update docs

Author: ajagadi1

microservices/dlstreamer-pipeline-server/docs/user-guide/how-to-deploy-with-helm.md

@@ -56,15 +56,7 @@ Update the below fields in `values.yaml` file in the helm chart
 ### Run default sample
 
 Once the pods are up, we will send a pipeline request to DL Streamer Pipeline Server to run a detection model on a warehouse video.
-
-Copy the resources such as video and model from local directory to the to the `dlstreamer-pipeline-server` pod to make them available for launching pipeline.
-```sh
-POD_NAME=$(kubectl get pods -n apps -o jsonpath='{.items[*].metadata.name}' | tr ' ' '\n' | grep dlstreamer-pipeline-server | head -n 1) 
-
-kubectl cp <edge-ai-libraries/microservices/dlstreamer-pipeline-server>/resources/models/geti/ $POD_NAME:/home/pipeline-server/resources/models/geti/ -c dlstreamer-pipeline-server -n apps
-
-kubectl cp <edge-ai-libraries/microservices/dlstreamer-pipeline-server>/resources/videos/warehouse.avi $POD_NAME:/home/pipeline-server/resources/videos/ -c dlstreamer-pipeline-server -n apps
-```
+The resources such as video and model are copied into `dlstreamer-pipeline-server` pod by `initContainers`.
 
 We will send the below curl request to run the inference.
 It comprises of a source file path which is `warehouse.avi`, a destination, with metadata directed to a json fine in `/tmp/resuts.jsonl` and frames streamed over RTSP with id `pallet_defect_detection`. Additionally, we will also provide the GETi model path that would be used for detecting defective boxes on the video file.

microservices/dlstreamer-pipeline-server/helm/README.md

@@ -14,14 +14,7 @@
     `helm install dlsps . -n apps --create-namespace`
 - Check if Deep Learning Streamer Pipeline Server is running fine
     `kubectl get pods --namespace apps`and monitor its logs using `kubectl logs -f <pod_name> -n apps`
-- Copy the resources such as video and model from local directory to the to the `dlstreamer-pipeline-server` pod to make them available for launching pipeline.
-    ```sh
-    POD_NAME=$(kubectl get pods -n apps -o jsonpath='{.items[*].metadata.name}' | tr ' ' '\n' | grep dlstreamer-pipeline-server | head -n 1) 
-
-    kubectl cp ../resources/models/geti/ $POD_NAME:/home/pipeline-server/resources/models/geti/ -c dlstreamer-pipeline-server -n apps
-
-    kubectl cp ../resources/videos/warehouse.avi $POD_NAME:/home/pipeline-server/resources/videos/ -c dlstreamer-pipeline-server -n apps
-    ```
+- The resources such as video and model are copied into `dlstreamer-pipeline-server` pod by `initContainers`.
 - Send the curl command to start the pallet defect detection pipeline
     ``` sh
         curl http://localhost:30007/pipelines/user_defined_pipelines/pallet_defect_detection -X POST -H 'Content-Type: application/json' -d '{