DeepStream SDK | NVIDIA Developer

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You can even deploy them on premises, on the edge, and in the cloud with just the click of a button.</p><div class="separator separator--45"></div><p id="isgvi"><a href="https://developer.nvidia.com/deepstream-getting-started" target="" title="Github Repo" class="btn btn-cta me-2 mt-2">Get Started</a><a href="https://www.nvidia.com/en-us/launchpad/ai/develop-a-custom-object-detection-model-with-tao-toolkit-and-deploy-with-deepstream/" target="_blank" title="Download Workflows" class="btn btn-cta--light btn-cta me-2 mt-2">Try on Launchpad</a></p></section><hr class="separator separator--md"><p class="mb-0"></p><section class="page__section page__second-section pb-0 pt-0"><h2 class="h--medium section__heading toc-item">What is NVIDIA DeepStream?</h2><p class="p--large text-color-gray mb-0">NVIDIA’s DeepStream SDK is a complete streaming analytics toolkit based on GStreamer for AI-based multi-sensor processing, video, audio, and image understanding. It’s ideal for vision AI developers, software partners, startups, and OEMs building IVA apps and services.<br><br>You can now create stream-processing pipelines that incorporate neural networks and other complex processing tasks like tracking, video encoding/decoding, and video rendering. These pipelines enable real-time analytics on video, image, and sensor data.<br><br></p><div id="imb4u" class="figure"><img alt="What is DeepStream and how does the software stack look like" src="https://developer.download.nvidia.com/images/deepstream/metropolis-deepstream-vision-ai-edge.jpg" class="img-fluid figure-img"><div class="figure-caption"><div id="irgxph">DeepStream is an integral part of <a href="https://www.nvidia.com/en-us/autonomous-machines/intelligent-video-analytics-platform/" id="ijkyol">NVIDIA Metropolis</a>, the platform for building end-to-end services and solutions that transform pixel and sensor data to actionable insights. </div></div></div><div class="separator separator--60 tablet-45"></div><h2 title="Benefits" class="h--medium section__heading toc-item tablet-45">Benefits<br></h2><div class="row cards-grid--60"><div class="col-lg-4 col-sm-12 grid-col col-md-6"><img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/deepstream/m48-configuration-sdk-256px-blk.png" id="i1iq9p" class="img-fluid mw-6-rem"><h3 class="h--smaller mb-0">Powerful and Flexible SDK</h3><div class="separator separator--30"></div><p class="mb-0">DeepStream SDK is ideal for a wide range of use cases across a broad set of industries.</p><div class="separator separator--30"></div></div><div class="col-lg-4 col-sm-12 grid-col col-md-6"><img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/deepstream/m48-complexity-1-256px-blk.png" id="iw48u5" class="img-fluid mw-6-rem"><h3 class="h--smaller mb-0">Multiple Programming Options</h3><div class="separator separator--30"></div><p class="mb-0">Create powerful vision AI applications using C/C++, Python, or Graph Composer’s simple and intuitive UI.</p><div class="separator separator--30"></div></div><div class="col-lg-4 col-sm-12 grid-col col-md-6"><img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/deepstream/m48-speed-256px-blk.png" id="ia9peg" class="img-fluid mw-6-rem"><h3 class="h--smaller mb-0">Real-Time Insights</h3><div class="separator separator--30"></div><p class="mb-0">Understand rich and multi-modal real-time sensor data at the edge.</p><div class="separator separator--30"></div></div><div class="col-lg-4 col-sm-12 grid-col col-md-6"><img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/deepstream/m48-microservices-2-256x-blk.png" id="ipodne" class="img-fluid mw-6-rem"><h3 class="h--smaller mb-0">Managed AI Services</h3><div class="separator separator--30"></div><p class="mb-0">Deploy AI services in cloud native containers and orchestrate them using Kubernetes. </p><div class="separator separator--30"></div></div><div class="col-lg-4 col-sm-12 grid-col col-md-6"><img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/deepstream/m48-edge-computing-256px-blk.png" id="ieofei" class="img-fluid mw-6-rem"><h3 class="h--smaller mb-0">Reduced TCO</h3><div class="separator separator--30"></div><p class="mb-0">ncrease stream density by training, adapting, and optimizing models with TAO toolkit and deploying models with DeepStream.