[DL Streamer Pipeline Server] Update helm chart and docs (#632)

Author: ajagadi1

microservices/dlstreamer-pipeline-server/README.md

@@ -123,6 +123,8 @@ Now you have successfully run the Deep Learning Streamer Pipeline Server contain
 
 You can build either an optimized or an extended DL Streamer Pipeline Server image (for both Ubuntu22 and Ubuntu24) based on your use case. The extended image contains the Geti SDK, the OpenVINO Model API and ROS2 on top of the optimized image.
 
+Note: Ensure to set the right values in the [.env file](./docker/.env) for building DL Streamer Pipeline Server optimized image and DL Streamer Pipeline Server extended image when you follow the below steps. The mentioned file has the necessary details written as comments.
+
 ### Prerequisites
 Add the following lines in [.env file](./docker/.env) if you are behind a proxy.
 
@@ -132,7 +134,7 @@ Add the following lines in [.env file](./docker/.env) if you are behind a proxy.
   no_proxy= # example: no_proxy=localhost,127.0.0.1
   ```
 
-Update the following lines in [.env file](./docker/.env) for choosing the right base image and also for naming the image that gets built.
+Update the following lines in [.env file](./docker/.env) for choosing the right base and target images and also for naming the image that gets built.
 
   ``` sh
   # See .env file for example values
@@ -157,7 +159,7 @@ Run the following commands:
      docker compose build
    ```
 
-   The docker image `intel/dlstreamer-pipeline-server:3.1.0-ubuntu22`, `intel/dlstreamer-pipeline-server:3.1.0-ubuntu24`, `intel/dlstreamer-pipeline-server:3.1.0-extended-ubuntu22` or `intel/dlstreamer-pipeline-server:3.1.0-extended-ubuntu24` is now built (based on the .env changes done above) and available for you to run.
+   The docker image of DL Streamer Pipeline Server is now built (based on the .env changes done above) and available for you to run.
 
    ```sh
      docker compose up

microservices/dlstreamer-pipeline-server/docs/user-guide/how-to-build-from-source.md

@@ -1,6 +1,8 @@
 # How to build from source
 
-You can build either an optimized or an extended DL Streamer Pipeline Server image (for both Ubuntu22 and Ubuntu24) based on your use case. The extended image contains the Geti SDK, the OpenVINO Model API and ROS2 on top of the optimized image.
+You can build either an optimized or an extended DL Streamer Pipeline Server image (for both Ubuntu22 and Ubuntu24) based on your use case. The extended image contains the Geti SDK, the OpenVINO Model API and ROS2 on top of the optimized image. 
+
+Note: Ensure to set the right values in the `[WORKDIR]/edge-ai-libraries/microservices/dlstreamer-pipeline-server/docker/.env` file for building DL Streamer Pipeline Server optimized image and DL Streamer Pipeline Server extended image when you follow the below steps. The mentioned file has the necessary details written as comments.
 
 ## Steps
 
@@ -21,7 +23,7 @@ You can build either an optimized or an extended DL Streamer Pipeline Server ima
     no_proxy= # example: no_proxy=localhost,127.0.0.1
     ```
 
-3. Update the following lines in `[WORKDIR]/edge-ai-libraries/microservices/dlstreamer-pipeline-server/docker/.env` for choosing the right base image and also for naming the image that gets built.
+3. Update the following lines in `[WORKDIR]/edge-ai-libraries/microservices/dlstreamer-pipeline-server/docker/.env` for choosing the right base and target images and also for naming the image that gets built.
 
     ``` sh
     # See .env file for example values
@@ -45,21 +47,12 @@ You can build either an optimized or an extended DL Streamer Pipeline Server ima
     source .env # sometimes this is needed as docker compose doesn't always pick up the necessary env variables
     docker compose build
     ```
----
 
-2. Once the build is complete, list the docker images
-    ```sh
-    docker image ls
-    ```
-Based on the `.env` file changes done above, verify that the appropriate image from the following is present in the system after the build is successful
-- `intel/dlstreamer-pipeline-server:<latest-version-number>-ubuntu22`
-- `intel/dlstreamer-pipeline-server:<latest-version-number>-ubuntu24`
-- `intel/dlstreamer-pipeline-server:<latest-version-number>-extended-ubuntu22`
-- `intel/dlstreamer-pipeline-server:<latest-version-number>-extended-ubuntu24`
+    The docker image of DL Streamer Pipeline Server is now built (based on the .env changes done above) and available for you to run.
 
