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remove content for TFJob dashboard (#1120)

* remove content for TFJob dashboard

Closes #1091.

* fix rebasing mixups
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kbhawkey authored and k8s-ci-robot committed Sep 4, 2019
1 parent 4390256 commit a106964150bda62300f9b9af9a9871656ab4e000
Showing with 1 addition and 25 deletions.
  1. +0 −22 content/docs/components/training/
  2. +1 −3 content/docs/other-guides/
@@ -236,28 +236,6 @@ Typically you can change the following values in the TFJob yaml file:

1. Attach PVs if you want to use PVs for storage.

### Accessing the TFJob dashboard

The TFJob dashboard has the title **kubeflow/tf-operator**.
You can access it at `<path>/tfjobs/ui/`. Specifically:

* If you followed the
guide to [deploying Kubeflow on GCP](/docs/gke/deploy/), you can
access the TFJob dashboard at the following URL:


* If you're using portforwarding, you can access the TFJob dashboard at the
following URL:


See more details about [accessing the Kubeflow UIs](/docs/other-guides/accessing-uis).

## Using GPUs

To use GPUs your cluster must be configured to use GPUs.
@@ -12,11 +12,10 @@ instructions on how to connect to them.
The Kubeflow UIs include the following:

* A central **Kubeflow** UI for navigation between the Kubeflow applications.
* **Pipelines** for a Kubeflow Pipelines dashboard
* **Pipelines** for a Kubeflow Pipelines dashboard.
* **Notebook Servers** for Jupyter notebooks.
* **Katib** for hyperparameter tuning.
* **Artifact Store** for tracking of artifact metadata.
* **tf-operator** for a TFJob dashboard.

Instructions below indicate how to connect to the Kubeflow central UI. From
there you can navigate to the different services using the left hand navigation
@@ -109,5 +108,4 @@ You can access Kubeflow via `kubectl` and port-forwarding as follows:

## Next steps

* See how to [access the TFJob dashboard](/docs/components/training/tftraining/).
* [Set up your Jupyter notebooks](/docs/notebooks/setup/) in Kubeflow.

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