</p><div class="separator separator--30"></div></div></div></section><hr class="separator separator--md"><section class="page__section pt-0 pb-0"><h2 title="Capabilities" class="h--medium section__heading toc-item tablet-45">Unique Capabilities<br></h2><div class="row cards__list"><div id="ix128i" class="col-lg-6 col-md-12 col-sm-12 col-xs-12"><h1 id="i5tj0g" class="h--smaller">Enjoy Seamless Development From Edge to Cloud</h1><p id="imr3nf">DeepStream gives you a faster, easier way to build seamless streaming pipelines for AI-based video, audio, and image analytics. It ships with 40+ hardware-accelerated plugins and extensions to optimize pre/post processing, inference, multi-object tracking, message brokers, and more. Plus, it offers some of the world's best-performing real-time, multi-object trackers.<br><br>Use DeepStream’s off-the-shelf containers to easily build cloud native applications that can be deployed on public and private clouds, on workstations powered with NVIDIA GPUs, or on NVIDIA Jetson. Its “develop once, deploy anywhere” approach simplifies code management and provides great scalability. The DeepStream Container Builder tool also makes it easier to build high-performance, cloud-native AI applications with NVIDIA NGC containers that are easily deployed at scale and managed with Kubernetes and Helm Charts.<br><br>DeepStream REST-APIs let you manage multiple parameters at run-time, simplifying the creation of SaaS solutions. With standard REST-API interface, you can build web portals for control and configuration or integrate into your existing applications.<br></p><a id="iv1huc" href="https://docs.nvidia.com/metropolis/deepstream/dev-guide/" target="_blank" class="link-cta text-transform-unset fw-bold">Learn More</a><div class="separator separator--30"></div></div><div id="idg8s6" class="col-lg-6 col-md-12 col-sm-12 col-xs-12"><a id="irm9b9" href="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/deepstream/embedded-deepstream-sdk3.jpg"><img alt="DeepStream helps developers build seamless streaming pipeline for AI based video analytics" src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/deepstream/embedded-deepstream-sdk3.jpg" class="img-fluid"></a></div></div><div class="row cards__list"><div class="col-lg-6 col-md-12 col-sm-12 col-xs-12"><a id="iejcv3" href="https://developer.download.nvidia.com/images/deepstream/end-to-end-vision-ai-development-ari.jpg"><img alt="DeepStream is integrated with NVIDIA Metropolis for complete end-to-end AI solutions" src="https://developer.download.nvidia.com/images/deepstream/end-to-end-vision-ai-development-ari.jpg" class="img-fluid"></a></div><div class="col-lg-6 col-md-12 col-sm-12 col-xs-12"><h1 id="i1dxdw" class="h--smaller">Build End-to-End AI Solutions<br></h1><p id="iklxkx">Speed up overall development efforts and unlock greater real-time performance by building an end-to-end vision AI system with NVIDIA Metropolis. Start with production-quality vision AI models, adapt and optimize them with TAO Toolkit, and deploy using DeepStream.<br><br>Get incredible flexibility–from rapid prototyping to full production level solutions–and choose your inference path. With native integration to <a href="https://developer.nvidia.com/nvidia-triton-inference-server" id="irycnu">NVIDIA Triton™ Inference Serve</a>r, you can deploy models in native frameworks such as PyTorch and TensorFlow for inference. Using <a href="https://developer.nvidia.com/tensorrt" id="i4xy95">NVIDIA TensorRT™</a> for high-throughput inference with options for multi-GPU, multi-stream, and batching support also helps you achieve the best possible performance.<br><br>PipeTuner 1.0, a new developer tool, now makes it easy to tune wide range of parameters to optimize AI pipelines for inference and tracking<br></p><a id="ibi4j2" href="https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_TAO_integration.html" target="_blank" class="link-cta text-transform-unset fw-bold">Learn More</a><div class="separator separator--30"></div></div></div><div class="row cards__list"><div class="col-lg-6 col-md-12 col-sm-12 col-xs-12"><h1 id="iv2o4k" class="h--smaller">Accelerate Vision AI Development<br></h1><p id="ib7j3o">The DeepStream SDK is bundled with 30+ sample applications designed to help you kick-start your development efforts. Most samples are available in C/C++, Python, and Graph Composer versions and run on both NVIDIA Jetson and dGPU platforms. With support for Windows Subsystem for Linux (WSL2), you can now develop in Windows environments without the need to access remote Linux systems.