# 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 and welcomes contributions — refer to the [Contribution Guidelines](#contribution-guidelines) for more details.
# Overview
This repository contains the complete source code for DeepStream 9.2.
**Components** ([`src/`](src/README.md)):
- [`src/gst-plugins/`](src/README.md#gstreamer-plugins) — DeepStream GStreamer plugin sources
- [`src/gst-utils/`](src/README.md#gstreamer-utilities) — GStreamer utility library 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
- [`src/3rdparty/`](src/3rdparty/) — x86-only vendored sources (`gst-nvunixfd`, `gst-v4l2`, `v4l2`) built by `build/build.sh`
**Tools** ([`tools/`](tools/)):
- [`inference_builder`](tools/inference_builder/README.md) — 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
- [`deepstream-setup-on-wsl`](skills/deepstream-setup-on-wsl/SKILL.md) — set up and verify DeepStream in Docker inside WSL2
- [`inference-builder`](skills/inference-builder/SKILL.md) — generate deployable GPU inference pipelines from declarative configs
- [`deepstream-eval-and-finetune`](skills/deepstream-eval-and-finetune/SKILL.md) — evaluate and improve object detectors for DeepStream
- [`amc-run-auto-calibration`](skills/amc-run-auto-calibration/SKILL.md) — launch AMC, verify the sample dataset, calibrate MP4 videos, or calibrate RTSP streams
- [`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/deepstream/9.2/text/DS_Installation).
- **DeepStream 9.2 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-9.2/`.
See [`src/README.md`](src/README.md) for component-specific READMEs, prerequisites, and platform limitations (on SBSA / DGX Spark only the source-available components are built), and [`CHANGELOG.md`](CHANGELOG.md) for the complete 9.2.0 release notes.
**Supported Platforms**
| Platform | Architecture | Notes |
|---|---|---|
| x86 dGPU | x86_64 | Ubuntu 24.04, CUDA 13.2, TensorRT 10.16.x, driver 595+ |
| Jetson | aarch64 | JetPack 7.2.1 (CUDA 13.2, TensorRT 10.16.x) |
| IGX Thor | aarch64 | IGX-SW 2.0 GA (CUDA 13.0, TensorRT 10.13.x, GPU driver 580.00) |
| 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-9.2/bin/`. Run the reference `deepstream-app` with one of the sample configs:
```bash
cd /opt/nvidia/deepstream/deepstream-9.2/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/deepstream/9.2/text/DS_Quickstart#run-deepstream-app-the-reference-application)
- Detailed API reference: [DeepStream SDK Developer Guide](https://docs.nvidia.com/deepstream/9.2)
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
# Pull the image (use 9.2-triton-sbsa-dgx-spark instead on SBSA / DGX Spark)
docker pull ghcr.io/nvidia/deepstream:9.2-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 \
ghcr.io/nvidia/deepstream:9.2-triton-multiarch
# Inside the container, run a Triton-backed sample
cd /opt/nvidia/deepstream/deepstream-9.2/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), and NVIDIA driver 595+. 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.
## Running with Triton Inference Server on Jetson (bare metal)
Prepare Triton dependencies and sample models on Jetson.
> **Building `gst-nvinferserver` standalone?** `bash build/build.sh` sets up the Triton client SDK under `/opt/tritonclient` automatically. If you instead build the plugin standalone (bypassing `build/build.sh`) — including rebuilding it from the `sources/` tree shipped inside a [`build/package.sh`](build/BUILD.md)-produced `.deb`/tarball, which does not bundle the Triton client SDK — set up the Triton client SDK yourself first. See the "Other dependencies" section in [`src/gst-plugins/gst-nvinferserver/README`](src/gst-plugins/gst-nvinferserver/README).
```bash
# Run from the cloned repository root after the DeepStream build has completed.
# Installs the Jetson-compatible Triton server libraries and backends.
sudo ./scripts/triton_backend_setup.sh
# Clear the plugin cache after the Triton libraries are installed.
rm -rf ~/.cache/gstreamer-1.0/
# Generate TensorRT engines and prepare the Triton model repository.
./scripts/prepare_ds_triton_model_repo.sh
# Run the local nvinferserver sample.
cd /opt/nvidia/deepstream/deepstream-9.2/samples/configs/deepstream-app-triton
deepstream-app -c source30_1080p_dec_infer-resnet_tiled_display.txt
```
> **Jetson only.** `triton_backend_setup.sh` downloads and installs the
> Jetson-compatible Triton components under `/opt`. For remote Triton inference
> over gRPC, use `samples/configs/deepstream-app-triton-grpc/` and start
> `tritonserver` separately.
