Add shell to commands

content/en/docs/components/katib/experiment.md

@@ -662,7 +662,7 @@ To define the metrics collector for your experiment:
    if the name of your objective metric is `loss` and the additional metrics are
    `recall` and `precision`, your training code should print the following output:
 
-   ```
+   ```shell
    epoch 1:
    loss=0.3
    recall=0.5
@@ -683,27 +683,27 @@ You can run a Katib experiment from the command line or from the Katib UI.
 You can use [kubectl](https://kubernetes.io/docs/reference/kubectl/overview/)
 to launch an experiment from the command line:
 
-```
+```shell
 kubectl apply -f <your-path/your-experiment-config.yaml>
 ```
 
 Note that if you deployed Katib as part of Kubeflow, you need to
 change Kubeflow namespace to your profile namespace.
 Run the following command to launch an experiment using the random algorithm example:
 
-```
+```shell
 kubectl apply -f https://raw.githubusercontent.com/kubeflow/katib/master/examples/v1beta1/random-example.yaml
 ```
 
 Check the experiment status:
 
-```
+```shell
 kubectl -n kubeflow describe experiment <your-experiment-name>
 ```
 
 For example, to check the status of the random algorithm example:
 
-```
+```shell
 kubectl -n kubeflow describe experiment random-example
 ```
 

content/en/docs/components/katib/hyperparameter.md

@@ -32,7 +32,7 @@ If you want to install Katib separately from Kubeflow, or to get a later version
 of Katib, run the following commands to install Katib directly from its
 repository on GitHub and deploy Katib to your cluster:
 
-```
+```shell
 git clone https://github.com/kubeflow/katib
 make deploy
 ```
@@ -54,7 +54,7 @@ volume (PV) to bind to the persistent volume claim (PVC) required by Katib.
 After deploying Katib to your cluster, run the following command to create the
 PV:
 
-```
+```shell
 kubectl apply -f https://raw.githubusercontent.com/kubeflow/katib/master/manifests/v1beta1/pv/pv.yaml
 ```
 
@@ -82,13 +82,13 @@ Katib UI from the Kubeflow UI:
 
 Alternatively, you can set port-forwarding for the Katib UI service:
 
-```
+```shell
 kubectl port-forward svc/katib-ui -n kubeflow 8080:80
 ```
 
 Then you can access the Katib UI at this URL:
 
-```
+```shell
 http://localhost:8080/katib/
 ```
 
@@ -122,18 +122,19 @@ example:
 
 1. Download the example:
 
-   ```
+   ```shell
    curl https://raw.githubusercontent.com/kubeflow/katib/master/examples/v1beta1/random-example.yaml --output random-example.yaml
    ```
 
 1. Edit `random-example.yaml` and change the following line to use your Kubeflow user profile namespace:
 
-   ```
+   ```shell
    Namespace: kubeflow
    ```
 
 1. Deploy the example:
-   ```
+
+   ```shell
    kubectl apply -f random-example.yaml
    ```
 
@@ -152,7 +153,7 @@ This example randomly generates the following hyperparameters:
 
 Check the experiment status:
 
-```
+```shell
 kubectl -n <your user profile namespace> get experiment random-example -o yaml
 ```
 
@@ -349,27 +350,28 @@ If you installed Katib as part of Kubeflow, you can’t run experiments in Kubef
 Run the following commands to launch an experiment using the Kubeflow's
 TensorFlow training job operator, TFJob:
 
-1. Download the tfjob-example.yaml file
+1. Download the tfjob-example.yaml file:
 
-   ```
+   ```shell
    curl https://raw.githubusercontent.com/kubeflow/katib/master/examples/v1beta1/tfjob-example.yaml --output tfjob-example.yaml
    ```
 
 1. Edit `tfjob-example.yaml` and change the following line to use your Kubeflow user profile namespace:
 
-   ```
+   ```shell
    Namespace: kubeflow
    ```
 
 1. Deploy the example:
 
-   ```
+   ```shell
    kubectl apply -f tfjob-example.yaml
    ```
 
 1. You can check the status of the experiment:
-   ```
-   kubectl -n <your user profile namespace> describe experiment tfjob-example
+
+   ```shell
+   kubectl -n <your user profile namespace> get experiment tfjob-example -o yaml
    ```
 
 Follow the steps as described for the _random algorithm example_
@@ -383,24 +385,25 @@ training job operator, PyTorchJob:
 
 1. Download the pytorchjob-example.yaml file
 
-   ```
+   ```shell
    curl https://raw.githubusercontent.com/kubeflow/katib/master/examples/v1beta1/pytorchjob-example.yaml --output pytorchjob-example.yaml
    ```
 
 1. Edit `pytorchjob-example.yaml` and change the following line to use your Kubeflow user profile namespace:
 
-   ```
+   ```shell
    Namespace: kubeflow
    ```
 
 1. Deploy the example:
 
-   ```
+   ```shell
    kubectl apply -f pytorchjob-example.yaml
    ```
 
 1. You can check the status of the experiment:
-   ```
+
+   ```shell
    kubectl -n <your user profile namespace> describe experiment pytorchjob-example
    ```
 
@@ -411,7 +414,7 @@ Follow the steps as described for the _random algorithm example_
 
 To delete Katib from Kubernetes cluster run::
 
-```
+```shell
 make undeploy
 ```