<br><br><b>DeepStream Service Maker</b> simplifies the development process by abstracting the complexities of GStreamer to easily build C++ object-oriented applications. Use Service Maker to build complete DeepStream pipelines with a few lines of code<br><br><b>DeepStream Libraries</b> powered by CV-CUDA, NvImageCodec, and PyNvVideoCodec that offers low-level GPU-accelerated operations to optimize pre and post stages of vision AI pipelines.<br><br><b>Graph Composer</b> gives DeepStream developers a powerful, low-code development option to create complex pipelines and quickly deploy them using Container Builder.</p><a href="https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_C_Sample_Apps.html" target="_blank" class="link-cta text-transform-unset fw-bold">Learn More</a><div class="separator separator--30"></div></div><div class="col-lg-6 col-md-12 col-sm-12 col-xs-12"><a id="iampv7" href="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/deepstream/industrial-inspection-2c50-p%402x.jpg"><img alt="DeepStream is bundled with multiple reference applications" src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/deepstream/industrial-inspection-2c50-p%402x.jpg" class="img-fluid"></a><p id="iulx3u"><br></p></div></div><div class="row cards__list"><div class="col-lg-6 col-md-12 col-sm-12 col-xs-12"><img alt="Use DeepStream to build next generation AI applications " src="https://developer.download.nvidia.com/images/deepstream/deepstream-gen-ai-siemens-hyundai-ari.jpg" class="img-fluid"><div id="is19zs">Siemens, HD Hyundai</div></div><div class="col-lg-6 col-md-12 col-sm-12 col-xs-12"><h1 id="iltyub" class="h--smaller">Create Next-Generation AI Applications</h1><p id="ib3pmn">Tight scheduling control, custom schedulers, and efficient resource management are all critical to integrating with deterministic systems such as robotic arms and automated quality control lines. <br><br>With the introduction of Graph eXecution Format (GXF), it’s easy to integrate with control signals that operate on a different time domain than the vision streaming sensors being processed by a DeepStream pipeline. <br><br>New reference applications help you jumpstart development of Generative AI applications. And new support for sensor fusion, BEVFusion, adds both lidar and radar inputs that can be fused with camera inputs bringing a new range of use cases for developers.<br></p><a id="ifim35" href="https://docs.nvidia.com/metropolis/deepstream/dev-guide/graphtools-docs/docs/text/GraphComposer_intro.html" target="_blank" class="link-cta text-transform-unset fw-bold">Learn More</a><div class="separator separator--30"></div></div></div><div class="row cards__list"><div class="col-lg-6 col-md-12 col-sm-12 col-xs-12"><h1 id="i4abjm" class="h--smaller">Get Production-Ready Solution for Vision AI<br></h1><p id="irjb2u">DeepStream is available as a part of NVIDIA AI Enterprise, an end-to-end, secure, cloud-native AI software platform optimized to accelerate enterprises to the leading edge of AI.<br>NVIDIA AI Enterprise delivers validation and integration for NVIDIA AI open-source software, access to AI solution workflows to speed time to production, certifications to deploy AI everywhere, and enterprise-grade support, security, and API stability to mitigate the potential risks of open-source software.<br></p><a id="itznw4" href="https://www.nvidia.com/en-us/data-center/products/ai-enterprise/" target="_blank" class="link-cta text-transform-unset fw-bold">Learn More</a><div class="separator separator--30"></div></div><div class="col-lg-6 col-md-12 col-sm-12 col-xs-12"><img alt="DeepStream is part of NVIDIA AI Enterprise to help deploy AI anywhere." src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/nvidia-ai-enterprise-630x354_1.jpg" class="img-fluid"></div></div></section><section class="page__section pt-0 pb-0"><hr class="separator separator--md"><h2 title="Programming Options" class="h--medium section__heading toc-item tablet-45">Explore Multiple Programming Options</h2><div class="row cards__list"><div class="col-xl-4 col-lg-4 col-md-12 col-sm-12"><div class="card-wrapper"><div class="card"><div class="card__content"><div class="card__text"><h3 class="h--smaller txt-clr--blck mb-0">C/C++</h3><div class="separator separator--30"></div><p class="mb-0">Create applications in C/C++, interact directly with GStreamer and DeepStream plug-ins, and use reference applications and templates.