updated docs

Author: ajagadi1

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

@@ -55,7 +55,16 @@ 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.
+
+Copy the resources such as video and model from local directory to the to the `dlstreamer-pipeline-server` pod to make them available for launching pipeline.
+```sh
+POD_NAME=$(kubectl get pods -n apps -o jsonpath='{.items[*].metadata.name}' | tr ' ' '\n' | grep dlstreamer-pipeline-server | head -n 1) 
+
+kubectl cp <edge-ai-libraries/microservices/dlstreamer-pipeline-server>/resources/models/geti/ $POD_NAME:/home/pipeline-server/resources/models/geti/ -c dlstreamer-pipeline-server -n apps
+
+kubectl cp <edge-ai-libraries/microservices/dlstreamer-pipeline-server>/resources/videos/warehouse.avi $POD_NAME:/home/pipeline-server/resources/videos/ -c dlstreamer-pipeline-server -n apps
+```
 
 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.

microservices/dlstreamer-pipeline-server/docs/user-guide/troubleshooting-guide.md

@@ -0,0 +1,104 @@
+# 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
+
+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, 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.
+
+## WebRTC Stream on web browser
+The firewall may prevent you from viewing the video stream on web browser. Please disable the firewall using this command.
+```sh
+sudo ufw disable
+```
+
+## Error Logs
+
+View the container logs using this command.
+```sh
+docker logs -f <CONTAINER_NAME>
+```

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

@@ -3,7 +3,7 @@
 ## Steps to deploy the helm chart:
 
 - Note: Incase you do not have a k8s cluster, then please follow the steps mentioned in 'Setup k8s cluster' below before deploying the helm chart.
-- Get into the helm directory
+- Get into the helm directory (where this README.md exists)
     `cd helm`
 - Update the below fields in `values.yaml` file in the helm chart
     ``` sh
@@ -14,6 +14,14 @@
     `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`
+- Copy the resources such as video and model from local directory to the to the `dlstreamer-pipeline-server` pod to make them available for launching pipeline.
+    ```sh
+    POD_NAME=$(kubectl get pods -n apps -o jsonpath='{.items[*].metadata.name}' | tr ' ' '\n' | grep dlstreamer-pipeline-server | head -n 1) 
+
+    kubectl cp ../resources/models/geti/ $POD_NAME:/home/pipeline-server/resources/models/geti/ -c dlstreamer-pipeline-server -n apps
+
+    kubectl cp ../resources/videos/warehouse.avi $POD_NAME:/home/pipeline-server/resources/videos/ -c dlstreamer-pipeline-server -n apps
+    ```
 - Send the curl command to start the pallet defect detection pipeline
     ``` sh
         curl http://localhost:30007/pipelines/user_defined_pipelines/pallet_defect_detection -X POST -H 'Content-Type: application/json' -d '{