Introduction

We have introduced PipeTuner Collection. PipeTuner is an automatic tuning tool that efficiently explores the parameter space and finds the optimal parameters for the pipelines, which yields the highest KPI on the dataset provided by the user.\nThis resource page contains the documentation, sample data and config files to run PipeTuner. Visit \"File Browser\" to download them.

Getting Started

Users need to follow all the steps in this section to start tuning.

System Requirements

PipeTuner requires the following components on an x86_64 system:

OS Ubuntu 22.04
NVIDIA driver 535.104 or 535.161
Docker (need to run without sudo privilege)
NVIDIA container toolkit

NGC Setup

Users need to follow below steps to sign in to an NGC account and get an API key.

Visit NGC sign in page, Enter your email address and click Next, or Create an Account.
Choose your organization when prompted for Organization/Team. DeepStream users may use any organization and team; Metropolis Microservice users need to select nv-mdx/mdx-v2-0; Click Sign In.
Generate an API key following the instructions.
Log in to the NGC docker registry (nvcr.io) and enter the following credentials, where YOUR_NGC_API_KEY corresponds to the key you generated from the previous step.

$ docker login nvcr.io\nUsername: \"$oauthtoken\"\nPassword: \"YOUR_NGC_API_KEY\"\n

Metropolis Microservice users need to install NGC CLI following the instructions, and set ngc config as below. DeepStream users can skip this step.

$ ngc config set\nEnter API key: \"YOUR_NGC_API_KEY\"\nEnter org: nfgnkvuikvjm\nEnter team: mdx-v2-0\n

Sample Data Setup

The sample data consists of a mini-synthetic dataset with eight 1-minute streams and config files for tuning. You can download the sample files pipe-tuner-sample.zip by clicking “Download” from this page.

Once you download the sample file, unzip the file and run setup.sh to finish sample data for either DeepStream or Metropolis Microservices.

$ unzip pipe-tuner-sample.zip\n$ cd pipe-tuner-sample/scripts\n\n$ # DeepStream or Metropolis Microservices users should run only one of the following two commands based on their usage \n$ bash setup.sh deepstream            # DeepStream users\n\n$ bash setup.sh metropolis            # Metropolis Microservices users\n

DeepStream users should see docker images like below.

$ docker images # bash setup.sh deepstream \nREPOSITORY                                              TAG                    \nnvcr.io/nvidia/pipetuner                                1.0\nnvcr.io/nvidia/deepstream                               7.0-triton-multiarch\n

Also, model files should be under the ‘models’ folder. They will be mapped into DeepStream containers during tuning.

$ ls ../models\nlabels.txt  resnet34_peoplenet_int8.etlt  resnet34_peoplenet_int8.txt  resnet50_market1501_aicity156.onnx\n

Metropolis users should see docker images like below. The ‘models’ folder is empty because default models in mdx-perception container will be used.

$ docker images # bash setup.sh metropolis\nREPOSITORY                                              TAG                    \nnvcr.io/nvidia/pipetuner                                1.0\nnvcr.io/nfgnkvuikvjm/mdx-v2-0/mdx-perception            2.1\n

The final directory under pipe-tuner-sample is like:

pipe-tuner-sample\n├── configs\n│   ├── config_CameraMatrix\n│   ├── config_GuiTool\n│   ├── config_MTMC\n│   ├── config_PGIE\n│   ├── config_PipeTuner\n│   └── config_Tracker\n├── data\n│   ├── SDG_1min_utils  \n│   └── SDG_1min_videos\n├── models\n├── ngc_download\n├── multi-camera-tracking (only for Metropolis Microservice)\n└── scripts\n

License

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Asset	Applicable EULA	Notes
PipeTuner Container	NVIDIA_PipeTuner_EULA	A copy of the license is available in the following path inside the container: `/pipe-tuner/NVIDIA_PipeTuner_EULA.pdf`

NOTE: By pulling, downloading, or using PipeTuner, you accept the terms and conditions of the EULA licenses listed above.

For DeepStream SDK and Metropolis Microservices, please refer to their own licenses.

Ethical AI

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.

