[DL Streamer Pipeline Server] Add init container in helm chart, fix trivy config warnings, add troubleshooting guide and doc updates

microservices/dlstreamer-pipeline-server/README.md

@@ -134,18 +134,13 @@ Add the following lines in [.env file](./docker/.env) if you are behind a proxy.
 Update the following lines in [.env file](./docker/.env) for choosing the right base image and also for naming the image that gets built.
 
   ``` sh
-  # For Ubuntu 22.04: ghcr.io/open-edge-platform/edge-ai-libraries/deb-final-img-ubuntu22:candidate1407
-  # For Ubuntu 24.04: ghcr.io/open-edge-platform/edge-ai-libraries/deb-final-img-ubuntu24:candidate1407
+  # See .env file for example values
   BASE_IMAGE=
 
-  # For Ubuntu 22.04 and optimized image: intel/dlstreamer-pipeline-server:3.1.0-ubuntu22
-  # For Ubuntu 24.04 and optimized image: intel/dlstreamer-pipeline-server:3.1.0-ubuntu24
-  # For Ubuntu 22.04 and extended image: intel/dlstreamer-pipeline-server:3.1.0-extended-ubuntu22
-  # For Ubuntu 24.04 and extended image: intel/dlstreamer-pipeline-server:3.1.0-extended-ubuntu24
+  # See .env file for example values
   DLSTREAMER_PIPELINE_SERVER_IMAGE=
 
-  # For optimized image: dlstreamer-pipeline-server
-  # For extended image: dlstreamer-pipeline-server-extended
+  # See .env file for example values
   BUILD_TARGET=
   ```
 
