Docs should refer to Kubeflow user guide for deploying the TFJob oper…

…ator. (#412) * We don't have the resources to support and maintain ksonnet and helm packages. * We want to focus on just using ksonnet to deploy Kubeflow.
kubeflow · Feb 27, 2018 · 0094aaa · 0094aaa
1 parent c54cda9
commit 0094aaa
Showing 1 changed file with 2 additions and 125 deletions.
diff --git a/README.md b/README.md
@@ -46,132 +46,9 @@ TFJob requires Kubernetes >= 1.8
 
 ## Installing the TFJob CRD and operator on your k8s cluster
 
-1. Ensure helm is running on your cluster
+Please refer to the [Kubeflow user guide](https://github.com/kubeflow/kubeflow/blob/master/user_guide.md).
 
-   * On GKE with K8s 1.8, follow these
-     [instructions](https://docs.helm.sh/using_helm/#tiller-namespaces-and-rbac)
-     to setup appropriate service accounts for tiller.
-
-   * Azure K8s clusters should have service accounts configured by
-     default for tiller.
-
-1. Deploy the operator
-
-   For RBAC-enabled clusters:
-   ```
-   CHART=https://storage.googleapis.com/tf-on-k8s-dogfood-releases/latest/tf-job-operator-chart-latest.tgz
-   helm install ${CHART} -n tf-job --wait --replace --set rbac.install=true,cloud=<gke or azure>
-   ```
-
-   * If you aren't running on GKE or Azure don't set cloud.
-
-   For non-RBAC enabled clusters:
-   ```
-   CHART=https://storage.googleapis.com/tf-on-k8s-dogfood-releases/latest/tf-job-operator-chart-latest.tgz
-   helm install ${CHART} -n tf-job --wait --replace --set cloud=<gke or azure>
-   ```
-
-    * The above instructions use the latest release.
-    * Releases are versioned
-    * You can see a list of versions
-    ```
-    gsutil ls  gs://tf-on-k8s-dogfood-releases
-    ```
-    * **Avoiding Breakages**
-      * During Alpha there is no guarantees about TFJob API
-        compatibility.
-      * To avoid being broken by changes you can pin to a particular
-        version of the helm chart and control when you upgrade.
-
-1. Make sure the operator is running
-
-    ```
-    kubectl get pods
-
-    NAME                               READY     STATUS    RESTARTS   AGE
-    tf-job-operator-3083500267-wxj43   1/1       Running   0          48m
-
-    ```
-
-1. Run the helm tests
-
-    ```
-    helm test tf-job
-    RUNNING: tf-job-tfjob-test-pqxkwk
-    PASSED: tf-job-tfjob-test-pqxkwk
-    ```
-
-### Installing `kubeflow/tf-operator`'s Dashboard
-
-> **Caution: the dashboard is in very early development stage!**
-
-`kubeflow/tf-operator` also includes a dashboard allowing you to monitor and create `TFJobs` through a web UI.
-To deploy the dashboard, set `dashboard.install` to `true`.
-Note that by default the dashboard will only be accessible from within the cluster or by proxying, as the default `ServiceType` is `ClusterIP`.
-If you wish to expose the dashboard through an external IP, set `dashboard.serviceType` to `LoadBalancer`.
-
-So, for example, if you want to enable the dashboard, and also want to expose it externally, you would do:
-
-```
-CHART=https://storage.googleapis.com/tf-on-k8s-dogfood-releases/latest/tf-job-operator-chart-latest.tgz
-helm install ${CHART} -n tf-job --wait --replace --set cloud=<gke or azure>,dashboard.install=true,dashboard.serviceType=LoadBalancer
-```
-
-This should create a service named `tf-job-dashboard` as well as an additional deployment named `tf-job-dashboard`.
-
-
-### Configuring the CRD
-
-The CRD must be configured properly to work with your specific Kubernetes cluster.
-Since it will be mounting GPU drivers into your pods, the CRD needs to know where to find them on the Kubernetes agents. It also needs to know which environment variable needs to be injected in the pods.
-
-If your Kubernetes cluster is running on GKE or Azure (ACS, AKS, acs-engine) simply pass the provider name to the helm install (or in `tf-job-operator-chart/values.yaml`).
-
-For **GKE**:
-```
-helm install ${CHART} -n tf-job --wait --replace --set cloud=gke
-```
-
-For **Azure**:
-```
-helm install ${CHART} -n tf-job --wait --replace --set cloud=azure
-```
-
-If the cluster is not hosted on GKE or Azure, you will need to specify a custom configuration.
-To do so create a `ConfigMap` with your desired settings.
-
-This is the structure of the expected configuration file:
-
-```yaml
-apiVersion: v1
-kind: ConfigMap
-metadata:
-  name: tf-job-operator-config
-  namespace: default
-data:
-  controller-config-file.yaml: |
-    accelerators:
-      alpha.kubernetes.io/nvidia-gpu:
-        volumes:
-          - name: <volume-name> # Desired name of the volume, ex: nvidia-libs
-            mountPath: <mount-path> # Path where this should be mounted
-            hostPath: <host-path> # Path on the host machine
-          - name: <volume2-name> # optional
-            mountPath: <mount-path>
-            hostPath: <host-path>
-        envVars:
-          - name: <env-var-name> # Name of the environment variable, ex: LD_LIBRARY_PATH
-            value: <env-value> # Value of the environment variable
-```
-
-Then simply create the `ConfigMap` and install the Helm chart (**the order matters**) without specifying any cloud provider:
-
-```
-kubectl create configmap tf-job-operator-config --from-file <your-configmap-path> --dry-run -o yaml | kubectl replace configmap tf-job-operator-config -f -
-helm install ${CHART} -n tf-job --wait --replace
-```
-
-Subsequently, any pod requesting a resource of type `alpha.kubernetes.io/nvidia-gpu` will have these Volumes\VolumeMounts and environment variables injected at creation.
+We recommend deploying Kubeflow in order to use the TFJob operator.
 
 ## Creating a job