Updated mnist example README with AWS credentials setting

mnist/README.md

@@ -354,47 +354,50 @@ various environment variables configuring access to S3.
        --from-literal=awsSecretAccessKey=${AWS_SECRET_ACCESS_KEY}
      ```
   
-  1. Mount the secret into the pod
+  1. Pass secrets as environment variables into pod
 
      ```
-     ks param set --env=${KSENV} train secret aws-creds=/var/secrets
+     ks param set --env=${KSENV} train secretKeyRefs AWS_ACCESS_KEY_ID=aws-creds.awsAccessKeyID,AWS_SECRET_ACCESS_KEY=aws-creds.awsSecretAccessKey
      ```
 
-     * Setting this ksonnet parameter causes a volumeMount and volume to be added to your TFJob
+     * Setting this ksonnet parameter causes a two new environment variables to be added to your TFJob
      * To see this you can run
 
        ```
        ks show ${KSENV} -c train
        ```
 
-     * The output should now include a volumeMount and volume section
+     * The output should now include two environment variables referencing K8s secret
 
        ```
-apiVersion: kubeflow.org/v1beta1
-kind: TFJob
-metadata:
-  ...
-spec:
-  tfReplicaSpecs:
-    Chief:
-      ...
-      template:
+        apiVersion: kubeflow.org/v1beta1
+        kind: TFJob
+        metadata:
         ...
         spec:
-          containers:
-          - command:
+          tfReplicaSpecs:
+            Chief:
             ...
-            volumeMounts:
-            - mountPath: /var/secrets
-              name: aws-creds
-              readOnly: true
+            template:
             ...
-          volumes:
-          - name: aws-creds
-            secret:
-              secretName: aws-creds
-       ...
-       ```
+            spec:
+              containers:
+              - command:
+              ...
+                env:
+                ...
+                - name: AWS_ACCESS_KEY_ID
+                  valueFrom:
+                    secretKeyRef:
+                      key: awsAccessKeyID
+                      name: aws-creds
+                - name: AWS_SECRET_ACCESS_KEY
+                  valueFrom:
+                    secretKeyRef:
+                      key: awsSecretAccessKey
+                      name: aws-creds
+                      ...
+      ```
   
   1. Next we need to set a whole bunch of S3 related environment variables so that TensorFlow
      knows how to talk to S3