# 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.

```sh
sudo ufw disable
```

## Error Logs

View the container logs using this command.

```sh
docker logs -f <CONTAINER_NAME>
```

## Resolve Time Sync Issues in Prometheus

If you see the following warning in Prometheus, it indicates a time sync issue.

```text
Warning: Error fetching server time: Detected xxx.xxx seconds time difference between your browser and the server.
```

You can follow 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.

## Axis RTSP camera freezes or pipeline stops

Restart the DL Streamer pipeline server container with the pipeline that has this RTSP source.

## Deployment with Intel® GPU K8S Extension

If you are deploying a GPU based pipeline (example: with VA 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
```

## For Helm Deployments, **`dlstreamer-pipeline-server` pod shows `CreateContainerError`**

  - **Issue**: The `dlstreamer-pipeline-server` pod fails to start and shows `CreateContainerError`. This issue is seen only on environments using the `docker://` container runtime.
  - **Check Container Runtime**: Run the following command to check which container runtime is being used:

    ```bash
    kubectl get nodes -o wide
    ```

    Inspect the `CONTAINER-RUNTIME` column to check if the node is using the `docker://` runtime.

  - **Fix**: Run the following command to configure the Intel GPU device plugin for container runtime compatibility:

    ```bash
    kubectl patch ds intel-gpu-plugin -n intel-device-plugins --type='json' \
      -p='[{"op": "add", "path": "/spec/template/spec/containers/0/args/-", "value": "-bypath=none"}]'
    ```
    Restart the Helm deployment once this fix is implemented.



## Inference 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 prerequisites. Refer to the relevant [DL Streamer instructions](https://docs.openedgeplatform.intel.com/2026.2/edge-ai-libraries/dlstreamer/dev_guide/advanced_install/advanced_install_guide_prerequisites.html#optional-prerequisite-2-install-intel-npu-drivers) to install Intel® NPU drivers.

## NPU Inference Failures with Geti-Trained Models (Pallet Defect Detection)

If you experience errors or failures when running an NPU workload with a model trained in Geti™, this may be caused by **Non-Maximum Suppression (NMS)** being embedded within the model graph. The NPU does not support dynamic shapes, and NMS operations with dynamic output shapes are incompatible with NPU execution.

**Resolution**: Follow the [Export and Optimize Geti™ Model](./pallet-defect-detection/how-to-guides/export-and-optimize-geti-model.md) guide to generate a model with NMS removed from the model graph. NMS will then be handled by DL Streamer.

## Inaccurate detections seen when running the NPU inference pipeline on ARL and MTL NPUs

This is a [tracked known issue](https://github.com/open-edge-platform/edge-ai-suites/issues/2230).

## Unable to parse JSON payload due to missing `jq` package

While running the `sample_start.sh` script, you may encounter
`ERROR: jq is not installed. Cannot parse JSON payload.` This indicates that
your system is missing the `jq` package, required to parse the payload JSON file.
Use the commands below to install it.

```sh
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.

Alternatively, install OpenCL runtime packages in the host system. Refer to the relevant [OpenVINO™ documentation](https://docs.openvino.ai/2026/get-started/install-openvino/configurations/configurations-intel-gpu.html#linux) to install GPU drivers.

## Deployment on Edge Microvisor Toolkit

Since Edge Microvisor Toolkit OS image does not include `unzip` nor `jq`
packages by default, you need to install them for proper operation of the
application.

To install `unzip` run:

```sh
sudo apt install unzip
```

To install `jq`, refer to the following
[instructions](#unable-to-parse-json-payload-due-to-missing-jq-package).