Add note on the use of kubectl kustomize (#1610)

content/docs/components/training/mpi.md

@@ -13,7 +13,7 @@ The MPI Operator makes it easy to run allreduce-style distributed training on Ku
 
 ## Installation
 
-You can deploy the operator with default settings without using `kustomize` by running the following commands:
+You can deploy the operator with default settings by running the following commands:
 
 ```shell
 git clone https://github.com/kubeflow/mpi-operator
@@ -40,7 +40,7 @@ mpijobs.kubeflow.org                       4d
 ...
 ```
 
-If it is not included you can add it as follows:
+If it is not included you can add it as follows using [kustomize](https://github.com/kubernetes-sigs/kustomize):
 
 ```bash
 git clone https://github.com/kubeflow/manifests
@@ -48,6 +48,12 @@ cd manifests/mpi-job/mpi-operator
 kustomize build base | kubectl apply -f -
 ```
 
+Note that since Kubernetes v1.14, `kustomize` became a subcommand in `kubectl` so you can also run the following command instead:
+
+```bash
+kubectl kustomize base | kubectl apply -f -
+```
+
 ## Creating an MPI Job
 
 You can create an MPI job by defining an `MPIJob` config file. See [TensorFlow benchmark example](https://github.com/kubeflow/mpi-operator/blob/master/examples/v1alpha2/tensorflow-benchmarks.yaml) config file for launching a multi-node TensorFlow benchmark training job. You may change the config file based on your requirements.