Configure sample applications

• Prototype and validate AI workloads across heterogeneous compute targets (CPU, GPU, DSP), helping teams choose the most efficient runtime for their use case.
• Customize application behavior using JSON-based configuration, allowing precise control over input/output sources, model types, and runtime parameters.
• Accelerate development and deployment by leveraging pre-integrated models from AI Hub and supported frameworks like TFLite, QNN, and SNPE.
• Benchmark performance and optimize resource usage, which is critical for embedded systems and edge devices where compute and power budgets are constrained.
• Ensure compatibility and reproducibility across Qualcomm platforms by using standardized scripts and directory structures for models, labels, and media assets.

• Setup the Device.
• Ensure that the IQ‑9075 device is running Ubuntu OS.
• Setup Wi-Fi & access IQ-9075 EVK device via SSH.
• Basic familiarity with JSON configuration files and runtime environment variables.
• Access to AI Hub for model selection and export.
  • Create AI Hub account** if you don’t already have one**
• If the PPA packages are not already installed, run the Install needed packages step.

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Install needed packages

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Download and run the artifact script

• Models – Pretrained machine‑learning models used by the sample applications for inference.
• Labels – Metadata files that map model outputs to human‑readable class names.
• Media files – Sample input data (such as images or videos) used to run and validate the applications.

cd /home/ubuntu 
curl -L -O https://raw.githubusercontent.com/quic/sample-apps-for-qualcomm-linux/refs/heads/main/qualcomm-linux/scripts/download_artifacts.sh
sudo chmod +x download_artifacts.sh 
sudo ./download_artifacts.sh

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Verify Model/Label/Media Files

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Check the following directories:

• Model files → /etc/models/
• Label files → /etc/labels/
• Media files → /etc/media/

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Updating JSON Config File

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What to Do

• Update the required JSON config file based on your model and config requirements.

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Configuration Parameters

sudo vi /etc/configs/config-multistream-inference.json

{
  "input-file-path":
    [
      "/etc/media/video.mp4",
      "/etc/media/video.mp4",
      "/etc/media/video.mp4",
      "/etc/media/video.mp4",
      .......
       "/etc/media/video.mp4",
    ]
}

#Primary
"camera":0
#Secondary
"camera":1

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Run the sample application

1. Use the SSH or SBC terminal to launch the sample application.
2. Set the required environment variables to connect a Wayland application to the active user session.

export XDG_RUNTIME_DIR=/run/user/$(id -u ubuntu)
export WAYLAND_DISPLAY=wayland-1

3. For Desktop build:
   Run the following additional command to creates a missing symbolic link (for TensorFlow Lite C library).

sudo ln -s /lib/aarch64-linux-gnu/libtensorflowlite_c.so.2 /lib/aarch64-linux-gnu/libtensorflowlite_c.so

4. Run sample application.\

gst-ai-multistream-inference

5. To display the available help options, run the following command in the SSH shell:

gst-ai-multistream-inference -h

export GST_DEBUG=3

6. To stop the application, press CTRL + C

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Sample Application Configuration Matrix

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Reference Docs