[Edge AI Suites] Metro and IRD apps: Update all rc1 tags with rc2

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-rc1
+DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2
 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-rc1
+DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2
 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-rc1
+DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2
 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-rc1
+DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2
 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-rc1
+DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2
 RTSP_CAMERA_IP=
 
 # MinIO service & client

manufacturing-ai-suite/industrial-edge-insights-vision/apps/pallet-defect-detection/setup.sh

@@ -3,7 +3,7 @@
 # Download artifacts for a specific sample application
 #   by calling respective app's setup.sh script
 SCRIPT_DIR=$(dirname $(readlink -f "$0"))
-MODEL_URL="https://github.com/open-edge-platform/edge-ai-resources/raw/1d40dfe1791d44e8cf6e8472c28c034e40fa508d/models/INT8/pallet_defect_detection.zip"
+MODEL_URL="https://github.com/open-edge-platform/edge-ai-resources/raw/06bb0d621cb14a1791672552a538beddddcc4066/models/INT8/pallet_defect_detection.zip"
 VIDEO_URL="https://github.com/open-edge-platform/edge-ai-resources/raw/c13b8dbf23d514c2667d39b66615bd1400cb889d/videos/warehouse.avi"
 
 err() {

manufacturing-ai-suite/industrial-edge-insights-vision/apps/worker-safety-gear-detection/configs/pipeline-server-config.json