</p><div class="separator separator--30"></div></div><div class="card__cta"><a href="https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_C_Sample_Apps.html" target="_blank" class="link-cta text-transform-unset fw-bold">Learn More About C/C++</a></div></div></div></div></div><div class="col-xl-4 col-lg-4 col-md-12 col-sm-12"><div class="card-wrapper"><div class="card"><div class="card__content"><div class="card__text"><h3 class="h--smaller txt-clr--blck mb-0">Python</h3><div class="separator separator--30"></div><p class="mb-0">DeepStream pipelines can be constructed using Gst Python, the GStreamer framework's Python bindings. The source code for the binding and Python sample applications are available on GitHub.</p><div class="separator separator--30"></div></div><div class="card__cta"><a href="https://github.com/NVIDIA-AI-IOT/deepstream_python_apps" target="_blank" class="link-cta text-transform-unset fw-bold">Learn More About Python</a></div></div></div></div></div><div class="col-xl-4 col-lg-4 col-md-12 col-sm-12"><div class="card-wrapper"><div class="card"><div class="card__content"><div class="card__text"><h3 class="h--smaller txt-clr--blck mb-0">Graph Composer<br></h3><div class="separator separator--30"></div><p class="mb-0">Graph Composer is a low-code development tool that enhances the DeepStream user experience. Using a simple, intuitive UI, processing pipelines are constructed with drag-and-drop operations.<br></p><div class="separator separator--30"></div></div><div class="card__cta"><a href="https://youtu.be/zotnq7UNOPM" target="_blank" class="link-cta text-transform-unset fw-bold">Learn More About <br>Graph Composer</a></div></div></div></div></div></div></section><hr class="separator separator--md"><section class="page__section pt-0 pb-0"><h2 title="Performance" class="h--medium section__heading toc-item">Improve Accuracy and Real-Time Performance<br></h2></section> <div class="custom-html-wrapper"> <div class="custom-html-wrapper__code"><div class="nv-table "> <table class="table table-responsive table-bordered"> <thead> <tr style="background-color:#EEE;border-bottom-color:#666;"> <th colspan="5" style=""></th> <th style="vertical-align:middle;">Jetson Orin Nano</th> <th colspan="3" style="vertical-align:middle;width:100px;">Jetson Orin NX</th> <th colspan="3" style="vertical-align:middle;width:100px;">Jetson Orin AGX™</th> <th style="vertical-align:middle;width:100px;">T4</th> <th style="vertical-align:middle;width:100px;">A2</th> <th style="vertical-align:middle;width:100px;">A10</th> <th style="vertical-align:middle;width:100px;">A30</th> <th style="vertical-align:middle;width:100px;">A100</th> <th style="vertical-align:middle;width:100px;">H100</th> <th style="vertical-align:middle;width:100px;">L40</th> <th style="vertical-align:middle;width:100px;">L4</th> <th style="vertical-align:middle;width:100px;">Quadro (A6000)</th> <th style="vertical-align:middle;width:100px;">A4000</th> <th style="vertical-align:middle;width:100px;">L4000</th> <th style="vertical-align:middle;width:100px;">ARM SBSA</th> </tr> </thead> <thead> <tr style="background-color:#f7f7f7;border-bottom-color:#666;"> <th style="vertical-align:middle;width:100px;">Application</th> <th style="vertical-align:middle;width:100px;">Models</th> <th style="vertical-align:middle;width:100px;">Tracker</th> <th style="vertical-align:middle;width:100px;">Infer Resolution</th> <th style="vertical-align:middle;width:100px;">Precision</th> <th style="vertical-align:middle;width:100px;">GPU</th> <th style="vertical-align:middle;width:100px;">GPU</th> <th style="vertical-align:middle;width:100px;">DLA1</th> <th style="vertical-align:middle;width:100px;">DLA2</th> <th style="vertical-align:middle;width:100px;">GPU</th> <th style="vertical-align:middle;width:100px;">DLA1</th> <th style="vertical-align:middle;width:100px;">DLA2</th> <th style="vertical-align:middle;width:100px;">GPU </th> <th style="vertical-align:middle;width:100px;">GPU </th> <th style="vertical-align:middle;width:100px;">GPU </th> <th style="vertical-align:middle;width:100px;">GPU </th> <th style="vertical-align:middle;width:100px;">GPU </th> <th style="vertical-align:middle;width:100px;">GPU </th> <th style="vertical-align:middle;width:100px;">GPU </th> <th style="vertical-align:middle;width:100px;">GPU </th> <th style="vertical-align:middle;width:100px;">GPU </th> <th style="vertical-align:middle;width:100px;">GPU </th> <th style="vertical-align:middle;width:100px;">GPU </th> <th style="vertical-align:middle;width:100px;">GPU </th> </tr> </thead> <tbody> <tr> <td rowspan="3" style="vertical-align:middle;width:100px;"> People Detect </td> <td style="vertical-align:middle;width:100px;"> PeopleNet-ResNet34 (v2.3.3) </td> <td style="vertical-align:middle;width:100px;"> No Tracker </td> <td style="vertical-align:middle;width:100px;"> 