Document bug fixes for main branch

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

@@ -58,7 +58,7 @@
    MTX_WEBRTCICESERVERS2_0_USERNAME=<username>  # WebRTC credentials e.g. intel1234
    MTX_WEBRTCICESERVERS2_0_PASSWORD=<password>
    ```
-    > **Note:** For GPU-based pipelines, set `privileged_access_required: true` in the `helm/values_<SAMPLE_APP>.yaml` file to enable access to host hardware devices.
+    > **Note:** For GPU/NPU based pipelines, set `privileged_access_required: true` in the `helm/values_<SAMPLE_APP>.yaml` file to enable access to host hardware devices.
 
 4. Install pre-requisites for all instances
 

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

@@ -23,7 +23,7 @@
    ```sh
    cp helm/values_pallet-defect-detection.yaml helm/values.yaml
    ```
-      > **Note:** For GPU-based pipelines, set `privileged_access_required: true` in the `helm/values.yaml` file to enable access to host hardware devices.
+      > **Note:** For GPU/NPU based pipelines, set `privileged_access_required: true` in the `helm/values.yaml` file to enable access to host hardware devices.
 
 3. Optional: Pull the helm chart and replace the existing helm folder with it
 

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

@@ -6,6 +6,7 @@ This section collects guides for Pallet Defect Detection sample application.
 - [Manage pipelines](./how-to-guides/manage-pipelines.md)
 - [Run multiple AI pipelines](./how-to-guides/run-multiple-ai-pipelines.md)
 - [Use GPU For Inference](./how-to-guides/use-gpu-for-inference.md)
+- [Use NPU For Inference](./how-to-guides/use-npu-for-inference.md)
 - [Use Your AI Model and Video](./how-to-guides/use-your-ai-model-and-video.md)
 - [Change the Input Video Source](./how-to-guides/change-input-video-source.md)
 - [Scale Video Resolution](./how-to-guides/scale-video-resolution.md)
@@ -27,6 +28,7 @@ This section collects guides for Pallet Defect Detection sample application.
 ./how-to-guides/manage-pipelines
 ./how-to-guides/run-multiple-ai-pipelines
 ./how-to-guides/use-gpu-for-inference
+./how-to-guides/use-npu-for-inference
 ./how-to-guides/use-your-ai-model-and-video
 ./how-to-guides/change-input-video-source
 ./how-to-guides/scale-video-resolution

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

@@ -95,15 +95,15 @@ If not available, you can simulate this by downloading the sample model from edg
 
    Note the instance-id of the pipeline launched.
 
-8. Verify the pipeline is running. You can View the WebRTC streaming on `http://<HOST_IP>/mediamtx/<peer-str-id>` by replacing `<peer-str-id>` with the value used in the original cURL command to start the pipeline.
+8. Verify the pipeline is running. You can View the WebRTC streaming on `https://<HOST_IP>/mediamtx/<peer-str-id>` by replacing `<peer-str-id>` with the value used in the original cURL command to start the pipeline.
 
    ![WebRTC streaming](../_assets/webrtc-streaming.png)
 
    ### Downloading model with Model Download
 
    At this point, user would like to restart the pipeline with a newer model. The new model can be a retrained version of the existing model or a different model altogether. We use the [Model Download](https://github.com/open-edge-platform/edge-ai-libraries/blob/main/microservices/model-download/docs/user-guide/index.md) microservice to help download the model. It supports downloading public models as well as Geti models from a running Geti server. To learn more about the microservice, see how to [get started with it](https://github.com/open-edge-platform/edge-ai-libraries/blob/main/microservices/model-download/docs/user-guide/get-started.md).
 
-   For our demonstration, we will assume the pallet defect detection model has been retrained and is available for downloaded from a Geti server using the Model Download service. Also, the downloaded location is accessible by the dlstreamer pipeline server. In our example, it is `/tmp/tmp-models`. The `/tmp`dir is already accessible by the sample application. If not, please add it to the `volumes` section of `dlstreamer-pipeline-server service in docker-compose file.
+   For our demonstration, we will assume the pallet defect detection model has been retrained and is available for downloaded from a Geti server using the Model Download service. Also, the downloaded location is accessible by the dlstreamer pipeline server. In our example, it is `/tmp/models`. The `/tmp`dir is already accessible by the sample application. If not, please add it to the `volumes` section of `dlstreamer-pipeline-server service in docker-compose file.
 
