might wish to plunge directly into the end-to-end in Chapter 9. |
That's great feedback. Thank you! If you have specific recommendation on what prerequisites are missing to follow the code snippets in the last chapter, please let me know.
They should not be part of the book. Please follow the |
So I ran these, per the README, and they appeared to run OK (see middle job in screenshot below).
However, each time I try to run It seems I am missing the various templates referenced here. It somehow expects these workflow-templates to pre-exist, but I can't find them anywhere in the code. Am I missing something? |
Thank you! I just fixed it. Could you try again? |
That change permitted the workflow to begin running. It seemed like a small tweak. Do you mind explaining what the fix was? However, I'm seeing that the If I look into one of them I see the following 'event' - Maybe this is a namespace issue. My |
Did you run the following to change to current namespace? kns kubeflow Once it's ran, all your manifests without specifying the namespace will use the current namespace. |
Is |
I see it at blendle/kns. I suppose the kubectl-native way would be |
Ran this:
Same issue with Will have to try to re-create |
It should be covered in previous chapter. Instead of switching current namespace, you can also add -n Kubeflow in your kubectl commands to specify namespace explicitly. |
@terrytangyuan ... got things working by recreating the I noticed here, that it should be I'll close out this for now and maybe raise a separate issue ref. other feedback on Chap 9. |
Great thanks! |
@terrytangyuan .. I've been working my way through your book. Thanks for putting together such an informative book. The latest version available on Manning is v7 from May. Perhaps there is something more up to date ... that might explain why I'm hitting the issue described below?
From chapter 8, various CRDs are created with
kubectl kustomize manifests | k apply -f -
. It might be worth using the long-formkubectl kustomize manifests | kubectl apply -f -
, for those that may not have an aliask
set up forkubectl
.I notice that this calls the
distributed-ml-patterns/code/project/manifests/kustomization.yaml
file, which in turn activates various manifests in theargo-workflows
andkubeflow-training
folders.It seems when I try to run
kubectl create -f workflow.yaml
in Chapter 9, it fails (see below). I think it might be due to the absence of the correct workflow-templates pre-populated in argo. Could it be that manifests from thee2e-demo
folder should have been included in thekustomization.yaml
above, or is something else missing?Appreciate your input. Thanks.
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