# Documentation
| Page | Description |
|------|-------------|
| [Overview & Architecture](https://docs.nvidia.com/deepstream/9.2/text/DS_Overview) | What DeepStream is, key features, and the GStreamer-based pipeline architecture. |
| [Release Notes](https://docs.nvidia.com/deepstream/9.2/text/DS_Release_notes) | What's new in this release, supported platforms, and known issues. |
| [Installation](https://docs.nvidia.com/deepstream/9.2/text/DS_Installation) | Step-by-step installation of the NVIDIA compute stack and DeepStream SDK on x86 and Jetson. |
| [Docker Containers](https://docs.nvidia.com/deepstream/9.2/text/DS_docker_containers) | Available DeepStream container images (incl. Triton variants) and how to pull and run them. |
| [DeepStream Samples](https://docs.nvidia.com/deepstream/9.2/text/DS_C_Sample_Apps) | Reference walkthroughs of the bundled C/C++ sample applications and what each one demonstrates. |
| [Reference Applications](https://docs.nvidia.com/deepstream/9.2/text/DS_ref_app_github) | Advanced GitHub-hosted reference apps demonstrating specialized end-to-end pipelines. |
| [TAO Apps](https://docs.nvidia.com/deepstream/9.2/text/DS_TAO_integration) | DeepStream sample apps integrating TAO-trained models (detection, classification, segmentation, pose, LPR). |
| [DeepStream Plugins](https://docs.nvidia.com/deepstream/9.2/text/DS_plugin_Intro) | Reference for every DeepStream GStreamer plugin — properties, pad caps, and usage. |
| [Service Maker](https://docs.nvidia.com/deepstream/9.2/text/DS_service_maker_intro) | Build DeepStream pipelines declaratively with the C++ / Python Service Maker SDK. |
| [Inference Builder](tools/inference_builder/README.md) | 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/deepstream/9.2/text/DS_Libraries) | DeepStream utility and helper libraries bundled in this repository at `deepstream_libraries/`. |
| [DeepStream Coding Agent](https://docs.nvidia.com/deepstream/9.2/text/DS_AI_Agent) | 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-auto-calibration/ # AMC setup, sample, video, and RTSP calibration 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/
│ ├── 3rdparty/ # x86-only vendored gst-nvunixfd, gst-v4l2, v4l2
│ ├── 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
├── 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/) |
## Git Submodules
| Submodule | Path | License |
| --------- | ---- | ------- |
| [NVIDIA-Optical-Character-Detection-and-Recognition-Solution (nvocdr)](https://github.com/NVIDIA-AI-IOT/NVIDIA-Optical-Character-Detection-and-Recognition-Solution) | [src/apps/tao_apps/apps/tao_others/deepstream-nvocdr-app/nvocdr/](src/apps/tao_apps/apps/tao_others/deepstream-nvocdr-app/nvocdr/) | [NVIDIA OCDR Solution Software License](src/apps/tao_apps/apps/tao_others/deepstream-nvocdr-app/nvocdr/LICENSE) |
# Performance
Summary of benchmarks; for detailed performance numbers and hardware used, refer to the [DeepStream Performance Guide](https://docs.nvidia.com/deepstream/9.2/text/DS_Performance).
# 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-9.2_9.2.0-1_amd64.deb` | x86_64 | DeepStream 9.2 SDK Debian package for x86 dGPU. |
| `deepstream-9.2_9.2.0-1_arm64.deb` | aarch64 | DeepStream 9.2 SDK Debian package for Jetson. |
| `deepstream-binaries-x86_9.2.0_amd64.deb` | x86_64 | Prebuilt proprietary runtime libraries for x86. |
| `deepstream-binaries-aarch64_9.2.0_arm64.deb` | aarch64 | Prebuilt proprietary runtime libraries for Jetson. |
| `deepstream-sample-data_9.2.0.deb` | all | Sample models, configs, and video streams. |
### Tarball archives (`.tar.gz` / `.tbz2`)
| Asset | Architecture | Description |
|---|---|---|
| `deepstream_sdk_v9.2.0_x86_64.tbz2` | x86_64 | DeepStream 9.2 SDK tarball for x86 dGPU. |
| `deepstream_sdk_v9.2.0_jetson.tbz2` | aarch64 | DeepStream 9.2 SDK tarball for Jetson. |
| `deepstream_sdk_v9.2.0_sbsa.tbz2` | aarch64 (SBSA) | DeepStream 9.2 SDK tarball for SBSA. Tarball only (no deb). |
| `deepstream-binaries-x86_9.2.0.tar.gz` | x86_64 | Prebuilt proprietary runtime libraries for x86. |
| `deepstream-binaries-aarch64_9.2.0.tar.gz` | aarch64 | Prebuilt proprietary runtime libraries for Jetson. |
| `deepstream-sample-data_9.2.0.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/v9.2.0/deepstream-9.2_9.2.0-1_amd64.deb'
```
**wget**
```bash
wget --content-disposition 'https://github.com/NVIDIA/DeepStream/releases/download/v9.2.0/deepstream-9.2_9.2.0-1_amd64.deb'
```
> **NOTE:** Replace `deepstream-9.2_9.2.0-1_amd64.deb` with the name of the asset you want to download.