@@ -184,6 +179,10 @@ Refer [here](https://docs.openedgeplatform.intel.com/edge-ai-libraries/dlstreame
   ```
 ---
 
+## Troubleshooting
+- [Troubleshooting Guide](docs/user-guide/troubleshooting-guide.md)
+
+---
 ## Learn More
 
 -   Understand the components, services, architecture, and data flow, in the [Overview](https://docs.openedgeplatform.intel.com/edge-ai-libraries/dlstreamer-pipeline-server/main/user-guide/Overview.html)

microservices/dlstreamer-pipeline-server/docs/dictionary_append.txt

@@ -238,4 +238,5 @@ ntp
 bitrate
 kbps
 kb
-VA-API
+VA-API
+Troubleshooting

microservices/dlstreamer-pipeline-server/docs/user-guide/advanced-guide/detailed_usage/rest_api/defining_pipelines.md

@@ -450,7 +450,7 @@ Parameters default value in pipeline definitions can be set in section in one of
     }
     ```
 
-1. **Read default value from environment variable**
+2. **Read default value from environment variable**
 
     A default value can be set using environment variable for the element property using `default` key.
 
@@ -475,10 +475,6 @@ Parameters default value in pipeline definitions can be set in section in one of
     }
     ```
 
-    Set `DETECTION_DEVICE` environment variable at Pipeline Server start.
-    ```bash
-    ./docker/run.sh -e DETECTION_DEVICE=GPU
-    ```
 
 #### Parameters and FFmpeg Filters
 

microservices/dlstreamer-pipeline-server/docs/user-guide/get-started.md

@@ -111,96 +111,7 @@ For alternative ways to set up the microservice, see:
 - [How to Deploy with Helm](./how-to-deploy-with-helm.md)
 
 ## Troubleshooting
-
-- **Using REST API in Image Ingestor mode has low first inference latency**
-
-    This is an expected behavior observed only for the first inference. Subsequent inferences would be considerably faster. 
-    For inference on GPU, the first inference might be even slower. Latency for up to 15 seconds have been observed for image requests inference on GPU.
-    When in `sync` mode, we suggest users to provide a `timeout` with a value to accommodate for the first inference latency to avoid request time out. 
-    Read [here](./advanced-guide/detailed_usage/rest_api/restapi_reference_guide.md#post-pipelinesnameversioninstance_id) to learn more about the API.
-
-
-- **Axis RTSP camera freezes or pipeline stops**
-
-    Restart the DL Streamer pipeline server container with the pipeline that has this rtsp source.
-
-
-- **Deploying with Intel GPU K8S Extension on ITEP**
-
-    If you're deploying a GPU based pipeline (example: with VA-API elements like `vapostproc`, `vah264dec` etc., and/or with `device=GPU` in `gvadetect` in `dlstreamer_pipeline_server_config.json`) with Intel GPU k8s Extension on ITEP, ensure to set the below details in the file `helm/values.yaml` appropriately in order to utilize the underlying GPU.
-    ```sh
-    gpu:
-      enabled: true
-      type: "gpu.intel.com/i915"
-      count: 1
-    ```
-
-- **Deploying without Intel GPU K8S Extension**
-
-    If you're deploying a GPU based pipeline (example: with VA-API elements like `vapostproc`, `vah264dec` etc., and/or with `device=GPU` 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 GPU.
-    ```sh
-    privileged_access_required: true
-    ```
-
-- **Using RTSP/WebRTC streaming, S3_write or MQTT fails with GPU elements in pipeline**
-
-    If you are using GPU elements in the pipeline, RTSP/WebRTC streaming, S3_write and MQTT will not work because these are expects CPU buffer. \
-    Add `vapostproc ! video/x-raw` before appsink element or `jpegenc` element(in case you are using S3_write) in the GPU pipeline.
-    ```sh
-    # Sample pipeline
-
-    "pipeline": "{auto_source} name=source ! parsebin ! vah264dec ! vapostproc ! video/x-raw(memory:VAMemory) ! gvadetect name=detection model-instance-id=inst0 ! queue ! gvafpscounter ! gvametaconvert add-empty-results=true name=metaconvert ! gvametapublish name=destination ! vapostproc ! video/x-raw ! appsink name=appsink"
-    ```
-
-- **RTSP streaming fails if you are using udfloader**
-
-    If you are using udfloader<link> pipeline RTSP streaming will not work because RTSP pipeline does not support RGB, BGR or Mono format.
-    If you are using `udfloader pipeline` or `RGB, BGR or GRAY8` format in the pipeline, add  `videoconvert ! video/x-raw, format=(string)NV12` before `appsink` element in pipeline.
-    ```sh
-    # Sample pipeline
-
