mirror of https://github.com/kubeflow/examples.git
[docs]: enhance documentation for chosen namespace (#555)
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@ -91,7 +91,9 @@ With our data and workloads ready, now the cluster must be prepared. We will be
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In the following instructions we will install our required components to a single namespace. For these instructions we will assume the chosen namespace is `kubeflow`.
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```
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kubectl config set-context $(kubectl config current-context) --namespace=kubeflow
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```
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### Training your model
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@ -246,7 +248,7 @@ then a number of steps have already been performed for you
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* To see the secrets in your cluster
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```
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kubectl get secrets -n kubeflow
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kubectl get secrets
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```
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3. We granted this service account permission to read/write GCS buckets in this project
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@ -596,7 +598,7 @@ kustomize build . | kubectl apply -f -
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To access TensorBoard using port-forwarding
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```
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kubectl -n kubeflow port-forward service/tensorboard-tb 8090:80
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kubectl port-forward service/tensorboard-tb 8090:80
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```
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TensorBoard can now be accessed at [http://127.0.0.1:8090](http://127.0.0.1:8090).
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