Troubleshooting#

The following are options to help you resolve issues with the sample application.

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.

sudo ufw disable

Error Logs#

View the container logs using this command.

docker logs -f <CONTAINER_NAME>

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:

    sudo apt install systemd-timesyncd

  2. Check service status:

    systemctl status systemd-timesyncd

  3. Configure an NTP server (if behind a corporate proxy):

    sudo nano /etc/systemd/timesyncd.conf

    Add:

    [Time]
NTP=corp.intel.com

    Replace corp.intel.com with a different ntp server that is supported on your network.

  4. Restart the service:

    sudo systemctl restart systemd-timesyncd

  5. Verify the status:

    systemctl status systemd-timesyncd

This should resolve the time discrepancy in Prometheus.

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.

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.

privileged_access_required: true

Inferencing on NPU#

To perform inferencing on an NPU device (for platforms with NPU accelerators such as Ultra Core processors), ensure you have completed the required pre-requisites. Refer to the instructions here to install Intel NPU drivers.

Unable to parse JSON payload due to missing jq package#

While running sample_start.sh script, you may get an error- ERROR: jq is not installed. Cannot parse JSON payload. This is due to mising jq package that is need to parse the payload JSON. Please follow the steps to install it.

sudo apt update
sudo apt install jq

Unable to run GPU inference on some Arrow Lake machines with resource allocation failed errors#

For example:

ERROR vafilter gstvafilter.c:390:gst_va_filter_open:<vafilter0> vaCreateContext: resource allocation failed

This issue has been observed on systems with the Ultra Core 7 265K processor running Ubuntu 22.04. There are few options to fix this.

One is updating the kernel to 6.11.11-061111-generic in the host system.

Alternately, install OpenCL runtime packages in the host system. Refer to the instructions from OpenVINO documentation here to install GPU drivers.