Add references to kustomize issues (#1015)

content/docs/components/serving/istio.md

@@ -35,6 +35,8 @@ the pod has annotation.
 
 ## Kubeflow TF Serving with Istio
 
+_This section has not yet been converted to kustomize, please refer to [kubeflow/manifests/issues/18](https://github.com/kubeflow/manifests/issues/18)._
+
 After installing Istio, we can deploy the TF Serving component as in 
 [TensorFlow Serving](/docs/components/tfserving_new/) with
 additional params:

content/docs/components/serving/pytorchserving.md

@@ -35,6 +35,8 @@ You can find more details about wrapping a model with seldon-core [here](https:/
 
 ## Deploying the model to your Kubeflow cluster
 
+_This section has not yet been converted to kustomize, please refer to [kubeflow/manifests/issues/10](https://github.com/kubeflow/manifests/issues/10)._
+
 We need to have seldon component deployed, you can deploy the model once trained using a pre-defined ksonnet component, similar to [this](https://github.com/kubeflow/examples/blob/master/pytorch_mnist/ks_app/components/serving_model.jsonnet) example.
 
 Create an environment variable, `${KF_ENV}`, to represent a conceptual

content/docs/components/serving/seldon.md

@@ -5,6 +5,9 @@ weight = 40
 +++
 
 ## Serve a model using Seldon
+
+_This section has not yet been converted to kustomize, please refer to [kubeflow/manifests/issues/10](https://github.com/kubeflow/manifests/issues/10)._
+
 [Seldon-core](https://github.com/SeldonIO/seldon-core) provides deployment for any machine learning runtime that can be [packaged in a Docker container](https://docs.seldon.io/projects/seldon-core/en/latest/wrappers/README.html).
 
 Install the seldon package:

content/docs/components/serving/tfserving_new.md

@@ -6,6 +6,8 @@ weight = 51
 
 ## Serving a model
 
+_This section has not yet been converted to kustomize, please refer to [kubeflow/website/issues/958](https://github.com/kubeflow/website/issues/958)._
+
 We treat each deployed model as two [components](https://ksonnet.io/docs/tutorial#2-generate-and-deploy-an-app-component)
 in your APP: one tf-serving-deployment, and one tf-serving-service.
 We can think of the service as a model, and the deployment as the version of the model.

content/docs/components/serving/trtinferenceserver.md

@@ -81,6 +81,8 @@ $ gsutil cp -r model_store gs://inference-server-model-store
 
 ## Kubernetes Generation and Deploy
 
+_This section has not yet been converted to kustomize, please refer to [kubeflow/website/issues/959](https://github.com/kubeflow/website/issues/959)._
+
 Next use ksonnet to generate Kubernetes configuration for the NVIDIA TensorRT
 Inference Server deployment and service. The --image option points to
 the NVIDIA Inference Server container in the [NVIDIA GPU Cloud

content/docs/components/training/chainer.md

@@ -31,6 +31,8 @@ An **alpha** version of [Chainer](https://chainer.org/) support was introduced w
 
 ## Verify that Chainer support is included in your Kubeflow deployment
 
+_This section has not yet been converted to kustomize, please refer to [kubeflow/manifests/issues/232](https://github.com/kubeflow/manifests/issues/232)._
+
 Check that the Chainer Job custom resource is installed
 
 ```shell

content/docs/components/training/mpi.md

@@ -8,6 +8,8 @@ This guide walks you through using MPI for training.
 
 ## Installation
 
+_This section has not yet been converted to kustomize, please refer to [kubeflow/manifests/issues/227](https://github.com/kubeflow/manifests/issues/227)._
+
 If you haven’t already done so please follow the [Getting Started Guide](https://www.kubeflow.org/docs/started/getting-started/) to deploy Kubeflow.
 
 An alpha version of MPI support was introduced with Kubeflow 0.2.0. You must be using a version of Kubeflow newer than 0.2.0.

content/docs/components/training/mxnet.md

@@ -8,6 +8,8 @@ This guide walks you through using MXNet with Kubeflow.
 
 ## Installing MXNet Operator
 
+_This section has not yet been converted to kustomize, please refer to [kubeflow/manifests/issues/228](https://github.com/kubeflow/manifests/issues/228)._
+
 If you haven't already done so please follow the [Getting Started Guide](https://www.kubeflow.org/docs/started/getting-started/) to deploy Kubeflow.
 
 A version of MXNet support was introduced with Kubeflow 0.2.0. You must be using a version of Kubeflow newer than 0.2.0.