960x544 </td> <td style="vertical-align:middle;width:100px;"> INT8 </td> <td style="vertical-align:middle;width:100px;"> 256 </td> <td style="vertical-align:middle;width:100px;"> 372 </td> <td style="vertical-align:middle;width:100px;"> 175 </td> <td style="vertical-align:middle;width:100px;"> 175 </td> <td style="vertical-align:middle;width:100px;"> 970 </td> <td style="vertical-align:middle;width:100px;"> 329 </td> <td style="vertical-align:middle;width:100px;"> 329 </td> <td style="vertical-align:middle;width:100px;"> 912 </td> <td style="vertical-align:middle;width:100px;"> 610 </td> <td style="vertical-align:middle;width:100px;"> 2059 </td> <td style="vertical-align:middle;width:100px;"> 3273 </td> <td style="vertical-align:middle;width:100px;"> 4952 </td> <td style="vertical-align:middle;width:100px;"> 6920 </td> <td style="vertical-align:middle;width:100px;"> 4443 </td> <td style="vertical-align:middle;width:100px;"> 1674 </td> <td style="vertical-align:middle;width:100px;"> 2787 </td> <td style="vertical-align:middle;width:100px;"> 1282 </td> <td style="vertical-align:middle;width:100px;"> 1512 </td> <td style="vertical-align:middle;width:100px;"> 6977 </td> </tr> <tr> <td style="vertical-align:middle;width:100px;"> PeopleNet-ResNet34 (v2.3.3) </td> <td style="vertical-align:middle;width:100px;"> NvDCF (Accuracy) </td> <td style="vertical-align:middle;width:100px;"> 960x544 </td> <td style="vertical-align:middle;width:100px;"> INT8 </td> <td style="vertical-align:middle;width:100px;"> 82 </td> <td style="vertical-align:middle;width:100px;"> 128 </td> <td style="vertical-align:middle;width:100px;"> 77 </td> <td style="vertical-align:middle;width:100px;"> 77 </td> <td style="vertical-align:middle;width:100px;"> 318 </td> <td style="vertical-align:middle;width:100px;"> 196 </td> <td style="vertical-align:middle;width:100px;"> 196 </td> <td style="vertical-align:middle;width:100px;"> 429 </td> <td style="vertical-align:middle;width:100px;"> 295 </td> <td style="vertical-align:middle;width:100px;"> 1009 </td> <td style="vertical-align:middle;width:100px;"> 1229 </td> <td style="vertical-align:middle;width:100px;"> 2040 </td> <td style="vertical-align:middle;width:100px;"> 2936 </td> <td style="vertical-align:middle;width:100px;"> 1870 </td> <td style="vertical-align:middle;width:100px;"> 701 </td> <td style="vertical-align:middle;width:100px;"> 1301 </td> <td style="vertical-align:middle;width:100px;"> 746 </td> <td style="vertical-align:middle;width:100px;"> 623 </td> <td style="vertical-align:middle;width:100px;"> 3613 </td> </tr> <tr> <td style="vertical-align:middle;width:100px;"> PeopleNet-ResNet34 (v2.3.3) </td> <td style="vertical-align:middle;width:100px;"> NvDCF (Performance) </td> <td style="vertical-align:middle;width:100px;"> 960x544 </td> <td style="vertical-align:middle;width:100px;"> INT8 </td> <td style="vertical-align:middle;width:100px;"> 215 </td> <td style="vertical-align:middle;width:100px;"> 315 </td> <td style="vertical-align:middle;width:100px;"> 170 </td> <td style="vertical-align:middle;width:100px;"> 170 </td> <td style="vertical-align:middle;width:100px;"> 625 </td> <td style="vertical-align:middle;width:100px;"> 310 </td> <td style="vertical-align:middle;width:100px;"> 310 </td> <td style="vertical-align:middle;width:100px;"> 866 </td> <td style="vertical-align:middle;width:100px;"> 568 </td> <td style="vertical-align:middle;width:100px;"> 2063 </td> <td style="vertical-align:middle;width:100px;"> 2806 </td> <td style="vertical-align:middle;width:100px;"> 4250 </td> <td style="vertical-align:middle;width:100px;"> 6103 </td> <td style="vertical-align:middle;width:100px;"> 4278 </td> <td style="vertical-align:middle;width:100px;"> 1563 </td> <td style="vertical-align:middle;width:100px;"> 2855 </td> <td style="vertical-align:middle;width:100px;"> 1277 </td> <td style="vertical-align:middle;width:100px;"> 1413 </td> <td style="vertical-align:middle;width:100px;"> 5788 </td> </tr> <tr> <td style="vertical-align:middle;width:100px;"> License Plate Recognition </td> <td style="vertical-align:middle;width:100px;"> TrafficCamNet <br> LPDNet <br> LPRNet </td> <td style="vertical-align:middle;width:100px;"> NvDCF </td> <td style="vertical-align:middle;width:100px;"> 960x544 <br> 640x480 <br> 96x48 </td> <td style="vertical-align:middle;width:100px;"> INT8 <br> INT8 <br> FP16 </td> <td style="vertical-align:middle;width:100px;"> 120 </td> <td style="vertical-align:middle;width:100px;"> 180 </td> <td style="vertical-align:middle;width:100px;"> - </td> <td