Merge branch 'main' into update_roboticsstructure

Author: achamuah

manufacturing-ai-suite/industrial-edge-insights-vision/.env

@@ -4,7 +4,7 @@ HOST_IP=
 REST_SERVER_PORT=8080
 
 # DL Streamer Pipeline Server
-DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2
+DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc3
 RTSP_CAMERA_IP=
 
 # MinIO service & client

manufacturing-ai-suite/industrial-edge-insights-vision/.env_pallet-defect-detection

@@ -4,7 +4,7 @@ HOST_IP=
 REST_SERVER_PORT=8080
 
 # DL Streamer Pipeline Server
-DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2
+DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc3
 RTSP_CAMERA_IP=
 
 # MinIO service & client

manufacturing-ai-suite/industrial-edge-insights-vision/.env_pcb-anomaly-detection

@@ -4,7 +4,7 @@ HOST_IP=
 REST_SERVER_PORT=8080
 
 # DL Streamer Pipeline Server
-DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2
+DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc3
 RTSP_CAMERA_IP=
 
 # MinIO service & client

manufacturing-ai-suite/industrial-edge-insights-vision/.env_weld-porosity

@@ -4,7 +4,7 @@ HOST_IP=
 REST_SERVER_PORT=8080
 
 # DL Streamer Pipeline Server
-DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2
+DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc3
 RTSP_CAMERA_IP=
 
 # MinIO service & client

manufacturing-ai-suite/industrial-edge-insights-vision/.env_worker-safety-gear-detection

@@ -4,7 +4,7 @@ HOST_IP=
 REST_SERVER_PORT=8080
 
 # DL Streamer Pipeline Server
-DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2
+DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc3
 RTSP_CAMERA_IP=
 
 # MinIO service & client

manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/get-started/deploy-with-helm.md

@@ -33,13 +33,13 @@
    - Download helm chart with the following command
 
      ```bash
-     helm pull oci://registry-1.docker.io/intel/pallet-defect-detection-reference-implementation --version 2.6.0-rc2
+     helm pull oci://registry-1.docker.io/intel/pallet-defect-detection-reference-implementation --version 2.6.0-rc3
      ```
 
    - Unzip the package using the following command
 
      ```bash
-     tar -xvf pallet-defect-detection-reference-implementation-2.6.0-rc2.tgz
+     tar -xvf pallet-defect-detection-reference-implementation-2.6.0-rc3.tgz
      ```
 
    - Replace the helm directory
@@ -171,6 +171,21 @@
    ```
 
    > **Note:**- This would start the pipeline. You can view the inference stream on WebRTC by opening a browser and navigating to https://<HOST_IP>:30443/mediamtx/pdd/ for Pallet Defect Detection. If you're running helm using an `NGINX_HTTPS_PORT` other than the default 30443, replace `<HOST_IP>` with `<HOST_IP>:<NGINX_HTTPS_PORT>`.
+   
+   ### Starting GPU and NPU based pipelines
+   For GPU and NPU based pipelines, ensure you have done the necessary [setup](../how-to-guides/use-gpu-for-inference.md#deploying-with-helm) from here, and start the respective pipelines as following.
+
+      **For GPU-based pipelines:**
+
+      ```sh
+      ./sample_start.sh helm -p pallet_defect_detection_gpu
+      ```
+
+      **For NPU-based pipelines:**
+
+      ```sh
+      ./sample_start.sh helm -p pallet_defect_detection_npu
+      ```
 
 5. Get status of pipeline instance(s) running.
 

manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/how-to-guides/integrate-balluff-sdk.md

@@ -24,7 +24,7 @@ cd edge-ai-libraries/microservices/dlstreamer-pipeline-server
 Create a Docker file named `BalluffDockerfile` inside your `dlstreamer-pipeline-server` directory with the following content.
 
 ```dockerfile
-FROM intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2
+FROM intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc3
 
 USER root
 
@@ -41,7 +41,7 @@ RUN chmod +x /home/pipeline-server/src-gst-gencamsrc/autogen.sh
 RUN chmod +x /home/pipeline-server/install_gencamsrc_gstreamer_plugin.sh
 RUN /home/pipeline-server/install_gencamsrc_gstreamer_plugin.sh
 
-# For Ubuntu24 intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2 base image
+# For Ubuntu24 intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc3 base image
 RUN apt-get update && apt-get install -y libwxgtk-webview3.2-dev
 
 # For Ubuntu 22 with intel/dlstreamer-pipeline-server:3.1.0-ubuntu22, uncomment the line below and comment the above line
@@ -67,7 +67,7 @@ USER intelmicroserviceuser
 Run the following command to build the image:
 
 ```bash
-docker build -t intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2-gencamsrc-balluff -f BalluffDockerfile .
+docker build -t intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc3-gencamsrc-balluff -f BalluffDockerfile .
 ```
 
 This command builds your Docker image using the steps defined above.
@@ -76,7 +76,7 @@ This command builds your Docker image using the steps defined above.
 
 After the build completes, update .env and start the container:
 
-> update .env DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2-gencamsrc-balluff
+> update .env DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc3-gencamsrc-balluff
 