## Docker Images
DeepStream Docker images are available from two registries, depending on the release:
- DeepStream 9.2.0 and future releases: [GHCR](https://github.com/orgs/NVIDIA/packages/container/package/deepstream) (`ghcr.io/nvidia/deepstream`)
- DeepStream 9.1.0 and previous releases: [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/deepstream)
For example, to pull the DeepStream 9.2 Triton image from GHCR:
```bash
docker pull ghcr.io/nvidia/deepstream:9.2-triton-multiarch
```
# Contribution Guidelines
Contributions are welcome through pull requests to the `develop` branch. Read [CONTRIBUTING.md](CONTRIBUTING.md) for how to propose a change, coding conventions, licensing, and the Developer Certificate of Origin (DCO) sign-off (`git commit -s`). For bugs and feature requests, open an issue first. The product roadmap is managed by NVIDIA, so large changes should be discussed in an issue before a pull request is opened.
To submit a pull request:
1. Create a working branch from `develop`.
2. Make and review your changes.
3. Build using the GitHub build script.
4. Run `deepstream-app` with the source4 config for a sanity test on x86 or Jetson.
5. Commit with contributor sign-off.
6. Push the working branch.
7. Open a pull request targeting `NVIDIA/DeepStream:develop`.
For details on each step, refer to the [Pull Request Guidelines](PULL_REQUEST_GUIDELINES.md).
## 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 container — prebuilt runtime image (also bundles Triton).
- DeepStream 9.2.0 and future releases: [GHCR](https://github.com/orgs/NVIDIA/packages/container/package/deepstream)
- DeepStream 9.1.0 and previous releases: [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/deepstream)
- [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
The CC-BY-4.0 AND Apache-2.0 project license is not applicable to [`src/3rdparty/`](src/3rdparty/). Those vendored sources keep their own licenses as listed below.
### 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
Vendored sources under [`src/3rdparty/`](src/3rdparty/) (x86 only) keep their upstream licenses:
| Path | License |
| ---- | ------- |
| [src/3rdparty/gst-nvunixfd/](src/3rdparty/gst-nvunixfd/) | LGPL-2.1-only ([LICENSE](src/3rdparty/gst-nvunixfd/LICENSE)); NVIDIA-authored files (`Makefile`, `gstnvipcbufferpool.*`) are Apache-2.0 |
| [src/3rdparty/gst-v4l2/](src/3rdparty/gst-v4l2/) | LGPL-2.0 ([LICENSE.gst-nvvideo4linux2](src/3rdparty/gst-v4l2/LICENSE.gst-nvvideo4linux2)); NVIDIA-authored files (`Makefile`, `README.txt`, `nvhelper_dyn.*`, `wsl_utils.*`) are Apache-2.0; `nalutils.*` and `gstv4l2h26xparser.*` are derived from GStreamer `gst-plugins-bad` codecparsers (LGPL-2.0-or-later) |
| [src/3rdparty/v4l2/](src/3rdparty/v4l2/) | LGPL-2.1-or-later (`libv4l2` / `libv4lconvert` from v4l-utils); `libv4lconvert` bundles TinyJPEG (BSD-3-Clause) and an IDCT from the Independent JPEG Group (IJG), so this software is based in part on the work of the Independent JPEG Group (notices in `THIRD_PARTY_LICENSES.txt`); NVIDIA-authored `README` files are Apache-2.0 |
A few component subdirectories retain their own vendored/upstream `LICENSE` file (in addition to being covered by `THIRD_PARTY_LICENSES.txt` and/or the root [`LICENSE`](./LICENSE)); these are kept in place for build/CI tooling that expects a local license file at that path:
| Path | License |
| ---- | ------- |
| [src/3rdparty/gst-nvunixfd/LICENSE](src/3rdparty/gst-nvunixfd/LICENSE) | LGPL-2.1-only (vendored GStreamer unixfd; also listed in `THIRD_PARTY_LICENSES.txt`) |
| [src/3rdparty/gst-v4l2/LICENSE.gst-nvvideo4linux2](src/3rdparty/gst-v4l2/LICENSE.gst-nvvideo4linux2) | LGPL-2.0 (vendored GStreamer V4L2; also listed in `THIRD_PARTY_LICENSES.txt`) |
| [tools/inference_builder/dependencies/triton-inference-server/common/LICENSE](tools/inference_builder/dependencies/triton-inference-server/common/LICENSE) | BSD-3-Clause (vendored from [triton-inference-server/common](https://github.com/triton-inference-server/common); also listed in `THIRD_PARTY_LICENSES.txt`) |
| [skills/deepstream-sop/references/license.txt](skills/deepstream-sop/references/license.txt) | Apache-2.0 (copy of the root project license, kept local for the SOP microservice's own packaging needs) |
| [tools/auto-magic-calib/LICENSE](tools/auto-magic-calib/LICENSE) | Apache-2.0 (copy of the root project license) |
| [tools/auto-magic-calib/assets/LICENSE](tools/auto-magic-calib/assets/LICENSE) | CC-BY-4.0 (copy of the root project license, scoped to the `assets/` directory) |
| [src/utils/ds3d/3rdparty/realsense2/LICENSE](src/utils/ds3d/3rdparty/realsense2/LICENSE) | Apache-2.0, Copyright 2017 Intel Corporation (vendored Intel RealSense SDK `librealsense2` headers used by the DS3D RealSense dataloader; also listed in `THIRD_PARTY_LICENSES.txt`) |