 9. Stop the running pipeline by using the pipeline instance "id".
 
@@ -143,7 +143,7 @@ If not available, you can simulate this by downloading the sample model from edg
     ./sample_start.sh -p pallet_defect_detection_mlops
     ```
 
-11. View the WebRTC streaming on `http://<HOST_IP>/mediamtx/<peer-str-id>` by replacing `<peer-str-id>` with the value used in the original cURL command to start the pipeline.
+11. View the WebRTC streaming on `https://<HOST_IP>/mediamtx/<peer-str-id>` by replacing `<peer-str-id>` with the value used in the original cURL command to start the pipeline.
 
 ## Additional resources
 

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

@@ -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:3.1.0-ubuntu24 base image
+# For Ubuntu24 intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2 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:3.1.0-ubuntu24-gencamsrc-balluff -f BalluffDockerfile .
+docker build -t intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2-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:3.1.0-ubuntu24-gencamsrc-balluff
+> update .env DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2-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:3.1.0-ubuntu24-gencamsrc-balluff
+DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2-gencamsrc-balluff
 ```
 
 

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

@@ -53,7 +53,7 @@ USER intelmicroserviceuser
 Run the following command to build the image:
 
 ```bash
-docker build -t intel/dlstreamer-pipeline-server:3.1.0-ubuntu24-gencamsrc-basler -f BaslerDockerfile .
+docker build -t intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2-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:3.1.0-ubuntu24-gencamsrc-basler
+> update .env DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2-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:3.1.0-ubuntu24-gencamsrc-basler
+DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2-gencamsrc-basler
 ```
 
 ### Step 3: Run the Setup Script

manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/how-to-guides/use-npu-for-inference.md

@@ -0,0 +1,98 @@
+# How to use NPU for inference
+
+## Pre-requisites
+
+To take full advantage of hardware acceleration, pipelines can be designed so that different stages—such as decoding and inference—are executed on the most suitable hardware devices.
+
+Low-power accelerators like a Neural Processing Unit (NPU) can offload neural network computation from the CPU or GPU, enabling more efficient resource utilization and improved overall system performance.
+
+DLStreamer and the DLStreamer Pipeline Server support inference on NPU devices, allowing applications built on these frameworks to leverage NPU acceleration for improved efficiency and performance.
+
+Before running inference on an NPU, ensure that:
+- The host system includes a supported NPU device
+- The required NPU drivers are installed and properly configured
+
+For detailed setup instructions, refer to the [documentation](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/dev_guide/advanced_install/advanced_install_guide_prerequisites.html#optional-prerequisite-2-install-intel-npu-drivers).
+
+ For containerized application, following additional changes are required.
+
+### Provide NPU access to the container
+
+This can be done by making the following changes to the docker compose file.
+
+```yaml
+services:
+  dlstreamer-pipeline-server:
+    group_add:
+      # render group ID for ubuntu 22.04 host OS
+      - "110"
+      # render group ID for ubuntu 24.04 host OS
+      - "992"
+    devices:
+      # you can add specific devices in case you don't want to provide access to all like below.
+      - "/dev:/dev"
+```
+The changes above adds the container user to the `render` group and provides access to the NPU devices.
+
+### Hardware specific encoder/decoders
+
+Unlike the changes done for the container above, the following requires a modification to the media pipeline itself.
+
+Gstreamer has a variety of hardware specific encoders and decoders elements such as Intel specific VA-API elements that you can benefit from by adding them into your media pipeline. Examples of such elements are `vah264dec`, `vah264enc`, `vajpegdec`, `vajpegdec`, etc.
+
+Additionally, one can also enforce zero-copy of buffers using GStreamer caps (capabilities) to the pipeline by adding `video/x-raw(memory: VAMemory)` for Intel NPUs.
+
+Read DL Streamer [docs](https://dlstreamer.github.io/dev_guide/gpu_device_selection.html) for more details.
+
+### NPU specific element properties
+
+DL Streamer inference elements also provides property such as `device=NPU` and `pre-process-backend=va` which should be used in pipelines with NPU memory. It performs mapping to the system memory and uses VA pre-processor. Read DL Streamer [docs](https://dlstreamer.github.io/dev_guide/model_preparation.html#model-pre-and-post-processing) for more.
