# 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](https://github.com/open-edge-platform/edge-ai-suites/issues) board. Feel free
to file new tickets there (after learning about the guidelines for [Contributing](https://github.com/open-edge-platform/edge-ai-suites/blob/main/CONTRIBUTING.md)).

## 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:

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

     Example:
     ```bash
     ./install.sh smart-parking 192.168.1.100
     ```

2. **Containers Not Starting**
   - Check the Docker logs for errors:
     ```bash
     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`:

     ```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:
     ```bash
     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:
      ```bash
      # 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.
```sh
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.
```sh
privileged_access_required: true
```