remove content for TFJob dashboard (#1120)

* remove content for TFJob dashboard

Closes #1091.

* fix rebasing mixups

content/docs/components/training/tftraining.md

@@ -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:
-
-    ```
-    https://<deployment-name>.endpoints.<project>.cloud.goog/tfjobs/ui/
-    ```
-
-* If you're using portforwarding, you can access the TFJob dashboard at the
-  following URL:
-
-    ```
-    http://localhost:8080/tfjobs/ui/
-    ```
-
-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.

content/docs/other-guides/accessing-uis.md

@@ -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.