@@ -5,7 +5,7 @@
                 "name": "worker_safety_gear_detection_mlops",
                 "source": "gstreamer",
                 "queue_maxsize": 50,
-                "pipeline": "multifilesrc loop=TRUE location=/home/pipeline-server/resources/videos/Safety_Full_Hat_and_Vest.avi name=source ! h264parse ! decodebin3 ! gvadetect name=detection ! gvametaconvert add-empty-results=true name=metaconvert ! queue ! gvafpscounter ! appsink name=destination",
+                "pipeline": "multifilesrc loop=TRUE location=/home/pipeline-server/resources/videos/Safety_Full_Hat_and_Vest.avi name=source ! h264parse ! decodebin3 ! gvadetect name=detection threshold=0.4 ! gvametaconvert add-empty-results=true name=metaconvert ! queue ! gvafpscounter ! appsink name=destination",
                 "parameters": {
                     "type": "object",
                     "properties": {
@@ -23,7 +23,7 @@
                 "name": "worker_safety_gear_detection_mqtt",
                 "source": "gstreamer",
                 "queue_maxsize": 50,
-                "pipeline": "{auto_source} name=source ! decodebin3 ! gvadetect name=detection model-instance-id=inst0 ! gvametaconvert add-empty-results=true name=metaconvert ! queue ! gvawatermark displ-cfg=font-scale=1.0,draw-txt-bg=false ! queue ! gvafpscounter ! queue ! jpegenc ! appsink name=destination",
+                "pipeline": "{auto_source} name=source ! decodebin3 ! gvadetect name=detection model-instance-id=inst0 threshold=0.4 ! gvametaconvert add-empty-results=true name=metaconvert ! queue ! gvawatermark displ-cfg=font-scale=1.0,draw-txt-bg=false ! queue ! gvafpscounter ! queue ! jpegenc ! appsink name=destination",
                 "parameters": {
                     "type": "object",
                     "properties": {
@@ -41,7 +41,7 @@
                 "name": "worker_safety_gear_detection_s3write",
                 "source": "gstreamer",
                 "queue_maxsize": 50,
-                "pipeline": "{auto_source} name=source ! decodebin3 ! gvadetect name=detection model-instance-id=inst0 ! gvametaconvert add-empty-results=true name=metaconvert ! queue ! gvafpscounter ! queue ! jpegenc ! appsink name=destination",
+                "pipeline": "{auto_source} name=source ! decodebin3 ! gvadetect name=detection model-instance-id=inst0 threshold=0.4 ! gvametaconvert add-empty-results=true name=metaconvert ! queue ! gvafpscounter ! queue ! jpegenc ! appsink name=destination",
                 "parameters": {
                     "type": "object",
                     "properties": {
@@ -64,7 +64,7 @@
                 "name": "worker_safety_gear_detection_opcua",
                 "source": "gstreamer",
                 "queue_maxsize": 50,
-                "pipeline": "{auto_source} name=source ! decodebin3 ! gvadetect name=detection model-instance-id=inst0 ! gvametaconvert add-empty-results=true name=metaconvert ! queue ! gvawatermark displ-cfg=font-scale=1.0,draw-txt-bg=false ! queue ! gvafpscounter ! queue ! jpegenc ! appsink name=destination",
+                "pipeline": "{auto_source} name=source ! decodebin3 ! gvadetect name=detection model-instance-id=inst0 threshold=0.4 ! gvametaconvert add-empty-results=true name=metaconvert ! queue ! gvawatermark displ-cfg=font-scale=1.0,draw-txt-bg=false ! queue ! gvafpscounter ! queue ! jpegenc ! appsink name=destination",
                 "parameters": {
                     "type": "object",
                     "properties": {
@@ -86,7 +86,7 @@
                 "name": "worker_safety_gear_detection_gpu",
                 "source": "gstreamer",
                 "queue_maxsize": 50,
-                "pipeline": "{auto_source} name=source ! decodebin3 ! gvadetect device=GPU model-instance-id=instgpu0 inference-region=full-frame inference-interval=1 batch-size=8 nireq=2 ie-config=\"GPU_THROUGHPUT_STREAMS=2\" threshold=0.7 name=detection ! queue ! gvametaconvert add-empty-results=true name=metaconvert ! queue ! gvafpscounter ! appsink name=destination",
+                "pipeline": "{auto_source} name=source ! decodebin3 ! gvadetect device=GPU model-instance-id=instgpu0 inference-region=full-frame inference-interval=1 batch-size=8 nireq=2 ie-config=\"GPU_THROUGHPUT_STREAMS=2\" threshold=0.4 name=detection ! queue ! gvametaconvert add-empty-results=true name=metaconvert ! queue ! gvafpscounter ! appsink name=destination",
                 "parameters": {
                     "type": "object",
                     "properties": {
@@ -104,7 +104,7 @@
                 "name": "worker_safety_gear_detection_npu",
                 "source": "gstreamer",
                 "queue_maxsize": 50,
-                "pipeline": "{auto_source} name=source ! decodebin3 ! gvadetect device=NPU model-instance-id=instnpu0 inference-region=full-frame inference-interval=1 batch-size=1 nireq=4 threshold=0.7 name=detection ! queue ! gvametaconvert add-empty-results=true name=metaconvert ! queue ! gvafpscounter ! appsink name=destination",
+                "pipeline": "{auto_source} name=source ! decodebin3 ! gvadetect device=NPU model-instance-id=instnpu0 inference-region=full-frame inference-interval=1 batch-size=1 nireq=4 threshold=0.4 name=detection ! queue ! gvametaconvert add-empty-results=true name=metaconvert ! queue ! gvafpscounter ! appsink name=destination",
                 "parameters": {
                     "type": "object",
                     "properties": {
@@ -122,7 +122,7 @@
                 "name": "worker_safety_gear_detection",
                 "source": "gstreamer",
                 "queue_maxsize": 50,
-                "pipeline": "{auto_source} name=source ! decodebin3 ! gvadetect device=CPU model-instance-id=inst0 name=detection ! queue ! gvametaconvert add-empty-results=true name=metaconvert ! queue ! gvafpscounter ! appsink name=destination",
+                "pipeline": "{auto_source} name=source ! decodebin3 ! gvadetect device=CPU model-instance-id=inst0 threshold=0.4 name=detection ! queue ! gvametaconvert add-empty-results=true name=metaconvert ! queue ! gvafpscounter ! appsink name=destination",
                 "parameters": {
                     "type": "object",
                     "properties": {

manufacturing-ai-suite/industrial-edge-insights-vision/apps/worker-safety-gear-detection/setup.sh

