Troubleshooting

This page provides troubleshooting steps, FAQs, and resources to help you resolve common issues. If you encounter any problems with the application not addressed here, check the GitHub Issues board. Feel free to file new tickets there (after learning about the guidelines for Contributing).

Troubleshooting Steps

1. Changing the Host IP Address

   • If you need to use a specific Host IP address instead of the one automatically detected during installation, you can explicitly provide it using the following command:

     ./install.sh <application-name> <HOST_IP>
     

     Example:

     ./install.sh smart-parking 192.168.1.100
     

2. Containers Not Starting

   • Check the Docker logs for errors:

     docker ps -a
     docker logs <CONTAINER_ID>
     

3. Failed Service Deployment

   • If unable to deploy services successfully due to proxy issues, ensure the proxy is configured in the ~/.docker/config.json:

     {
       "proxies": {
         "default": {
           "httpProxy": "http://your-proxy:port",
           "httpsProxy": "https://your-proxy:port",
           "noProxy": "localhost,127.0.0.1"
         }
       }
     }
     

   • After editing the file, restart docker:

     sudo systemctl daemon-reload
     sudo systemctl restart docker
     

4. Video stream not displaying on Grafana UI

   • If you do not see the video stream because of a URL issue, ensure that WEBRTC_URL in Grafana has:

     # When Grafana is opened on https://localhost/grafana
     https://localhost/mediamtx/
     
     # When Grafana is opened on https://<HOST_IP>/grafana
     https://<HOST_IP>/mediamtx/
     

Troubleshooting Helm deployments

1. Deploy with Intel GPU K8S Extension on Intel® Tiber™ Edge Platform

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 config.json) with Intel GPU k8s Extension on Intel® Tiber™ Edge Platform, ensure to set the following 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

2. 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 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