<commit_before>## Setup Kubeflow
### Requirements

 - Kubernetes cluster
 - Access to a working `kubectl` (Kubernetes CLI)
 - Ksonnet CLI: [ks](https://ksonnet.io/)

### Setup
Refer to the [getting started guide](https://www.kubeflow.org/docs/started/getting-started) for instructions on how to setup kubeflow on your kubernetes cluster. Specifically, look at the [quick start](https://www.kubeflow.org/docs/started/getting-started/#quick-start) section.
For this example, we will be using ks `nocloud` environment (on premise K8s). If you plan to use `cloud` ks environment, please make sure you follow the proper instructions in the kubeflow getting started guide.

After completing the steps in the kubeflow getting started guide you will have the following:
- A ksonnet app directory called `my-kubeflow` 
- A new namespace in you K8s cluster called `kubeflow`
- The following pods in your kubernetes cluster in the `kubeflow` namespace:
```
kubectl -n kubeflow get pods
NAME                                      READY     STATUS    RESTARTS   AGE
ambassador-7987df44b9-4pht8               2/2       Running   0          1m
ambassador-7987df44b9-dh5h6               2/2       Running   0          1m
ambassador-7987df44b9-qrgsm               2/2       Running   0          1m
tf-hub-0                                  1/1       Running   0          1m
tf-job-operator-v1alpha2-b76bfbdb-lgbjw   1/1       Running   0          1m
```

## Overview

During the course of this tutorial you will apply a set of ksonnet components that will:

1. Create a PersistentVolume to store our data and training results.
2. Download the dataset, dataset annotations, a pre-trained model checkpoint, and the training pipeline configuration file.
3. Decompress the downloaded dataset, pre-trained model, and dataset annotations.
4. Create a TensorFlow pet record since we will be training a pet detector model.
5. Execute a distributed TensorFlow object detection training job using the previous configurations.
6. Export the trained pet detector model and serve it using TF-Serving

We have prepared a ksonnet app `ks-app` with a set of components that will be used in this example.
The components can be found at the [ks-app/components](./ks-app/components) directory in case you want to perform some<commit_msg>Highlight the kubectl command. (#369)<commit_after>## Setup Kubeflow
### Requirements

 - Kubernetes cluster
 - Access to a working `kubectl` (Kubernetes CLI)
 - Ksonnet CLI: [ks](https://ksonnet.io/)

### Setup
Refer to the [getting started guide](https://www.kubeflow.org/docs/started/getting-started) for instructions on how to setup kubeflow on your kubernetes cluster. Specifically, look at the [quick start](https://www.kubeflow.org/docs/started/getting-started/#quick-start) section.
For this example, we will be using ks `nocloud` environment (on premise K8s). If you plan to use `cloud` ks environment, please make sure you follow the proper instructions in the kubeflow getting started guide.

After completing the steps in the kubeflow getting started guide you will have the following:
- A ksonnet app directory called `my-kubeflow` 
- A new namespace in you K8s cluster called `kubeflow`
- The following pods in your kubernetes cluster in the `kubeflow` namespace:
```
$ kubectl -n kubeflow get pods
NAME                                      READY     STATUS    RESTARTS   AGE
ambassador-7987df44b9-4pht8               2/2       Running   0          1m
ambassador-7987df44b9-dh5h6               2/2       Running   0          1m
ambassador-7987df44b9-qrgsm               2/2       Running   0          1m
tf-hub-0                                  1/1       Running   0          1m
tf-job-operator-v1alpha2-b76bfbdb-lgbjw   1/1       Running   0          1m
```

## Overview

During the course of this tutorial you will apply a set of ksonnet components that will:

1. Create a PersistentVolume to store our data and training results.
2. Download the dataset, dataset annotations, a pre-trained model checkpoint, and the training pipeline configuration file.
3. Decompress the downloaded dataset, pre-trained model, and dataset annotations.
4. Create a TensorFlow pet record since we will be training a pet detector model.
5. Execute a distributed TensorFlow object detection training job using the previous configurations.
6. Export the trained pet detector model and serve it using TF-Serving

We have prepared a ksonnet app `ks-app` with a set of components that will be used in this example.
The components can be found at the [ks-app/components](./ks-app/components) directory in case you want to perform some
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