@@ -3,7 +3,7 @@
 # Download artifacts for a specific sample application
 #   by calling respective app's setup.sh script
 SCRIPT_DIR=$(dirname $(readlink -f "$0"))
-MODEL_URL="https://github.com/open-edge-platform/edge-ai-resources/raw/1d40dfe1791d44e8cf6e8472c28c034e40fa508d/models/INT8/worker-safety-gear-detection.zip"
+MODEL_URL="https://github.com/open-edge-platform/edge-ai-resources/raw/06bb0d621cb14a1791672552a538beddddcc4066/models/INT8/worker-safety-gear-detection.zip"
 VIDEO_URL="https://github.com/open-edge-platform/edge-ai-resources/raw/edd25f37c324a9ef73df1642354b2ba5fa7b7df5/videos/Safety_Full_Hat_and_Vest.avi"
 
 err() {

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

@@ -58,6 +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.
 
 4. Install pre-requisites for all instances
 
@@ -806,7 +807,7 @@ Applications can take advantage of S3 publish feature from DL Streamer Pipeline
    >NOTE- For sake of simplicity, we assume that the new model has already been downloaded by Model Download microservice. The following curl command is only a simulation that just downloads the model. In production, however, they will be downloaded by the Model Download service.
 
    ```sh
-   export MODEL_URL='https://github.com/open-edge-platform/edge-ai-resources/raw/a7c9522f5f936c47de8922046db7d7add13f93a0/models/INT8/pallet_defect_detection.zip'
+   export MODEL_URL='https://github.com/open-edge-platform/edge-ai-resources/raw/06bb0d621cb14a1791672552a538beddddcc4066/models/INT8/pallet_defect_detection.zip'
 
    curl -L "$MODEL_URL" -o "$(basename $MODEL_URL)"
 

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

@@ -23,6 +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.
 
 3. Optional: Pull the helm chart and replace the existing helm folder with it
 
@@ -32,13 +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-rc1
+     helm pull oci://registry-1.docker.io/intel/pallet-defect-detection-reference-implementation --version 2.6.0-rc2
      ```
 
    - Unzip the package using the following command
 
      ```bash
-     tar -xvf pallet-defect-detection-reference-implementation-2.6.0-rc1.tgz
+     tar -xvf pallet-defect-detection-reference-implementation-2.6.0-rc2.tgz
      ```
 
    - Replace the helm directory
@@ -408,7 +409,7 @@ Applications can take advantage of S3 publish feature from DL Streamer Pipeline
    >NOTE- For sake of simplicity, we assume that the new model has already been downloaded by Model Download microservice. The following curl command is only a simulation that just downloads the model. In production, however, they will be downloaded by the Model Download service.
 
    ```sh
-   export MODEL_URL='https://github.com/open-edge-platform/edge-ai-resources/raw/a7c9522f5f936c47de8922046db7d7add13f93a0/models/INT8/pallet_defect_detection.zip'
+   export MODEL_URL='https://github.com/open-edge-platform/edge-ai-resources/raw/06bb0d621cb14a1791672552a538beddddcc4066/models/INT8/pallet_defect_detection.zip'
 
    curl -L "$MODEL_URL" -o "$(basename $MODEL_URL)"
 

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-rc1
+FROM intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2
 
 USER root
 

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-rc1
+FROM intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2
 
 USER root
 

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

@@ -58,6 +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.
 
 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

@@ -23,17 +23,18 @@
    ```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.
 
 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`
 
    - Download helm chart with the following command
 
-       `helm pull oci://registry-1.docker.io/intel/pcb-anomaly-detection --version 1.2.0-rc1`
+       `helm pull oci://registry-1.docker.io/intel/pcb-anomaly-detection --version 1.2.0-rc2`
    - unzip the package using the following command
 
-       `tar -xvf pcb-anomaly-detection-1.2.0-rc1.tgz`
+       `tar -xvf pcb-anomaly-detection-1.2.0-rc2.tgz`
    - Replace the helm directory
 
        `rm -rf helm && mv pcb-anomaly-detection helm`

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

@@ -58,6 +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.
 
 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

@@ -23,16 +23,17 @@
    ```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.
 