style="vertical-align:middle;width:100px;"> - </td> <td style="vertical-align:middle;width:100px;"> 370 </td> <td style="vertical-align:middle;width:100px;"> - </td> <td style="vertical-align:middle;width:100px;"> - </td> <td style="vertical-align:middle;width:100px;"> 382 </td> <td style="vertical-align:middle;width:100px;"> 253 </td> <td style="vertical-align:middle;width:100px;"> 1071 </td> <td style="vertical-align:middle;width:100px;"> 1327 </td> <td style="vertical-align:middle;width:100px;"> 2150 </td> <td style="vertical-align:middle;width:100px;"> 2801 </td> <td style="vertical-align:middle;width:100px;"> 2280 </td> <td style="vertical-align:middle;width:100px;"> 741 </td> <td style="vertical-align:middle;width:100px;"> 1404 </td> <td style="vertical-align:middle;width:100px;"> 788 </td> <td style="vertical-align:middle;width:100px;"> 670 </td> <td style="vertical-align:middle;width:100px;"> N/A </td> </tr> <tr> <td style="vertical-align:middle;width:100px;"> 3D Body Pose Estimation </td> <td style="vertical-align:middle;width:100px;"> PeopleNet-ResNet34 BodyPose3D </td> <td style="vertical-align:middle;width:100px;"> NvDCF </td> <td style="vertical-align:middle;width:100px;"> 960x544 <br> 192x256 </td> <td style="vertical-align:middle;width:100px;"> INT8 <br> FP16 </td> <td style="vertical-align:middle;width:100px;"> 28 </td> <td style="vertical-align:middle;width:100px;"> -40 </td> <td style="vertical-align:middle;width:100px;"> - </td> <td style="vertical-align:middle;width:100px;"> - </td> <td style="vertical-align:middle;width:100px;"> 76 </td> <td style="vertical-align:middle;width:100px;"> - </td> <td style="vertical-align:middle;width:100px;"> - </td> <td style="vertical-align:middle;width:100px;"> 101 </td> <td style="vertical-align:middle;width:100px;"> 67 </td> <td style="vertical-align:middle;width:100px;"> 160 </td> <td style="vertical-align:middle;width:100px;"> 128 </td> <td style="vertical-align:middle;width:100px;"> 151 </td> <td style="vertical-align:middle;width:100px;"> 255 </td> <td style="vertical-align:middle;width:100px;"> 226 </td> <td style="vertical-align:middle;width:100px;"> 200 </td> <td style="vertical-align:middle;width:100px;"> 235 </td> <td style="vertical-align:middle;width:100px;"> 148 </td> <td style="vertical-align:middle;width:100px;"> 104 </td> <td style="vertical-align:middle;width:100px;"> 313 </td> </tr> <tr> <td style="vertical-align:middle;width:100px;"> Action Recognition </td> <td style="vertical-align:middle;width:100px;"> ActionRecognitionNet (3DConv) </td> <td style="vertical-align:middle;width:100px;"> No Tracker </td> <td style="vertical-align:middle;width:100px;"> 224x224x3x32 </td> <td style="vertical-align:middle;width:100px;"> FP16 </td> <td style="vertical-align:middle;width:100px;"> 34 </td> <td style="vertical-align:middle;width:100px;"> 51 </td> <td style="vertical-align:middle;width:100px;"> - </td> <td style="vertical-align:middle;width:100px;"> - </td> <td style="vertical-align:middle;width:100px;"> 147 </td> <td style="vertical-align:middle;width:100px;"> - </td> <td style="vertical-align:middle;width:100px;"> - </td> <td style="vertical-align:middle;width:100px;"> 173 </td> <td style="vertical-align:middle;width:100px;"> 74 </td> <td style="vertical-align:middle;width:100px;"> 450 </td> <td style="vertical-align:middle;width:100px;"> 552 </td> <td style="vertical-align:middle;width:100px;"> 996 </td> <td style="vertical-align:middle;width:100px;"> 1270 </td> <td style="vertical-align:middle;width:100px;"> 870 </td> <td style="vertical-align:middle;width:100px;"> 313 </td> <td style="vertical-align:middle;width:100px;"> 638 </td> <td style="vertical-align:middle;width:100px;"> 319 </td> <td style="vertical-align:middle;width:100px;"> 300 </td> <td style="vertical-align:middle;width:100px;"> 1910 </td> </tr> </tbody> </table> </div></div> <div data-react-class="CustomHtml" data-react-props="{"preview":false}"></div> </div> <div id="ixorip"><br>RTX GPUs performance is only reported for flagship product(s). All SKUs support DeepStream.<br><br>The DeepStream SDK lets you apply AI to streaming video and simultaneously optimize video decode/encode, image scaling, and conversion and edge-to-cloud connectivity for complete end-to-end performance optimization.<br><br>To learn more about the performance using DeepStream, check the <a href="https://www.google.com/url?q=https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Performance.html&sa=D&source=docs&ust=1715369000151037&usg=AOvVaw0yBjmmdaH_95_HgwtP21jm" id="iywt4t" target="_blank">documentation</a>.