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Welcome

The QIM SDK App Builder guides walk you through constructing your own GStreamer-based application on Qualcomm platforms — without needing deep GStreamer expertise. IoT engineering teams span a wide spectrum of needs, so the App Builder SDKs meet developers where they are:
  • Python — high-level interfaces for rapid prototyping and orchestration, ideal for partners and hobbyists.
  • C++ — high-performance environments for commercial product builders.

Why These SDKs

  • Allow developers to build Python-based applications with hardware acceleration across Camera, AI/NPU, Video (Encode/Decode), Audio, GPU, and Display IPs.
  • Easy APIs with an extremely low learning curve.
  • Easy AI model pipeline enablement, with custom pre- and post-processing.
  • Hosting compiler-independent applications at AI Hub.
  • Eliminate the learning curve involved with native SDKs.
  • Quick prototyping to help ODMs/customers quantify SoC capabilities, including:
    • Single-model performance.
    • Single / daisy-chain / multi-stream pipeline performance — ISP camera (single/multi), video decode/encode capabilities, latencies, GPU%, CPU%, thermal readouts.
  • Amazon Fleet 3.0: 5 cameras, 13 AI streams, 2 network streams, 10 encode, overlays, WebRTC/RTSP.
  • Geotab: 2 cameras, two four-stage daisy-chain AI pipelines with 4 AI models running cascaded.
  • Help ODMs/partners in the SoC-selection process, reducing dependency on CE and Dev Engineering teams — BD/Marketing teams can share the public documentation and customers evaluate on their own.
  • Help non-Edge-AI/app developers such as camera sensor tuners, AI model trainers, and data collectors.
  • Meets ODM requests for Nvidia-equivalent SDKs to keep migration to Qualcomm easy, offering the same Pythonic SDK style they expect.
Introduction

What These SDKs Cover

The App Builder SDKs provide comprehensive building blocks to build a complete AI application, covering all possible facets:
  • Data capture — ISP/USB camera, network camera (WebRTC/HLS/RTSP), file source, audio (mic/USB/RTSP).
  • Inference — tensor in/out via TFLite/LiteRT, ONNXRT, QNN, SNPE.
  • Hardware-accelerated ML pre-processing — GPU (GLES), CPU (Neo Kernels), EVA Kernels — covering a wide range of AI Hub models.
  • ML post-processing — covering a wide range of AI Hub models.
  • An easy mechanism for developers to write their own pre- and post-processing for AI model onboarding.
  • Hardware-accelerated overlays — bounding boxes, masks, labels, privacy masks, arbitrary shapes/sizes — with full developer access to build their own.
  • Zero-copy throughout the pipeline — multi-threaded, parallel workloads with optimal hardware acceleration utilization and no overhead.
  • Video decode/encode — all formats supported by the SoC, including JPEG encode/decode and MJPEG.
  • Wide range of muxers/parsers and file containers (200+) covering audio, video, and metadata streams.
  • Wide range of software codecs (100+).
  • Wide range of streaming stacks — WebRTC, RTSP, HLS, RTMP.
  • Multi-stream AI / daisy-chain — nth-stage AI metadata cascading, JSON formatting, AI metadata streaming.
  • IoT message broker protocols — MQTT, Kafka, and Redis.
  • Concurrent AI — vision AI, audio AI, and sensor AI (time-series), with time synchronization.
  • Support for running these SDKs in a Docker container, giving the same experience at both the platform and container level.
  • Exception-based unified error handling.
  • A rich set of sample applications and documentation (API documentation and blog-style content).

Two Language Bindings

These guides cover the essential application-level concepts required to initialize GStreamer, construct and configure a pipeline programmatically, manage pipeline execution state, and handle runtime events — using QIM SDK plugins alongside standard GStreamer elements. Once you’re comfortable with the core concepts, explore the Sample Applications section for ready-to-run C++ and Python applications demonstrating AI and multimedia use cases built on these same concepts.