 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`
 
     - Download helm chart with the following command
 
-        `helm pull oci://registry-1.docker.io/intel/weld-porosity-sample-application --version 1.4.0-rc1`
+        `helm pull oci://registry-1.docker.io/intel/weld-porosity-sample-application --version 1.4.0-rc2`
     - unzip the package using the following command
 
-        `tar -xvf weld-porosity-sample-application-1.4.0-rc1.tgz`
+        `tar -xvf weld-porosity-sample-application-1.4.0-rc2.tgz`
     - Replace the helm directory
 
         `rm -rf helm && mv weld-porosity-sample-application helm`

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

@@ -58,6 +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.
 
 4. Install pre-requisites for all instances
 
@@ -820,7 +821,7 @@ Applications can take advantage of S3 publish feature from DL Streamer Pipeline
    >NOTE- For sake of simplicity, we assume that the new model has already been downloaded by Model Download microservice. The following curl command is only a simulation that just downloads the model. In production, however, they will be downloaded by the Model Download service.
 
    ```sh
-    export MODEL_URL='https://github.com/open-edge-platform/edge-ai-resources/raw/a7c9522f5f936c47de8922046db7d7add13f93a0/models/INT8/worker-safety-gear-detection.zip'
+    export MODEL_URL='https://github.com/open-edge-platform/edge-ai-resources/raw/06bb0d621cb14a1791672552a538beddddcc4066/models/INT8/worker-safety-gear-detection.zip'
 
     curl -L "$MODEL_URL" -o "$(basename $MODEL_URL)"
 

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

@@ -21,15 +21,17 @@
     ```sh
     cp helm/values_worker-safety-gear-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.
+
 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`
 
     - Download helm chart with the following command
 
-        `helm pull oci://registry-1.docker.io/intel/worker-safety-gear-detection --version 1.2.0-rc1`
+        `helm pull oci://registry-1.docker.io/intel/worker-safety-gear-detection --version 1.2.0-rc2`
     - unzip the package using the following command
 
-        `tar -xvf worker-safety-gear-detection-1.2.0-rc1.tgz`
+        `tar -xvf worker-safety-gear-detection-1.2.0-rc2.tgz`
     - Replace the helm directory
 
         `rm -rf helm && mv worker-safety-gear-detection helm`
@@ -392,7 +394,7 @@ Applications can take advantage of S3 publish feature from DL Streamer Pipeline
    >NOTE- For sake of simplicity, we assume that the new model has already been downloaded by Model Download microservice. The following curl command is only a simulation that just downloads the model. In production, however, they will be downloaded by the Model Download service.
 
    ```sh
-   export MODEL_URL='https://github.com/open-edge-platform/edge-ai-resources/raw/a7c9522f5f936c47de8922046db7d7add13f93a0/models/INT8/worker-safety-gear-detection.zip'
+   export MODEL_URL='https://github.com/open-edge-platform/edge-ai-resources/raw/06bb0d621cb14a1791672552a538beddddcc4066/models/INT8/worker-safety-gear-detection.zip'
 
    curl -L "$MODEL_URL" -o "$(basename $MODEL_URL)"
 

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

@@ -29,7 +29,7 @@ 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-rc1
+FROM intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2
 
 USER root
 

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

@@ -27,7 +27,7 @@ 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-rc1
+FROM intel/dlstreamer-pipeline-server:2026.0.0-ubuntu24-rc2
 
 USER root
 

manufacturing-ai-suite/industrial-edge-insights-vision/helm/Chart-pallet-defect-detection.yaml

@@ -17,10 +17,10 @@ type: application
 # This is the chart version. This version number should be incremented each time you make changes
 # to the chart and its templates, including the app version.
 # Versions are expected to follow Semantic Versioning (https://semver.org/)
-version: 2.6.0-rc1
+version: 2.6.0-rc2
 
 # This is the version number of the application being deployed. This version number should be
 # incremented each time you make changes to the application. Versions are not expected to
 # follow Semantic Versioning. They should reflect the version the application is using.
 # It is recommended to use it with quotes.
-appVersion: "2.6.0-rc1"
+appVersion: "2.6.0-rc2"