 ---
 
-3. Run the below command to start the container 
+2. Run the below command to start the container 
     ```sh
     docker compose up
     ```

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

@@ -27,10 +27,10 @@ Follow the steps in this section to quickly pull the latest pre-built DL Streame
 
 - Download helm chart with the following command
 
-    `helm pull oci://registry-1.docker.io/intel/dlstreamer-pipeline-server --version 3.1.0-helm`
+    `helm pull oci://ghcr.io/open-edge-platform/edge-ai-libraries/dlstreamer-pipeline-server --version 20250804-EAL1.2-DLSPS-RC4`
 - unzip the package using the following command
 
-    `tar xvf dlstreamer-pipeline-server-3.1.0-helm.tgz`
+    `tar -xvf dlstreamer-pipeline-server-20250804-EAL1.2-DLSPS-RC4.tgz`
 - Get into the helm directory
 
     `cd dlstreamer-pipeline-server`

microservices/dlstreamer-pipeline-server/docs/user-guide/how-to-publish-metadata-over-ros2.md

@@ -4,11 +4,12 @@ DL Streamer Pipeline Server offers publishing the metadata (with or without enco
 
 ## Prerequisite
 
-Ensure to build/pull the DL Streamer Pipeline Server extended image i.e., `intel/dlstreamer-pipeline-server:<version>-extended-ubuntu<ubuntu-version>`.
+Ensure to build/pull the DL Streamer Pipeline Server extended image.
 
 [Build instructions](./how-to-build-from-source.md)
 
-[Pull image](https://hub.docker.com/r/intel/dlstreamer-pipeline-server)
+Pull DL Streamer Pipeline Server extended image from [dockerhub](https://hub.docker.com/r/intel/dlstreamer-pipeline-server) or [ghcr](https://github.com/open-edge-platform/edge-ai-libraries/pkgs/container/edge-ai-libraries%2Fintel%2Fedge-ai-dlstreamer-pipeline-server)
+- Ensure to update the `DLSTREAMER_PIPELINE_SERVER_IMAGE` value in `[WORKDIR]/edge-ai-libraries/microservices/dlstreamer-pipeline-server/docker/.env` file accordingly, in order to run the pulled image.
 
 ## Publish 
 

microservices/dlstreamer-pipeline-server/docs/user-guide/how-to-run-udf-pipelines.md

@@ -2,11 +2,12 @@
 
 ## Prerequisite
 
-Ensure to build/pull the DL Streamer Pipeline Server extended image i.e., `intel/dlstreamer-pipeline-server:<version>-extended-ubuntu<ubuntu-version>`.
+Ensure to build/pull the DL Streamer Pipeline Server extended image.
 
 [Build instructions](./how-to-build-from-source.md)
 
-[Pull image](https://hub.docker.com/r/intel/dlstreamer-pipeline-server)
+Pull DL Streamer Pipeline Server extended image from [dockerhub](https://hub.docker.com/r/intel/dlstreamer-pipeline-server) or [ghcr](https://github.com/open-edge-platform/edge-ai-libraries/pkgs/container/edge-ai-libraries%2Fintel%2Fedge-ai-dlstreamer-pipeline-server)
+- Ensure to update the `DLSTREAMER_PIPELINE_SERVER_IMAGE` value in `[WORKDIR]/edge-ai-libraries/microservices/dlstreamer-pipeline-server/docker/.env` file accordingly, in order to run the pulled image.
 
 ## Steps
 DL Streamer Pipeline Server supports udfloader element which allow user to write an User Defined Function (UDF) that can transform video frames and/or manipulate metadata. You can do this by adding an element called 'udfloader'. You can try simple udfloader pipeline by replacing the following sections in [WORKDIR]/edge-ai-libraries/microservices/dlstreamer-pipeline-server/configs/default/config.json with the following

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

@@ -13,7 +13,7 @@
     images:
         dlstreamer_pipeline_server: # example: dlstreamer_pipeline_server: intel/dlstreamer-pipeline-server:3.1.0-ubuntu22
     ```
-- Install the helm chart
+- Install the helm chart:
     `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`
@@ -45,6 +45,8 @@
         }'
     ```
 - Open VLC media player on your windows system. Cick on Media -> Open Network Stream -> Enter `rtsp://<Host_IP_where_Deep_Learning_Streamer_Pipeline_Server_is_running>:30025/pallet-defect-detection` in network URL tab and hit Play to see the visualization.
+- Uninstall the helm chart:
+    `helm uninstall dlsps -n apps`
 
 ## Troubleshooting
 - [Troubleshooting Guide](../docs/user-guide/troubleshooting-guide.md)