</div><section class="page__section pt-0 pb-0"><hr class="separator separator--md"><h2 title="Customer Stories" class="h--medium section__heading toc-item tablet-45">Read Customer Stories<br></h2><div class="row cards__list"><div class="col-xl-4 col-lg-4 col-md-12 col-sm-12"><div class="card-wrapper"><div class="card"><img alt="Industry.AI Customer Story Pls ta" src="https://developer.download.nvidia.com/images/deepstream/bengaluru-airport-ai-ari.jpg" class="img-fluid"><div class="card__content"><div class="card__text"><h3 class="h--smaller txt-clr--blck mb-0">Optimizing Operations at Bengaluru Airport<br></h3><div class="separator separator--30"></div><p class="mb-0">Industry.AI used the NVIDIA Metropolis stack, including DeepStream, to increase the safety and efficiency of the airport. Using vision AI, it was able to track abandoned baggage, flag long passenger queues, and alert security teams of potential issues.</p><div class="separator separator--30"></div></div><div class="card__cta"><a href="https://blogs.nvidia.com/blog/bengaluru-airport-vision-ai/" target="_blank" class="link-cta text-transform-unset fw-bold">Read the Blog<i class="chevron-right"></i></a></div></div></div></div></div><div class="col-xl-4 col-lg-4 col-md-12 col-sm-12"><div class="card-wrapper"><div class="card"><img alt="KoiReader Customer Story" src="https://developer.download.nvidia.com/images/deepstream/koireader-distribution-center-operation-ari.jpg" class="img-fluid"><div class="card__content"><div class="card__text"><h3 class="h--smaller txt-clr--blck mb-0">Enhancing Distribution Center Operation </h3><div class="separator separator--30"></div><p class="mb-0">KoiReader developed an AI-powered machine vision solution using NVIDIA developer tools that included DeepStream SDK to help PepsiCo achieve precision and efficiency in dynamic distribution environments.</p><div class="separator separator--30"></div></div><div class="card__cta"><a href="https://resources.nvidia.com/l/en-us-metropolis-software-success-stories?overlay_url=https%3A%2F%2Fresources.nvidia.com%2Fen-us-metropolis-software-success-stories%2Fpepsi-koivision%3Flx%3DFjSmH9" target="_blank" class="link-cta text-transform-unset fw-bold">Learn More About KoiReader <i class="chevron-right"></i></a></div></div></div></div></div><div class="col-xl-4 col-lg-4 col-md-12 col-sm-12"><div class="card-wrapper"><div class="card"><img alt="YMA Customer Story Please take the image from the video " src="https://developer.download.nvidia.com/images/deepstream/ai-smart-spaces-ari.jpg" class="img-fluid"><div class="card__content"><div class="card__text"><h3 class="h--smaller txt-clr--blck mb-0">Scaling AI-Powered Smart Spaces</h3><div class="separator separator--30"></div><p class="mb-0">FYMA used NVIDIA DeepStream and NVIDIA Triton™ to improve AI-powered space analytics with frame rates exceeding previous benchmarks by 10X and accuracy by 3X. </p><div class="separator separator--30"></div></div><div class="card__cta"><a href="https://www.youtube.com/watch?v=GPsQAKq02lc" target="_blank" class="link-cta text-transform-unset fw-bold">Learn More</a></div></div></div></div></div></div></section><hr class="separator separator--md"><section class="page__section pt-0 pb-0"><h2 title="FAQ" class="h--medium section__heading">General FAQ</h2><div class="Accordion-izax6x-child visually-hidden"><div class="AccordionItem-i04rnk-child visually-hidden"><p id="i1ph19">DeepStream is a closed-source SDK. Note that sources for all reference applications and several plugins are available.</p></div><div data-react-class="AccordionItem" data-react-props="{"title":"Is DeepStream open-source?","id":"i04rnk","size":"smallest","childSelector":".AccordionItem-i04rnk-child"}" data-react-cache-id="AccordionItem-i04rnk"></div><div class="AccordionItem-iczu8s-child visually-hidden"><p id="isu07k">The DeepStream SDK can be used to build end-to-end AI-powered applications to analyze video and sensor data. Some popular use cases are retail analytics, parking management, managing logistics, optical inspection, robotics, and sports analytics.</p></div><div data-react-class="AccordionItem" data-react-props="{"title":"What applications are deployable using the DeepStream SDK?","size":"smallest","id":"iczu8s","childSelector":".AccordionItem-iczu8s-child"}" data-react-cache-id="AccordionItem-iczu8s"></div><div class="AccordionItem-iynjep-child visually-hidden"><p id="i18pl4">See the <a href="https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Quickstart.html#platform-and-os-compatibility" id="inj2bv" target="_blank">Platforms and OS compatibility table</a>.