right issue with train_pipeline.py job (chap 2)

[GodefroyClair]

When I try the pipeline of chapter 2 locally with minikube v1.18.1 & Docker version 20.10.5, I have the following error:

serve
simple-sci-kit-kf-pipeline-8jgpg-992827095
This step is in Failed state with this message: Error from server (Forbidden): error when creating "/tmp/manifest.yaml": seldondeployments.machinelearning.seldon.io is forbidden: User "system:serviceaccount:kubeflow:pipeline-runner" cannot create resource "seldondeployments" in API group "machinelearning.seldon.io" in the namespace "kubeflow"

[akhimji]

Looks like you are missing the Role and RoleBinding. Look under the folder

intro-to-ml-with-kubeflow-examples/ch2_seldon_examples/

applying the following and then try again. This should give you the required permissions.

pipeline_role.yaml
pipeline_rolebinding.yaml

HTH

[GodefroyClair]

Thx HTH, this works.

[zinzinhust96]

@GodefroyClair, I am sorry for bringing up this issue

I have been following the instructions of chapter 2 in the book

First, I ran the step to create the training workflow pipeline. It ran successfully:
dsl-compile --py train_pipeline.py --output job.yaml

After that, I logged in the Kubeflow UI via
kubectl port-forward svc/istio-ingressgateway -n istio-system 7777:80
and then went to localhost:7777.

Next, I need to create a namespace. I tried to type in "kubeflow", as this is the namespace you use to run and encounter the error. But the namespace "kubeflow" is not allowed

I have tried using other name for the namespace in this step (such as "anonymous"). But I found that in kubectl get namespace, it create a new namespace. I then use the web UI to import job.yaml pipeline and run the experiment, but I encounter the same error as you did (with the new namespace ofc).

This step is in Failed state with this message: Error from server (Forbidden): error when creating "/tmp/manifest.yaml": seldondeployments.machinelearning.seldon.io is forbidden: User "system:serviceaccount:anonymous:pipeline-runner" cannot create resource "seldondeployments" in API group "machinelearning.seldon.io" in the namespace "anonymous"

The solution of @akhimji does not work

Can you guide me how to run the experiments with the "kubeflow" namespace like you did, and what step am I doing wrong?
Thanks a bunch!