# DeepStream **Repository Path**: lyh4321/DeepStream ## Basic Information - **Project Name**: DeepStream - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-08-04 - **Last Updated**: 2026-08-12 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # DeepStream [NVIDIA DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) is a streaming analytics toolkit for AI-based video and image understanding, providing a GStreamer-based framework to build multi-stream, multi-model inference pipelines on NVIDIA GPUs (dGPU and Jetson). DeepStream pipelines combine hardware-accelerated decoding/encoding, TensorRT inference, object tracking, and message-broker integrations to deliver real-time video analytics across dGPU and Jetson platforms. DeepStream is published as a unified GitHub repository; sources are to be used under the project [LICENSE](LICENSE) (documentation: CC-BY-4.0; source code: Apache-2.0). GitHub Release assets specific license details can be referred at [Release Asset License](#release-asset-license) under [DeepStream Release Assets](#deepstream-release-assets). The project is actively maintained, but currently not accepting code contributions — refer to the [Contribution Guidelines](#contribution-guidelines) for more details. # Overview This repository contains the complete source code for DeepStream 9.1. **Components** ([`src/`](src/README.md)): - [`src/gst-plugins/`](src/README.md#gstreamer-plugins) — DeepStream GStreamer plugin sources - [`src/utils/`](src/README.md#utility-libraries) — utility library sources - [`src/apps/sample_apps/`](src/README.md#sample-applications) — GStreamer-based sample applications - [`src/apps/reference_apps/`](src/README.md#reference-applications) — advanced reference applications - [`src/apps/tao_apps/`](src/README.md#tao-apps) — TAO-model integration apps - [`src/service-maker/`](src/README.md#service-maker) — Service Maker C++/Python SDK and apps **Tools** ([`tools/`](tools/)): - [`inference_builder`](https://github.com/NVIDIA-AI-IOT/inference_builder/tree/3f0c09f2e3da076cbbb75e17bdebd565b03d1a18) — visual inference pipeline builder - [`auto-magic-calib`](https://github.com/NVIDIA-AI-IOT/auto-magic-calib/tree/bc5b1d9b3154d4242f147c35754670bc7981d37a) — camera auto-calibration tool - [`yolo_deepstream`](tools/yolo_deepstream/README.md) — YOLO + TensorRT integration - [`sam2-onnx-tensorrt`](tools/sam2-onnx-tensorrt/README.md) — SAM2 ONNX-to-TensorRT conversion - [`deepstream-host-provisioning`](tools/deepstream-host-provisioning/README.md) — Ansible playbooks to provision Ubuntu hosts (x86_64 & ARM SBSA) with the full NVIDIA compute stack (driver, CUDA, cuDNN, TensorRT, Docker) and DeepStream dependencies - [`deepstream-otelcol`](tools/deepstream-otelcol/README.md) — OpenTelemetry Collector setup for streaming metrics **AI agent skills** ([`skills/`](skills/README.md), for Claude Code & compatible coding agents): - [`deepstream-dev`](skills/deepstream-dev/SKILL.md) — general DeepStream development - [`deepstream-generate-pipeline`](skills/deepstream-generate-pipeline/SKILL.md) — interactive `gst-launch` pipeline builder - [`deepstream-profile-pipeline`](skills/deepstream-profile-pipeline/SKILL.md) — Nsight Systems profiling & config derivation - [`deepstream-sop`](skills/deepstream-sop/SKILL.md) — SOP step-sequence compliance microservice - [`deepstream-import-vision-model`](skills/deepstream-import-vision-model/SKILL.md) — autonomous vision-model onboarding - [`deepstream-run-mv3dt`](skills/deepstream-run-mv3dt/SKILL.md) — run the MV3DT reference app on sample or custom datasets - [`amc-setup-calibration-stack`](skills/amc-setup-calibration-stack/SKILL.md) — launch the AutoMagicCalib microservice and UI - [`amc-run-sample-calibration`](skills/amc-run-sample-calibration/SKILL.md) — verify AMC with the bundled sample dataset - [`amc-run-video-calibration`](skills/amc-run-video-calibration/SKILL.md) — calibrate user-provided MP4 camera videos - [`amc-run-rtsp-calibration`](skills/amc-run-rtsp-calibration/SKILL.md) — calibrate live RTSP camera streams through VIOS capture - [`rtvi-cv-customize-model`](skills/rtvi-cv-customize-model/SKILL.md) — swap the CV detection model in RTVI-CV - [`rtvi-vlm-customize-model`](skills/rtvi-vlm-customize-model/SKILL.md) — swap the VLM in the VSS Alerts Blueprint - [`rtvi-cv-scaffold-vss-service`](skills/rtvi-cv-scaffold-vss-service/SKILL.md) — scaffold a custom RTVI CV microservice that publishes to VSS via Kafka The Brev launchable workflow is available in [`deploy/brev/`](deploy/brev/README.md). # Requirements Before building, ensure the following prerequisites are installed: - **NVIDIA compute stack** — driver, CUDA, cuDNN, and TensorRT at the versions listed below. See the [DeepStream SDK Installation Guide](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Installation.html). - **DeepStream 9.1 proprietary runtime** — the prebuilt libraries and sample data are published as GitHub Release assets. `bash build/build.sh` automatically downloads the packages into `artifacts/` before the build process starts. > **Note:** After a successful build, `artifacts/` is removed automatically. Pass `--keep-assets` to retain them. > **SBSA / DGX Spark:** DeepStream bare-metal installation is not supported on SBSA. Build and validation must be done inside the NVIDIA SBSA Docker container, which ships with DeepStream pre-installed — `build/build.sh` is **not required** inside the container. If you modify an open-source component, run `make && make install` in that component's directory to deploy the updated binaries/libs. See [build/BUILD.md](build/BUILD.md). # Getting Started ```bash # Install Git LFS (required for LFS-tracked sample media in reference apps) sudo apt-get install git-lfs && git lfs install # Clone the repo with submodules git clone --recurse-submodules https://github.com/NVIDIA/DeepStream.git && cd DeepStream ``` See **[build/BUILD.md](build/BUILD.md)** for full build instructions, including system package dependencies (x86, aarch64, SBSA / DGX Spark), `build/build.sh` usage and environment variables (`CUDA_VER`, `NVDS_VERSION`, `INSTALL_METHOD`), and build output locations under `/opt/nvidia/deepstream/deepstream-9.1/`. **Supported Platforms** | Platform | Architecture | Notes | |---|---|---| | x86 dGPU | x86_64 | Ubuntu 24.04, CUDA 13.2, TensorRT 10.16.x, driver 595+ | | Jetson | aarch64 | JetPack 7.2 GA (CUDA 13.2, TensorRT 10.16.x) | | SBSA / DGX Spark | aarch64 | Bare-metal DS installation not supported; build and run inside the NVIDIA SBSA Docker container. Only open-source components are built — no artifacts installed. | ```bash # Build. The script prompts for sudo only when installing to system paths. bash build/build.sh ``` # Usage After `bash build/build.sh`, binaries are installed to `/opt/nvidia/deepstream/deepstream-9.1/bin/`. Run the reference `deepstream-app` with one of the sample configs: ```bash cd /opt/nvidia/deepstream/deepstream-9.1/samples/configs/deepstream-app deepstream-app -c source30_1080p_dec_infer-resnet_tiled_display.txt # After the first install, clear the GStreamer plugin cache if needed: rm -rf ~/.cache/gstreamer-1.0/ ``` - More such examples: [DeepStream Quickstart Guide](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Quickstart.html#run-deepstream-app-the-reference-application) - Detailed API reference: [DeepStream SDK Developer Guide](https://docs.nvidia.com/metropolis/deepstream/dev-guide/) Each app must be run from its source directory so relative config paths resolve correctly. Refer to the `README` inside each app directory for app-specific run instructions and config options. ## Running with Triton Inference Server (Docker) > **Optional.** Skip this if the bare-metal build above already works for you. Use the Triton container when you need Triton-backed inference. On SBSA / DGX Spark, use the SBSA Docker container as described above — bare-metal installation is not supported. NVIDIA publishes a DeepStream Docker image bundled with Triton Inference Server. ```bash # One-time NGC login (get an API key from https://ngc.nvidia.com) docker login nvcr.io # username: $oauthtoken, password: # Pull the image (use 9.1-triton-arm-sbsa instead on SBSA / DGX Spark) docker pull nvcr.io/nvidia/deepstream:9.1-triton-multiarch # Launch with display (use 'fakesink' in your pipeline for headless) export DISPLAY=:0 && xhost + docker run -it --rm --gpus all --network=host \ -e DISPLAY=$DISPLAY -v /tmp/.X11-unix/:/tmp/.X11-unix \ nvcr.io/nvidia/deepstream:9.1-triton-multiarch # Inside the container, run a Triton-backed sample cd /opt/nvidia/deepstream/deepstream-9.1/samples/configs/deepstream-app-triton deepstream-app -c source30_1080p_dec_infer-resnet_tiled_display.txt ``` **Prerequisites:** Docker (`docker-ce`), the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html), NVIDIA driver 595+, and an NGC API key. Triton sample model repos ship under `/opt/nvidia/deepstream/deepstream/samples/triton_model_repo/`. For gRPC-backed Triton, use `samples/configs/deepstream-app-triton-grpc/` instead. # Documentation | Page | Description | |------|-------------| | [Overview & Architecture](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Overview.html) | What DeepStream is, key features, and the GStreamer-based pipeline architecture. | | [Release Notes](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Release_notes.html) | What's new in this release, supported platforms, and known issues. | | [Installation](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Installation.html) | Step-by-step installation of the NVIDIA compute stack and DeepStream SDK on x86 and Jetson. | | [Docker Containers](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_docker_containers.html) | Available DeepStream NGC container images (incl. Triton variants) and how to pull and run them. | | [DeepStream Samples](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_C_Sample_Apps.html) | Reference walkthroughs of the bundled C/C++ sample applications and what each one demonstrates. | | [Reference Applications](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_ref_app_github.html) | Advanced GitHub-hosted reference apps demonstrating specialized end-to-end pipelines. | | [TAO Apps](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_TAO_integration.html) | DeepStream sample apps integrating TAO-trained models (detection, classification, segmentation, pose, LPR). | | [DeepStream Plugins](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_Intro.html) | Reference for every DeepStream GStreamer plugin — properties, pad caps, and usage. | | [Service Maker](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_service_maker_intro.html) | Build DeepStream pipelines declaratively with the C++ / Python Service Maker SDK. | | [Inference Builder](https://github.com/NVIDIA-AI-IOT/inference_builder/tree/3f0c09f2e3da076cbbb75e17bdebd565b03d1a18) | Compose and configure DeepStream inference pipelines visually with the Inference Builder tool. | | [Auto Magic Calib](https://github.com/NVIDIA-AI-IOT/auto-magic-calib/tree/bc5b1d9b3154d4242f147c35754670bc7981d37a) | Automatic camera calibration tool for multi-camera DeepStream deployments. | | [DeepStream Libraries](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Libraries.html) | DeepStream utility and helper libraries bundled as the `deepstream_libraries` submodule. | | [DeepStream Coding Agent](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_AI_Agent.html) | Use the bundled `skills/` with Claude Code and other AI coding assistants to generate DeepStream pipelines. | # Repository Structure ``` DeepStream/ ├── .claude/ # Claude Code project-local config (settings, commands) ├── .github/ # GitHub workflows, issue templates, CODEOWNERS ├── build/ │ ├── BUILD.md # build instructions │ └── build.sh # top-level build driver ├── example_prompts/ # example prompts for AI coding agents ├── deploy/brev/ # Brev launchable workflow for DeepStream Code Agent ├── includes/ # shared public headers (ds3d, nvdsinferserver, …) ├── scripts/ │ ├── install_artifacts.sh # installs proprietary libs + sample data to /opt │ ├── install_opensource_deps.sh # builds open-source deps (OpenTelemetry, civetweb, …) │ ├── install.sh # system integration (update-alternatives, pyservicemaker) │ ├── uninstall.sh # removes a DeepStream installation from /opt │ ├── triton_backend_setup.sh # sets up Triton inference server backends │ ├── prepare_ds_triton_model_repo.sh # prepares the Triton model repository │ ├── prepare_ds_triton_tao_model_repo.sh # prepares the Triton model repository for TAO models │ ├── prepare_classification_test_video.sh # generates a classification test video │ ├── rtpmanager/update_rtpmanager.sh # rtpjitterbuffer/rtsp EOS-hang fix script + patch files │ └── print_env.sh # env diagnostic helper for bug reports ├── skills/ │ ├── amc-run-sample-calibration/ # AMC sample-dataset calibration skill │ ├── amc-run-video-calibration/ # AMC custom-video calibration skill │ ├── amc-run-rtsp-calibration/ # AMC RTSP-stream calibration skill │ ├── amc-setup-calibration-stack/ # AMC microservice + UI launch skill │ ├── deepstream-dev/ # general DS development skill │ ├── deepstream-generate-pipeline/ # interactive gst-launch pipeline builder │ ├── deepstream-profile-pipeline/ # Nsight Systems profiling & config derivation │ ├── deepstream-sop/ # SOP step-sequence compliance microservice │ ├── deepstream-import-vision-model/ # autonomous vision-model onboarding skill │ └── deepstream-run-mv3dt/ # MV3DT reference-app operations skill │ ├── rtvi-cv-customize-model/ # RTVI-CV model customization skill │ ├── rtvi-vlm-customize-model/ # RTVI VLM model customization skill │ └── rtvi-cv-scaffold-vss-service/ # RTVI CV VSS-service scaffolding skill ├── src/ │ ├── apps/ # sample, reference, and TAO sample applications │ ├── gst-plugins/ # GStreamer plugin sources (per-plugin subdirs) │ ├── gst-utils/ # GStreamer utility library sources │ ├── service-maker/ # Service Maker C++/Python SDK and apps │ └── utils/ # utility library sources (per-library subdirs) ├── deepstream_libraries/ # DeepStream utility and helper libraries (submodule) ├── deepstream_dockers/ # DeepStream dockers └── tools/ ├── auto-magic-calib/ # camera auto-calibration tool ├── deepstream-otelcol/ # OpenTelemetry Collector setup for streaming metrics ├── inference_builder/ # visual inference pipeline builder ├── deepstream-host-provisioning/ # Ansible playbooks for host provisioning (x86_64 & ARM SBSA) ├── sam2-onnx-tensorrt/ # SAM2 ONNX-to-TensorRT conversion └── yolo_deepstream/ # YOLO + TensorRT integration ``` > **NOTE:** DeepStream-specific repositories previously hosted under the NVIDIA-AI-IOT GitHub organization are now consolidated into this repository. The table below maps the existing NVIDIA-AI-IOT GitHub repository sources to their location in this repository. | NVIDIA-AI-IOT GitHub repo | Location in this repository | | --------------------------------------------------------------------------------------- | ------------------------------------------------------ | | [deepstream_tao_apps](https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps) | [src/apps/tao_apps/](src/apps/tao_apps/) | | [deepstream_reference_apps](https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps) | [src/apps/reference_apps/](src/apps/reference_apps/) | | [deepstream_tools](https://github.com/NVIDIA-AI-IOT/deepstream_tools) | [tools/](tools/) | | [DeepStream_Coding_Agent](https://github.com/NVIDIA-AI-IOT/DeepStream_Coding_Agent) | [skills/](skills/README.md) | | [inference_builder](https://github.com/NVIDIA-AI-IOT/inference_builder) | [tools/inference_builder/](tools/inference_builder/) | | [auto-magic-calib](https://github.com/NVIDIA-AI-IOT/auto-magic-calib) | [tools/auto-magic-calib/](tools/auto-magic-calib/) | | [deepstream_dockers](https://github.com/NVIDIA-AI-IOT/deepstream_dockers) | [deepstream_dockers/](deepstream_dockers/) | | [deepstream_libraries](https://github.com/NVIDIA-AI-IOT/deepstream_libraries) | [deepstream_libraries/](deepstream_libraries/) | # Performance Summary of benchmarks; for detailed performance numbers and hardware used, refer to the [DeepStream Performance Guide](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Performance.html). # Releases & Roadmap - **Releases / Changelog:** [CHANGELOG.md](./CHANGELOG.md) ## DeepStream Release Assets The following `.deb` and `.tar` artifacts are published as GitHub Release assets. `build/build.sh` downloads them automatically into `artifacts/` before installation: ### Debian packages (`.deb`) | Asset | Architecture | Description | |---|---|---| | `deepstream-9.1_9.1.0-1_amd64.deb` | x86_64 | DeepStream 9.1 SDK Debian package for x86 dGPU. | | `deepstream-9.1_9.1.0-1_arm64.deb` | aarch64 | DeepStream 9.1 SDK Debian package