 ```bash
 docker compose up -d
@@ -111,7 +111,7 @@ cp .env_pallet-defect-detection .env
 Update the `.env` file with the newly created image as below and modify any other required variables.
 
 ```bash
-DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2-gencamsrc-balluff
+DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc3-gencamsrc-balluff
 ```
 
 

manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/how-to-guides/integrate-pylon-sdk.md

@@ -22,7 +22,7 @@ cd edge-ai-libraries/microservices/dlstreamer-pipeline-server
 Create a Docker file named `BaslerDockerfile` inside your `dlstreamer-pipeline-server` directory with the following content.
 
 ```dockerfile
-FROM intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2
+FROM intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc3
 
 USER root
 
@@ -53,7 +53,7 @@ USER intelmicroserviceuser
 Run the following command to build the image:
 
 ```bash
-docker build -t intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2-gencamsrc-basler -f BaslerDockerfile .
+docker build -t intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc3-gencamsrc-basler -f BaslerDockerfile .
 ```
 
 This command builds your Docker image using the steps defined above.
@@ -62,7 +62,7 @@ This command builds your Docker image using the steps defined above.
 
 After the build completes, update .env and start the container:
 
-> update .env DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2-gencamsrc-basler
+> update .env DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc3-gencamsrc-basler
 
 ```bash
 docker compose up -d
@@ -97,7 +97,7 @@ cp .env_pallet-defect-detection .env
 Update the `.env` file with the newly created image as below and modify any other required variables.
 
 ```bash
-DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2-gencamsrc-basler
+DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc3-gencamsrc-basler
 ```
 
 ### Step 3: Run the Setup Script

manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pcb-anomaly-detection/get-started/deploy-with-helm.md

@@ -30,10 +30,10 @@
 
    - Download helm chart with the following command
 
-       `helm pull oci://registry-1.docker.io/intel/pcb-anomaly-detection --version 1.2.0-rc2`
+       `helm pull oci://registry-1.docker.io/intel/pcb-anomaly-detection --version 1.2.0-rc3`
    - unzip the package using the following command
 
-       `tar -xvf pcb-anomaly-detection-1.2.0-rc2.tgz`
+       `tar -xvf pcb-anomaly-detection-1.2.0-rc3.tgz`
    - Replace the helm directory
 
        `rm -rf helm && mv pcb-anomaly-detection helm`
@@ -163,6 +163,21 @@
    > **Note:** This would start the pipeline. You can view the inference stream on WebRTC by
    > opening a browser and navigating to `https://<HOST_IP>:30443/mediamtx/anomaly/` for PCB Anomaly Detection.
    > If you're running helm using an NGINX_HTTPS_PORT other than the default 30443, replace 30443 with <NGINX_HTTPS_PORT>.
+   
+   ### Starting GPU and NPU based pipelines
+   For GPU and NPU based pipelines, ensure you have done the necessary [setup](../how-to-guides/use-gpu-for-inference.md#deploying-with-helm) from here, and start the respective pipelines as following.
+   
+      **For GPU-based pipelines:**
+
+      ```sh
+      ./sample_start.sh helm -p pcb_anomaly_detection_gpu
+      ```
+
+      **For NPU-based pipelines:**
+
+      ```sh
+      ./sample_start.sh helm -p pcb_anomaly_detection_npu
+      ```
 
 5. Get status of pipeline instance(s) running.
 

manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/weld-porosity/get-started/deploy-with-helm.md

@@ -30,10 +30,10 @@
 
     - Download helm chart with the following command
 
-        `helm pull oci://registry-1.docker.io/intel/weld-porosity-sample-application --version 1.4.0-rc2`
+        `helm pull oci://registry-1.docker.io/intel/weld-porosity-sample-application --version 1.4.0-rc3`
     - unzip the package using the following command
 
-        `tar -xvf weld-porosity-sample-application-1.4.0-rc2.tgz`
+        `tar -xvf weld-porosity-sample-application-1.4.0-rc3.tgz`
     - Replace the helm directory
 
         `rm -rf helm && mv weld-porosity-sample-application helm`
@@ -161,6 +161,21 @@
 
    > **Note:**- This would start the pipeline. You can view the inference stream on WebRTC by opening a browser and navigating to https://<HOST_IP>:30443/mediamtx/weld/
    >If you're running helm using an `NGINX_HTTPS_PORT` other than the default 30443, replace `<HOST_IP>` with `<HOST_IP>:<NGINX_HTTPS_PORT>`.
+   
+   ### Starting GPU and NPU based pipelines
+   For GPU and NPU based pipelines, ensure you have done the necessary [setup](../how-to-guides/use-gpu-for-inference.md#deploying-with-helm) from here, and start the respective pipelines as following.
+
+      **For GPU-based pipelines:**
+
+      ```sh
+      ./sample_start.sh helm -p weld_porosity_classification_gpu
+      ```
+
+      **For NPU-based pipelines:**
+
+      ```sh
+      ./sample_start.sh helm -p weld_porosity_classification_npu
+      ```
 
 5. Get status of pipeline instance(s) running.