NVIDIA 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 (documentation: CC-BY-4.0; source code: Apache-2.0). GitHub Release assets specific license details can be referred at Release Asset License under DeepStream Release Assets. The project is actively maintained, but currently not accepting code contributions — refer to the Contribution Guidelines for more details.
This repository contains the complete source code for DeepStream 9.1.
Components (src/):
src/gst-plugins/— DeepStream GStreamer plugin sourcessrc/utils/— utility library sourcessrc/apps/sample_apps/— GStreamer-based sample applicationssrc/apps/reference_apps/— advanced reference applicationssrc/apps/tao_apps/— TAO-model integration appssrc/service-maker/— Service Maker C++/Python SDK and apps
Tools (tools/):
inference_builder— visual inference pipeline builderauto-magic-calib— camera auto-calibration toolyolo_deepstream— YOLO + TensorRT integrationsam2-onnx-tensorrt— SAM2 ONNX-to-TensorRT conversiondeepstream-host-provisioning— Ansible playbooks to provision Ubuntu hosts (x86_64 & ARM SBSA) with the full NVIDIA compute stack (driver, CUDA, cuDNN, TensorRT, Docker) and DeepStream dependenciesdeepstream-otelcol— OpenTelemetry Collector setup for streaming metrics
AI agent skills (skills/, for Claude Code & compatible coding agents):
deepstream-dev— general DeepStream developmentdeepstream-generate-pipeline— interactivegst-launchpipeline builderdeepstream-profile-pipeline— Nsight Systems profiling & config derivationdeepstream-sop— SOP step-sequence compliance microservicedeepstream-import-vision-model— autonomous vision-model onboardingdeepstream-run-mv3dt— run the MV3DT reference app on sample or custom datasetsamc-setup-calibration-stack— launch the AutoMagicCalib microservice and UIamc-run-sample-calibration— verify AMC with the bundled sample datasetamc-run-video-calibration— calibrate user-provided MP4 camera videosamc-run-rtsp-calibration— calibrate live RTSP camera streams through VIOS capture
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.
-
DeepStream 9.1 proprietary runtime — the prebuilt libraries and sample data are published as GitHub Release assets.
bash build/build.shautomatically downloads the packages intoartifacts/before the build process starts.Note: After a successful build,
artifacts/is removed automatically. Pass--keep-assetsto 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.shis not required inside the container. If you modify an open-source component, runmake && make installin that component's directory to deploy the updated binaries/libs. See build/BUILD.md.
# 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 DeepStreamSee 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. |
# Build. The script prompts for sudo only when installing to system paths.
bash build/build.shAfter 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:
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
- Detailed API reference: DeepStream SDK Developer 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.
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.
# One-time NGC login (get an API key from https://ngc.nvidia.com)
docker login nvcr.io # username: $oauthtoken, password: <NGC API key>
# Pull the image (use 9.1-triton-arm-sbsa instead on SBSA / DGX Spark)
docker pull nvcr.io/nvidia/deepstream:9.1-triton-multiarch
# Launch with display (use 'fakesink' in your pipeline for headless)
export DISPLAY=:0 && xhost +
docker run -it --rm --gpus all --network=host \
-e DISPLAY=$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.txtPrerequisites: Docker (docker-ce), the NVIDIA Container Toolkit, 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.
| Page | Description |
|---|---|
| Overview & Architecture | What DeepStream is, key features, and the GStreamer-based pipeline architecture. |
| Release Notes | What's new in this release, supported platforms, and known issues. |
| Installation | Step-by-step installation of the NVIDIA compute stack and DeepStream SDK on x86 and Jetson. |
| Docker Containers | Available DeepStream NGC container images (incl. Triton variants) and how to pull and run them. |
| DeepStream Samples | Reference walkthroughs of the bundled C/C++ sample applications and what each one demonstrates. |
| Reference Applications | Advanced GitHub-hosted reference apps demonstrating specialized end-to-end pipelines. |
| TAO Apps | DeepStream sample apps integrating TAO-trained models (detection, classification, segmentation, pose, LPR). |
| DeepStream Plugins | Reference for every DeepStream GStreamer plugin — properties, pad caps, and usage. |
| Service Maker | Build DeepStream pipelines declaratively with the C++ / Python Service Maker SDK. |
| Inference Builder | Compose and configure DeepStream inference pipelines visually with the Inference Builder tool. |
| Auto Magic Calib | Automatic camera calibration tool for multi-camera DeepStream deployments. |
| DeepStream Libraries | DeepStream utility and helper libraries bundled as the deepstream_libraries submodule. |
| DeepStream Coding Agent | Use the bundled skills/ with Claude Code and other AI coding assistants to generate DeepStream pipelines. |
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
├── 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
├── 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.
Summary of benchmarks; for detailed performance numbers and hardware used, refer to the DeepStream Performance Guide.
- Releases / Changelog: CHANGELOG.md
The following .deb and .tar artifacts are published as GitHub Release assets. build/build.sh downloads them automatically into artifacts/ before installation:
| 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. |
| 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. |
| Asset | Architecture | Description |
|---|---|---|
deepstream_libraries-1.4-cp312-cp312-linux_x86_64.whl |
x86_64 | DeepStream Libraries Python wheel (CPython 3.12) for x86. |
Release assets can be downloaded via curl or wget from the command line.
curl
curl -LO 'https://github.com/NVIDIA/DeepStream/releases/download/v9.1.0/deepstream-9.1_9.1.0-1_amd64.deb'wget
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.debwith the name of the asset you want to download.
DeepStream Docker images are released through NGC. Browse and pull the images from the DeepStream NGC container catalog.
NOTE: For
docker loginanddocker pullcommand details, along with the full list of available image tags, refer to the DeepStream NGC container catalog.
This project is currently not accepting contributions. The product roadmap is managed internally by NVIDIA.
- Vulnerability disclosure: File an issue at Issues.
- Do not file public issues for security reports.
- Level: Maintained
- How to get help: Issues / DeepStream SDK Developer Forum
- Filing a bug? Use the bug report template and include: platform (x86 / Jetson / SBSA), DeepStream version, install method (Debian / Docker / source), and the output of
scripts/print_env.sh.
Join the DeepStream community to ask questions, share feedback, and report issues.
- DeepStream NGC container — prebuilt runtime image (also bundles Triton).
- NVIDIA TAO Toolkit — model training/optimization, source of the TAO sample apps.
- GStreamer documentation — underlying framework.
This project is licensed under CC-BY-4.0 AND Apache-2.0.
- SPDX-License-Identifier: CC-BY-4.0 AND Apache-2.0
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, refer the "LicenseAgreement" included within all GitHub Release Assets packages.
Refer THIRD_PARTY_LICENSES for all 3rd Party OSS licenses and Notices