for Jetson. | | `deepstream-binaries-x86_9.1.0_amd64.deb` | x86_64 | Prebuilt proprietary runtime libraries for x86. | | `deepstream-binaries-aarch64_9.1.0_arm64.deb` | aarch64 | Prebuilt proprietary runtime libraries for Jetson. | | `deepstream-sample-data_9.1.0.deb` | all | Sample models, configs, and video streams. | ### Tarball archives (`.tar.gz` / `.tbz2`) | Asset | Architecture | Description | |---|---|---| | `deepstream_sdk_v9.1.0_x86_64.tbz2` | x86_64 | DeepStream 9.1 SDK tarball for x86 dGPU. | | `deepstream_sdk_v9.1.0_jetson.tbz2` | aarch64 | DeepStream 9.1 SDK tarball for Jetson. | | `deepstream-binaries-x86_9.1.0.tar.gz` | x86_64 | Prebuilt proprietary runtime libraries for x86. | | `deepstream-binaries-aarch64_9.1.0.tar.gz` | aarch64 | Prebuilt proprietary runtime libraries for Jetson. | | `deepstream-sample-data_9.1.0.tar.gz` | all | Sample models, configs, and video streams. | ### Python wheels (`.whl`) | Asset | Architecture | Description | |---|---|---| | `deepstream_libraries-1.4-cp312-cp312-linux_x86_64.whl` | x86_64 | DeepStream Libraries Python wheel (CPython 3.12) for x86. | ### Download installer(s) Release assets can be downloaded via `curl` or `wget` from the command line. **curl** ```bash curl -LO 'https://github.com/NVIDIA/DeepStream/releases/download/v9.1.0/deepstream-9.1_9.1.0-1_amd64.deb' ``` **wget** ```bash wget --content-disposition 'https://github.com/NVIDIA/DeepStream/releases/download/v9.1.0/deepstream-9.1_9.1.0-1_amd64.deb' ``` > **NOTE:** Replace `deepstream-9.1_9.1.0-1_amd64.deb` with the name of the asset you want to download. ## Docker Images (NGC) DeepStream Docker images are released through NGC. Browse and pull the images from the [DeepStream NGC container catalog](https://catalog.ngc.nvidia.com/orgs/nvidia/-/containers/deepstream/-?_lr=1). > **NOTE:** For `docker login` and `docker pull` command details, along with the full list of available image tags, refer to the [DeepStream NGC container catalog](https://catalog.ngc.nvidia.com/orgs/nvidia/-/containers/deepstream/-?_lr=1). # Contribution Guidelines This project is currently not accepting contributions. The product roadmap is managed internally by NVIDIA. ## Security - Vulnerability disclosure: File an issue at [Issues](https://github.com/NVIDIA/DeepStream/issues). - Do not file public issues for security reports. ## Support - Level: Maintained - How to get help: [Issues](https://github.com/NVIDIA/DeepStream/issues) / [DeepStream SDK Developer Forum](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/deepstream-sdk/15) - Filing a bug? Use the [bug report template](.github/ISSUE_TEMPLATE/bug_report.md) and include: platform (x86 / Jetson / SBSA), DeepStream version, install method (Debian / Docker / source), and the output of `scripts/print_env.sh`. # Community Join the DeepStream community to ask questions, share feedback, and report issues. - [GitHub Issues](https://github.com/NVIDIA/DeepStream/issues) - [GitHub Discussions](https://github.com/NVIDIA/DeepStream/discussions) - [DeepStream SDK Developer Forum](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/deepstream-sdk/15) # References - [DeepStream NGC container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/deepstream) — prebuilt runtime image (also bundles Triton). - [NVIDIA TAO Toolkit](https://developer.nvidia.com/tao-toolkit) — model training/optimization, source of the TAO sample apps. - [GStreamer documentation](https://gstreamer.freedesktop.org/documentation/) — underlying framework. # License This project is licensed under [CC-BY-4.0 AND Apache-2.0](./LICENSE). - SPDX-License-Identifier: CC-BY-4.0 AND Apache-2.0 ### Release Asset License All GitHub Release assets (`.deb` packages and `.tar.gz` tarballs under the `deepstream-binaries-*` and `deepstream-sample-data_*` names) are governed by the [Software License Agreement For NVIDIA Software Development Kits](https://developer.download.nvidia.com/assets/Deepstream/DeepStream_6.0/NVIDIA_DeepStream_SDK_EULA_6.0.pdf), refer the "LicenseAgreement" included within all GitHub Release Assets packages. ## Third‑Party License/Notice Refer [THIRD_PARTY_LICENSES](./THIRD_PARTY_LICENSES.txt) for all 3rd Party OSS licenses and Notices