-    "pipeline": "{auto_source} name=source  ! decodebin ! videoconvert ! video/x-raw,format=RGB ! udfloader name=udfloader ! gvametaconvert add-empty-results=true name=metaconvert ! gvametapublish name=destination ! videoconvert ! video/x-raw, format=(string)NV12 ! appsink name=appsink"
-    ```
-
-- **Resolving Time Sync Issues in Prometheus**
-
-    If you see the following warning in Prometheus, it indicates a time sync issue.
-
-    **Warning: Error fetching server time: Detected xxx.xxx seconds time difference between your browser and the server.**
-
-    You can following the below steps to synchronize system time using NTP.
-    1. **Install systemd-timesyncd** if not already installed:
-        ```bash
-        sudo apt install systemd-timesyncd
-        ```
-
-    2. **Check service status**:
-        ```bash
-        systemctl status systemd-timesyncd
-        ```
-
-    3. **Configure an NTP server** (if behind a corporate proxy):
-        ```bash
-        sudo nano /etc/systemd/timesyncd.conf
-        ```
-        Add:
-        ```ini
-        [Time]
-        NTP=corp.intel.com
-        ```
-        Replace `corp.intel.com` with a different ntp server that is supported on your network.
-
-    4. **Restart the service**:
-        ```bash
-        sudo systemctl restart systemd-timesyncd
-        ```
-
-    5. **Verify the status**:
-        ```bash
-        systemctl status systemd-timesyncd
-        ```
-
-    This should resolve the time discrepancy in Prometheus.
+- [Troubleshooting Guide](./troubleshooting-guide.md)
 
 ## Known Issues
 

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

@@ -24,18 +24,13 @@ You can build either an optimized or an extended DL Streamer Pipeline Server ima
 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.
 
     ``` sh
-    # For Ubuntu 22.04: ghcr.io/open-edge-platform/edge-ai-libraries/deb-final-img-ubuntu22:candidate1407
-    # For Ubuntu 24.04: ghcr.io/open-edge-platform/edge-ai-libraries/deb-final-img-ubuntu24:candidate1407
+    # See .env file for example values
     BASE_IMAGE=
 
-    # For Ubuntu 22.04 and optimized image: intel/dlstreamer-pipeline-server:3.1.0-ubuntu22
-    # For Ubuntu 24.04 and optimized image: intel/dlstreamer-pipeline-server:3.1.0-ubuntu24
-    # For Ubuntu 22.04 and extended image: intel/dlstreamer-pipeline-server:3.1.0-extended-ubuntu22
-    # For Ubuntu 24.04 and extended image: intel/dlstreamer-pipeline-server:3.1.0-extended-ubuntu24
+    # See .env file for example values
     DLSTREAMER_PIPELINE_SERVER_IMAGE=
 
-    # For optimized image: dlstreamer-pipeline-server
-    # For extended image: dlstreamer-pipeline-server-extended
+    # See .env file for example values
     BUILD_TARGET=
     ```
 

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

@@ -10,11 +10,20 @@
   Refer to tutorials such as <https://adamtheautomator.com/install-kubernetes-ubuntu> and many other
   online tutorials to setup kubernetes cluster on the web with host OS as ubuntu 22.04.
 - For helm installation, refer to [helm website](https://helm.sh/docs/intro/install/)
+- Clone the Edge-AI-Libraries repository from open edge platform and change to the docker directory inside DL Streamer Pipeline Server project.
+
+  ```sh
+    cd [WORKDIR]
+    git clone https://github.com/open-edge-platform/edge-ai-libraries.git
+    cd edge-ai-libraries/microservices/dlstreamer-pipeline-server/helm
+    ```
 
 ## Quick try out
 Follow the steps in this section to quickly pull the latest pre-built DL Streamer Pipeline Server helm charts followed by running a sample usecase. 
 
-### Pull the helm chart
+### Pull the helm chart (Optional)
+
+- Note: The helm chart should be downloaded when you are not using the helm chart provided in `edge-ai-libraries/microservices/dlstreamer-pipeline-server/helm`
 
 - Download helm chart with the following command
 
@@ -46,10 +55,11 @@ 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. Both the model and video are provided as default sample in the docker image.
+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.
+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.
+It comprises of a source file path which is `warehouse.avi`, a destination, with metadata directed to a json file 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.
 
 Open another terminal and send the following curl request
 ```sh
@@ -108,6 +118,8 @@ To check the pipeline status and stop the pipeline send the following requests,
 