+
+## Tutorial on how to use NPU specific pipelines
+
+> Note - This sample application already provides a default `docker-compose.yml` file that includes the necessary NPU access to the containers.
+
+The pipeline `pallet_defect_detection_npu` in `pipeline-server-config.json` contains NPU specific elements and uses NPU backend for inferencing. Follow the steps below to run the pipeline.
+
+### Steps
+
+1. Ensure that the sample application is up and running. If not, follow the steps [here](../get-started.md#set-up-the-application) to setup the application and then bring the services up
+
+    >If you're running multiple instances of app, start the services using `./run.sh up` instead.
+
+    ```sh
+    docker compose up -d
+    ```
+2. Start the pipeline.
+    ```sh
+    ./sample_start.sh -p pallet_defect_detection_npu
+    ```
+
+    This will start the pipeline. The inference stream can be viewed on WebRTC, in a browser, at the following url:
+
+    >If you're running multiple instances of app, ensure to provide `NGINX_HTTPS_PORT` number in the url for the app instance i.e. replace <HOST_IP> with <HOST_IP>:<NGINX_HTTPS_PORT>
+
+    ```bash
+    https://<HOST_IP>/mediamtx/pdd/
+    ```
+
+## Deploying with Helm
+
+### Intel GPU K8S Extension
+
+If you're deploying a NPU based pipeline (example: with VA elements like `vapostproc`, `vah264dec` etc., and/or with `device=NPU` in `gvadetect` in `dlstreamer_pipeline_server_config.json`) with Intel GPU k8s Extension, ensure to set the below details in the file `helm/values.yaml` appropriately in order to utilize the underlying NPU.
+
+```sh
+gpu:
+  enabled: true
+  type: "gpu.intel.com/i915"
+  count: 1
+```
+
+### Without Intel GPU K8S Extension
+
+If you're deploying a NPU based pipeline (example: with VA elements like `vapostproc`, `vah264dec` etc., and/or with `device=NPU` in `gvadetect` in `dlstreamer_pipeline_server_config.json`) without Intel GPU k8s Extension, ensure to set the below details in the file `helm/values.yaml` appropriately in order to utilize the underlying NPU.
+
+```sh
+privileged_access_required: true
+```

manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/release-notes/march-2026.md

@@ -4,9 +4,9 @@
 
 ### Updates in v2.6.0
 
-- Consumed latest DL Streamer Pipeline Server 2026.0.0 image. Ubuntu24 variant of the image is default now.
-- NGINX,COTURN and MINIO ports made configurable as environment variables
 - Added support for simultaneous deployment of multiple applications in the same host via docker and helm
-- Model retrained with Geti v2.13.1
-- Remove Model Registry service and its references
-- MLOps is now demonstrated with Model Download microservice instead of Model Registry
+- MLOps is now demonstrated with Model Download microservice instead of Model Registry
+- Removed Model Registry service and its references
+- NGINX,COTURN and MINIO ports made configurable as environment variables
+- Consumed latest DL Streamer Pipeline Server 2026.0.0 image. Ubuntu24 variant of the image is default now.
+- Model retrained with Geti v2.13.1

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

@@ -58,7 +58,7 @@
    MTX_WEBRTCICESERVERS2_0_USERNAME=<username>  # WebRTC credentials e.g. intel1234
    MTX_WEBRTCICESERVERS2_0_PASSWORD=<password>
    ```
-    > **Note:** For GPU-based pipelines, set `privileged_access_required: true` in the `helm/values_<SAMPLE_APP>.yaml` file to enable access to host hardware devices.
+    > **Note:** For GPU/NPU based pipelines, set `privileged_access_required: true` in the `helm/values_<SAMPLE_APP>.yaml` file to enable access to host hardware devices.
 
 4. Install pre-requisites for all instances
 

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

@@ -23,8 +23,7 @@
    ```sh
    cp helm/values_pcb-anomaly-detection.yaml helm/values.yaml
    ```
-      > **Note:** For GPU-based pipelines, set `privileged_access_required: true` in the `helm/values.yaml` file to enable access to host hardware devices.
-
+      > **Note:** For GPU/NPU based pipelines, set `privileged_access_required: true` in the `helm/values.yaml` file to enable access to host hardware devices.
 3. Optional: Pull the helm chart and replace the existing helm folder with it
 
    - Note: The helm chart should be downloaded when you are not using the helm chart provided in `edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/helm`

manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pcb-anomaly-detection/how-to-guides/enable-mlops.md

@@ -95,15 +95,15 @@ If not available, you can simulate this by downloading the sample model from edg
    ```
    Note the instance-id of the pipeline launched.
 
-8. Verify the pipeline is running. You can View the WebRTC streaming on `http://<HOST_IP>/mediamtx/<peer-str-id>` by replacing `<peer-str-id>` with the value used in the original cURL command to start the pipeline.
