From a106964150bda62300f9b9af9a9871656ab4e000 Mon Sep 17 00:00:00 2001 From: Karen Bradshaw Date: Wed, 4 Sep 2019 01:40:58 -0400 Subject: [PATCH] remove content for TFJob dashboard (#1120) * remove content for TFJob dashboard Closes #1091. * fix rebasing mixups --- .../docs/components/training/tftraining.md | 22 ------------------- content/docs/other-guides/accessing-uis.md | 4 +--- 2 files changed, 1 insertion(+), 25 deletions(-) diff --git a/content/docs/components/training/tftraining.md b/content/docs/components/training/tftraining.md index f1e522071d..9a5ef6882d 100644 --- a/content/docs/components/training/tftraining.md +++ b/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 `/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://.endpoints..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. diff --git a/content/docs/other-guides/accessing-uis.md b/content/docs/other-guides/accessing-uis.md index be322788bf..34ab01f0db 100644 --- a/content/docs/other-guides/accessing-uis.md +++ b/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.