 Now you have successfully run the DL Streamer Pipeline Server container, sent a curl request to start a pipeline within the microservice which runs the Geti based pallet defect detection model on a sample warehouse video. Then, you have also looked into the status of the pipeline to see if everything worked as expected and eventually stopped the pipeline as well.
 
+## Troubleshooting
+- [Troubleshooting Guide](./troubleshooting-guide.md)
 
 ## Summary
 

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

@@ -53,7 +53,7 @@ Ensure to build/pull the DL Streamer Pipeline Server extended image i.e., `intel
 
 Below is an example that shows how to subscribe to the published data.
 
-- Install ROS2 Humble on Ubuntu22 and source it. Install pythyon and related dependencies too.
+- Install ROS2 Humble on Ubuntu22 and source it. Install python and related dependencies too.
     ```sh
         # Install ROS2 Humble
         sudo apt update && sudo apt install -y curl gnupg lsb-release
@@ -91,73 +91,73 @@ Below is an example that shows how to subscribe to the published data.
 
 - Save the below sample subscriber script as `ros_subscriber.py`
     ```python
-        #!/usr/bin/env python3
-        import sys
-        import rclpy
-        from rclpy.node import Node
-        from std_msgs.msg import String
-        import json
-        import base64
-        import cv2
-        import numpy as np
-        import re
-
-        class SimpleSubscriber(Node):
-            def __init__(self, topic_name):
-                super().__init__('simple_subscriber')
-                self.topic_name = topic_name
-                self.subscription = self.create_subscription(
-                    String,
-                    topic_name,
-                    self.listener_callback,
-                    10
-                )
-                self.counter = 0
-                # clean topic name for filename (remove /)
-                self.topic_safe = re.sub(r'[^a-zA-Z0-9_]', '_', topic_name)
-                print(f"Subscribed to topic: {topic_name}")
-
-            def listener_callback(self, msg):
-                try:
-                    data = json.loads(msg.data)
-
-                    metadata = data.get("metadata", {})
-                    print(f"Metadata on topic {self.topic_name}: {metadata}")
-
-                    image_b64 = data.get("blob", "")
-                    if image_b64:
-                        img_bytes = base64.b64decode(image_b64)
-                        np_arr = np.frombuffer(img_bytes, np.uint8)
-                        img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
-
-                        if img is not None:
-                            filename = f"{self.topic_safe}_{self.counter}.jpg"
-                            cv2.imwrite(filename, img)
-                            print(f"Image from topic {self.topic_name} saved to {filename}")
-                            self.counter += 1
-                        else:
-                            print("Failed to decode image.")
+    #!/usr/bin/env python3
+    import sys
+    import rclpy
+    from rclpy.node import Node
+    from std_msgs.msg import String
+    import json
+    import base64
+    import cv2
+    import numpy as np
+    import re
+
+    class SimpleSubscriber(Node):
+        def __init__(self, topic_name):
+            super().__init__('simple_subscriber')
+            self.topic_name = topic_name
+            self.subscription = self.create_subscription(
+                String,
+                topic_name,
+                self.listener_callback,
+                10
+            )
+            self.counter = 0
+            # clean topic name for filename (remove /)
+            self.topic_safe = re.sub(r'[^a-zA-Z0-9_]', '_', topic_name)
+            print(f"Subscribed to topic: {topic_name}")
+
+        def listener_callback(self, msg):
+            try:
+                data = json.loads(msg.data)
+
+                metadata = data.get("metadata", {})
+                print(f"Metadata on topic {self.topic_name}: {metadata}")
+
+                image_b64 = data.get("blob", "")
+                if image_b64:
+                    img_bytes = base64.b64decode(image_b64)
+                    np_arr = np.frombuffer(img_bytes, np.uint8)
+                    img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+
+                    if img is not None:
+                        filename = f"{self.topic_safe}_{self.counter}.jpg"
+                        cv2.imwrite(filename, img)
+                        print(f"Image from topic {self.topic_name} saved to {filename}")
+                        self.counter += 1
                     else:
-                        print("No image data in message.")
+                        print("Failed to decode image.")
+                else:
+                    print("No image data in message.")
 
-                except Exception as e:
-                    print(f"Error on topic {self.topic_name}: {e}")
+            except Exception as e:
+                print(f"Error on topic {self.topic_name}: {e}")
 
-        def main(args=None):
-            rclpy.init(args=args)
+    def main(args=None):
+        rclpy.init(args=args)
 
-            # get topic from command line
-            topic_name = '/dlstreamer_pipeline_results'  # default
-            if len(sys.argv) > 1:
-                topic_name = sys.argv[1]
+        # get topic from command line
+        topic_name = '/dlstreamer_pipeline_results'  # default
+        if len(sys.argv) > 1:
+            topic_name = sys.argv[1]
 
-            node = SimpleSubscriber(topic_name)
-            rclpy.spin(node)
-            node.destroy_node()
-            rclpy.shutdown()
+        node = SimpleSubscriber(topic_name)
+        rclpy.spin(node)
+        node.destroy_node()
+        rclpy.shutdown()
 
-        if __name__ == '__main__':
-            main()
+    if __name__ == '__main__':
+        main()
     ```
 
 - Run the sample subscriber script as follows and view the metadata being printed and frames being saved.

microservices/dlstreamer-pipeline-server/docs/user-guide/index.rst

@@ -8,6 +8,7 @@
    overview-architecture
    system-requirements
    get-started
+   troubleshooting-guide
 
 .. toctree::
    :caption: How to