Clarified meaning of environment in KF_ENV. (#625)

content/docs/components/hyperparameter.md

@@ -12,7 +12,8 @@ framework (such as TensorFlow, MXNet, or PyTorch).
 
 ## Installing Katib
 
-To run Katib jobs, you must install the required packages.
+To run Katib jobs, you must install the required packages as shown in this
+section.
 
 In your ksonnet application's root directory, run the following commands:
 
@@ -22,8 +23,12 @@ ks env set ${KF_ENV} --namespace=kubeflow
 ks registry add kubeflow github.com/kubeflow/kubeflow/tree/master/kubeflow
 ```
 
-You can read more about Kubeflow's use of ksonnet in the [ksonnet component
-guide](/docs/components/ksonnet/).
+The `KF_ENV` environment variable represents a conceptual deployment environment 
+such as development, test, staging, or production, as defined by 
+ksonnet. For this example, we use the `default` environment.
+
+You can read more about Kubeflow's use of ksonnet in the Kubeflow 
+[ksonnet component guide](/docs/components/ksonnet/).
 
 ### TFJob (tf-operator)
 

content/docs/components/istio.md

@@ -96,6 +96,7 @@ and gradually move traffic from A to B. This can be achieved using Istio's traff
 
     ```
     MODEL_COMPONENT2=mnist-v2
+    KF_ENV=default
     ks generate tf-serving-deployment-gcp ${MODEL_COMPONENT2}
     ks param set ${MODEL_COMPONENT2} modelName mnist  // modelName should be the SAME as  the previous one
     ks param set ${MODEL_COMPONENT2} versionName v2   // v2 !!
@@ -106,6 +107,12 @@ and gradually move traffic from A to B. This can be achieved using Istio's traff
     ks apply ${KF_ENV} -c ${MODEL_COMPONENT2}
     ```
 
+    The `KF_ENV` environment variable represents a conceptual deployment environment 
+    such as development, test, staging, or production, as defined by 
+    ksonnet. For this example, we use the `default` environment.
+    You can read more about Kubeflow's use of ksonnet in the Kubeflow 
+    [ksonnet component guide](/docs/components/ksonnet/).
+
 3. Update the traffic weight
    
    ```

content/docs/components/pytorchserving.md

@@ -36,10 +36,18 @@ You can find more details about wrapping a model with seldon-core [here](https:/
 ## Deploying the model to your Kubeflow cluster
 
 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.
-We need to setup our own environment `${KF_ENV}` (e.g., 'default') and modify the Ksonnet component 
+
+Create an environment variable, `${KF_ENV}`, to represent a conceptual
+deployment environment such as development, test, staging, or production, as
+defined by ksonnet. For this example, we use the `default` environment. You can
+read more about Kubeflow's use of ksonnet in the Kubeflow 
+[ksonnet component guide](/docs/components/ksonnet/).
+
+Then modify the Ksonnet component 
 [parameters](https://github.com/kubeflow/examples/blob/master/pytorch_mnist/ks_app/components/params.libsonnet) to use your specific image.
 
 ```bash
+export KF_ENV=default
 cd ks_app
 ks env add ${KF_ENV}
 ks apply ${KF_ENV} -c serving_model

content/docs/components/seldon.md

@@ -28,9 +28,16 @@ ks generate seldon seldon
 Deploy seldon cluster manager:
 
 ```
+export KF_ENV=default
 ks apply ${KF_ENV} -c seldon
 ```
 
+The `KF_ENV` environment variable represents a conceptual deployment environment 
+such as development, test, staging, or production, as defined by 
+ksonnet. For this example, we use the `default` environment.
+You can read more about Kubeflow's use of ksonnet in the Kubeflow 
+[ksonnet component guide](/docs/components/ksonnet/).
+
 ### Seldon Deployment Graphs
 
 Seldon allows complex runtime graphs for model inference to be deployed. Some example prototypes have been provided to help you get started. Follow the [Seldon docs](https://github.com/SeldonIO/seldon-core/blob/master/docs/wrappers/readme.md) to wrap your model code into an image that can be managed by Seldon. In the examples below we will use a model image ```seldonio/mock_classifier``` ; replace this with your actual model image. You will also need to choose between the v1alpha2 and v1alpha1 prototype examples depending on which version of Seldon you generated above. The following prototypes are available:

content/docs/components/tfbatchpredict.md

@@ -100,9 +100,16 @@ configuration to start the kubernetes job.
 ### Submit the job
 
 ```
+export KF_ENV=default
 ks apply ${KF_ENV} -c ${MY_BATCH_PREDICT_JOB_NAME}
 ```
 
+The `KF_ENV` environment variable represents a conceptual deployment environment 
+such as development, test, staging, or production, as defined by 
+ksonnet. For this example, we use the `default` environment.
+You can read more about Kubeflow's use of ksonnet in the Kubeflow 
+[ksonnet component guide](/docs/components/ksonnet/).
+
 You should see that a job is started to provision the batch-predict docker image.
 Then a pod starts to run the job.
 

content/docs/components/tfserving_new.md

@@ -104,10 +104,17 @@ for serving an object detection model with GPU.
 ### Deploying
 
 ```
+export KF_ENV=default
 ks apply ${KF_ENV} -c mnist-service
 ks apply ${KF_ENV} -c ${MODEL_COMPONENT}
 ```
 
+The `KF_ENV` environment variable represents a conceptual deployment environment 
+such as development, test, staging, or production, as defined by 
+ksonnet. For this example, we use the `default` environment.
+You can read more about Kubeflow's use of ksonnet in the Kubeflow 
+[ksonnet component guide](/docs/components/ksonnet/).
+
 ### Sending prediction request directly
 If the service type is LoadBalancer, it will have its own accessible external ip.
 Get the external ip by:

content/docs/components/tftraining.md

@@ -176,13 +176,20 @@ Run the `generate` command:
 ks generate tf-job-simple-v1beta1 ${CNN_JOB_NAME} --name=${CNN_JOB_NAME}
 ```
 
-Submit it
+Submit the job:
 
 ```
+export KF_ENV=default
 ks apply ${KF_ENV} -c ${CNN_JOB_NAME}
 ```
 
-Monitor it (Please refer to the [TfJob docs](https://github.com/kubeflow/tf-operator#monitoring-your-job))
+The `KF_ENV` environment variable represents a conceptual deployment environment 
+such as development, test, staging, or production, as defined by 
+ksonnet. For this example, we use the `default` environment.
+You can read more about Kubeflow's use of ksonnet in the Kubeflow 
+[ksonnet component guide](/docs/components/ksonnet/).
+
+Monitor the job (see the [TFJob docs](/docs/components/tftraining/#monitoring-your-job)):
 
 ```
 kubectl get -n kubeflow -o yaml tfjobs ${CNN_JOB_NAME}

content/docs/other-guides/troubleshooting.md

@@ -192,6 +192,7 @@ Kubeflow requires a [specific version of ksonnet](/docs/started/requirements).
 If you run `ks apply` with an older version of ksonnet you will likely get the error `Unknown variable: env` as illustrated below:
 
 ```shell
+export KF_ENV=default
 ks apply ${KF_ENV}
 ERROR Error reading /Users/xxx/projects/devel/go/src/github.com/kubeflow/kubeflow/my-kubeflow/environments/nocloud/main.jsonnet: /Users/xxx/projects/devel/go/src/github.com/kubeflow/kubeflow/my-kubeflow/components/jupyterhub.jsonnet:8:49-52 Unknown variable: env