+8. Verify the pipeline is running. You can View the WebRTC streaming on `https://<HOST_IP>/mediamtx/<peer-str-id>` by replacing `<peer-str-id>` with the value used in the original cURL command to start the pipeline.
 
    ![WebRTC streaming](../_assets/webrtc-streaming.png)
 
     ### Downloading model with Model Download
 
     At this point, user would like to restart the pipeline with a newer model. The new model can bea retrained version of the existing model or a different model altogether. We use [Model Download](https://github.com/open-edge-platform/edge-ai-libraries/blob/main/microservices/model-download/docs/user-guide/index.md) microservice to help download the model. It supports downloading  public models as well as geti models from a running Geti server. To learn more about it, see [here](https://github.com/open-edge-platform/edge-ai-libraries/blob/main/microservices/model-download/docs/user-guide/get-started.md).
 
-    For our demonstration, we will assume the pcb anomaly model has been retrained and is available for downloaded from a Geti server using the Model Download service. Also, the downloaded location is accessible by the dlstreamer pipeline server. In our example, it is `/tmp/tmp-models`. The `/tmp`dir is already accessible by the sample application. If not, please add it to the `volumes` section of `dlstreamer-pipeline-server service in docker-compose file.
+    For our demonstration, we will assume the pcb anomaly model has been retrained and is available for downloaded from a Geti server using the Model Download service. Also, the downloaded location is accessible by the dlstreamer pipeline server. In our example, it is `/tmp/models`. The `/tmp`dir is already accessible by the sample application. If not, please add it to the `volumes` section of `dlstreamer-pipeline-server service in docker-compose file.
 
 9. Stop the running pipeline by using the pipeline instance "id".
 
@@ -142,7 +142,7 @@ If not available, you can simulate this by downloading the sample model from edg
     ./sample_start.sh -p pcb_anomaly_detection_mlops
     ```
 
-11. View the WebRTC streaming on `http://<HOST_IP>/mediamtx/<peer-str-id>` by replacing `<peer-str-id>` with the value used in the original cURL command to start the pipeline.
+11. View the WebRTC streaming on `https://<HOST_IP>/mediamtx/<peer-str-id>` by replacing `<peer-str-id>` with the value used in the original cURL command to start the pipeline.
 
 ## Additional resources
 

manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pcb-anomaly-detection/release-notes/march-2026.md

@@ -4,9 +4,9 @@
 
 ### Updates in v1.2.0
 
-- Consumed latest DL Streamer Pipeline Server 2026.0.0 image. Ubuntu24 variant of the image is default now.
-- NGINX,COTURN and MINIO ports made configurable as environment variables
 - Added support for simultaneous deployment of multiple applications in the same host via docker and helm
-- Model retrained with Geti v2.13.1
-- Remove Model Registry service and its references
-- MLOps is now demonstrated with Model Download microservice instead of Model Registry
+- MLOps is now demonstrated with Model Download microservice instead of Model Registry
+- Removed Model Registry service and its references
+- NGINX,COTURN and MINIO ports made configurable as environment variables
+- Consumed latest DL Streamer Pipeline Server 2026.0.0 image. Ubuntu24 variant of the image is default now.
+- Model retrained with Geti v2.13.1

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

@@ -58,7 +58,7 @@
    MTX_WEBRTCICESERVERS2_0_USERNAME=<username>  # WebRTC credentials e.g. intel1234
    MTX_WEBRTCICESERVERS2_0_PASSWORD=<password>
    ```
-    > **Note:** For GPU-based pipelines, set `privileged_access_required: true` in the `helm/values_<SAMPLE_APP>.yaml` file to enable access to host hardware devices.
+    > **Note:** For GPU/NPU based pipelines, set `privileged_access_required: true` in the `helm/values_<SAMPLE_APP>.yaml` file to enable access to host hardware devices.
 
 4. Install pre-requisites for all instances
 

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

@@ -23,7 +23,7 @@
    ```sh
    cp helm/values_weld-porosity.yaml helm/values.yaml
    ```
-      > **Note:** For GPU-based pipelines, set `privileged_access_required: true` in the `helm/values.yaml` file to enable access to host hardware devices.
+      > **Note:** For GPU/NPU based pipelines, set `privileged_access_required: true` in the `helm/values.yaml` file to enable access to host hardware devices.
 
 3. Optional: Pull the helm chart and replace the existing helm folder with it
     - Note: The helm chart should be downloaded when you are not using the helm chart provided in `edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/helm`