</p></div><div data-react-class="AccordionItem" data-react-props="{"title":"What platforms and OS are compatible with DeepStream?","size":"smallest","id":"iynjep","childSelector":".AccordionItem-iynjep-child"}" data-react-cache-id="AccordionItem-iynjep"></div><div class="AccordionItem-ir17hq-child visually-hidden"><p id="iawfq4">Yes, that’s now possible with the integration of the Triton Inference server. Also with DeepStream 6.1.1, applications can communicate with independent/remote instances of Triton Inference Server using gPRC.</p></div><div data-react-class="AccordionItem" data-react-props="{"title":"Can I run my models natively in TensorFlow or PyTorch with DeepStream?","size":"smallest","id":"ir17hq","childSelector":".AccordionItem-ir17hq-child"}" data-react-cache-id="AccordionItem-ir17hq"></div><div class="AccordionItem-iwrtb6-child visually-hidden"><p id="isv86i">DeepStream supports several popular networks out of the box. For instance, DeepStream supports MaskRCNN. Also, DeepStream ships with an example to run the popular YOLO models, FasterRCNN, SSD and RetinaNet.</p></div><div data-react-class="AccordionItem" data-react-props="{"title":"How do I deploy models from TAO Toolkit with DeepStream?","size":"smallest","id":"iwrtb6","childSelector":".AccordionItem-iwrtb6-child"}" data-react-cache-id="AccordionItem-iwrtb6"></div><div class="AccordionItem-icerpm-child visually-hidden"><p id="i7a55a">Yes, DS 6.0 or later supports the Ampere architecture</p></div><div data-react-class="AccordionItem" data-react-props="{"title":"Is DeepStream supported on NVIDIA Ampere architecture GPUs?","size":"smallest","id":"icerpm","childSelector":".AccordionItem-icerpm-child"}" data-react-cache-id="AccordionItem-icerpm"></div><div class="AccordionItem-iwpzgh-child visually-hidden"><p id="ik97vj">Yes, audio is supported with DeepStream SDK 6.1.1. To get started, download the software and review the reference audio and Automatic Speech Recognition (ASR) applications. Learn more by reading the <a href="https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvdsasr.html" id="ieda2a" target="_blank">ASR DeepStream Plugin</a></p></div><div data-react-class="AccordionItem" data-react-props="{"title":"Is audio analytics supported with DeepStream SDK?","size":"smallest","id":"iwpzgh","childSelector":".AccordionItem-iwpzgh-child"}" data-react-cache-id="AccordionItem-iwpzgh"></div></div><div data-react-class="Accordion" data-react-props="{"id":"izax6x","childSelector":".Accordion-izax6x-child"}" data-react-cache-id="Accordion-izax6x"></div><div class="separator separator--60"><div data-react-class="Accordion" data-react-props="{"id":"ijejug"}" data-react-cache-id="Accordion-ijejug"></div></div></section><section class="page__section page__section--light-gray page__last-section page__cta-section"><p class="p--large lead text-center text-color-gray mb-0">Build high-performance vision AI apps and services using DeepStream SDK.<br></p><div class="separator separator--60"></div><p class="text-center mb-0"><a href="https://developer.nvidia.com/deepstream-getting-started" class="btn btn-cta">Get Started</a></p></section></main></div><div class="col-xl-1 col-separator"></div><div class="col-xl-2 col-lg-3 col-md-12 col-sm-12 col-sidebar"><aside class="page__sidebar with-sticky-nav"><div class="page-navigation-container"><div class="page-quick-links"><p class="p--small page-quick-links__header">Quick Links</p><ul><li><a href="https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/deepstream-sdk/15" class="link-cta page-quick-links__link">Forum</a></li><li><a href="https://catalog.ngc.nvidia.com/orgs/nvidia/collections/deepstream_sdk" target="_blank" class="link-cta page-quick-links__link">Download DeepStream</a></li><li><a href="https://www.nvidia.com/en-us/launchpad/ai/build-vision-ai-pipeline-with-deepstream-and-python" target="_blank" class="link-cta page-quick-links__link">Try on LaunchPad</a></li></ul></div><hr><div data-react-class="PageNavigation" data-react-props="{"draggable":"true","editable":"true","id":"iysymu"}" data-react-cache-id="PageNavigation-iysymu"></div></div></aside></div></div></div><div class="separator separator--90 phone-0"></div></div> </div> <div id='footer' class='mt-auto'></div> <script type="text/javascript"> (() => { const handleQuotesBlock = (quotesBlock, idx) => { const blockquotes = quotesBlock.querySelectorAll('blockquote'); 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