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PipeTuner is an automatic tuning tool that efficiently explores the parameter space and finds the optimal parameters for the pipelines, which yields the highest KPI on the dataset provided by the user. \nThis resource page contains the documentation, sample data and config files to run PipeTuner. Visit \"File Browser\" to download them.\n\n# Getting Started\nUsers need to follow all the steps in this section to start tuning.\n\n## System Requirements\nPipeTuner requires the following components on an x86_64 system:\n- OS Ubuntu 22.04\n- NVIDIA driver 535.104 or 535.161\n- Docker (need to run without sudo privilege)\n- NVIDIA container toolkit \n\n## NGC Setup\nUsers need to follow below steps to sign in to an NGC account and get an API key.\n\n1. Visit NGC sign in page, Enter your email address and click Next, or Create an Account.\n2. Choose your organization when prompted for Organization/Team. DeepStream users may use any organization and team; Metropolis Microservice users need to select nv-mdx/mdx-v2-0; Click Sign In.\n3. Generate an API key following the instructions.\n4. Log in to the NGC docker registry (nvcr.io) and enter the following credentials, where YOUR_NGC_API_KEY corresponds to the key you generated from the previous step.\n\n ```\n $ docker login nvcr.io\n Username: \"$oauthtoken\"\n Password: \"YOUR_NGC_API_KEY\"\n ```\n\n5. Metropolis Microservice users need to install NGC CLI following the instructions, and set ngc config as below. DeepStream users can skip this step.\n ```\n $ ngc config set\n Enter API key: \"YOUR_NGC_API_KEY\"\n Enter org: nfgnkvuikvjm\n Enter team: mdx-v2-0\n ```\n\n## Sample Data Setup\nThe sample data consists of a mini-synthetic dataset with eight 1-minute streams and config files for tuning. You can download the sample files **pipe-tuner-sample.zip** by clicking “Download” from this page.\n\nOnce you download the sample file, unzip the file and run setup.sh to finish sample data for either DeepStream or Metropolis Microservices. \n```\n$ unzip pipe-tuner-sample.zip\n$ cd pipe-tuner-sample/scripts\n\n$ # DeepStream or Metropolis Microservices users should run only one of the following two commands based on their usage \n$ bash setup.sh deepstream # DeepStream users\n\n$ bash setup.sh metropolis # Metropolis Microservices users\n```\n\nDeepStream users should see docker images like below.\n```\n$ docker images # bash setup.sh deepstream \nREPOSITORY TAG \nnvcr.io/nvidia/pipetuner 1.0\nnvcr.io/nvidia/deepstream 7.0-triton-multiarch\n```\n\nAlso, model files should be under the ‘models’ folder. They will be mapped into DeepStream containers during tuning.\n```\n$ ls ../models\nlabels.txt resnet34_peoplenet_int8.etlt resnet34_peoplenet_int8.txt resnet50_market1501_aicity156.onnx\n```\n\nMetropolis users should see docker images like below. The ‘models’ folder is empty because default models in mdx-perception container will be used.\n```\n$ docker images # bash setup.sh metropolis\nREPOSITORY TAG \nnvcr.io/nvidia/pipetuner 1.0\nnvcr.io/nfgnkvuikvjm/mdx-v2-0/mdx-perception 2.1\n```\n\nThe final directory under pipe-tuner-sample is like:\n```\npipe-tuner-sample\n├── configs\n│ ├── config_CameraMatrix\n│ ├── config_GuiTool\n│ ├── config_MTMC\n│ ├── config_PGIE\n│ ├── config_PipeTuner\n│ └── config_Tracker\n├── data\n│ ├── SDG_1min_utils \n│ └── SDG_1min_videos\n├── models\n├── ngc_download\n├── multi-camera-tracking (only for Metropolis Microservice)\n└── scripts\n```\n# License\n|Asset |Applicable EULA |Notes |\n|----------------------|---------------------------------------------------|--------|\n|PipeTuner Container |[NVIDIA_PipeTuner_EULA](https://developer.nvidia.com/pipetuner-eula) | A copy of the license is available in the following path inside the container: ```/pipe-tuner/NVIDIA_PipeTuner_EULA.pdf``` |\n\nNOTE: By pulling, downloading, or using PipeTuner, you accept the terms and conditions of the EULA licenses listed above.\n\nFor DeepStream SDK and Metropolis Microservices, please refer to their own licenses.\n\n# Ethical AI\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.","displayName","PipeTuner Documentation and Sample Data",["D",1715023154928],"publisher","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/-/resources/pipetuner-data","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[243,244,245,246,247],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_213":269,"_249":270,"_251":271,"_253":254},{"_213":265,"_249":266,"_251":267,"_253":254},{"_213":261,"_249":262,"_251":263,"_253":254},{"_213":257,"_249":258,"_251":259,"_253":254},{"_213":248,"_249":250,"_251":252,"_253":254},"Video Analytics","value","uscs_video_analytics","filter",{"_255":256,"_249":250},"category","label","field","application","Smart Cities / Spaces","indus_smart_cities_spaces",{"_255":260,"_249":258},"industry","NVIDIA AI","soln_nvidia_ai",{"_255":264,"_249":262},"technology","Metropolis","pltfm_metropolis",{"_255":268,"_249":266},"platform","DeepStream","pltfm_deepstream",{"_255":268,"_249":270},{"_114":1250,"_116":1251},"ngcApps",[1203],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1184":1185,"_1186":1187,"_1188":1189,"_1190":1191,"_1192":283,"_145":309,"_1193":1194,"_1195":56,"_1196":93,"_1197":93,"_1198":1199,"_1200":93,"_1201":1202},"amplitude",{"_1181":1182,"_821":-7,"_1183":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1175":1176,"_1177":1178,"_1179":1180},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787581527629,"1.12.0","defaultSearchSuggestions",{"_1061":188,"_1062":1063,"_1064":1065,"_1066":1067},"announcements",[],"consentState",{"_347":1051,"_1052":93,"_1053":31,"_1054":31,"_1055":1056},"consentConfig",{"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348},"showContactAdmin",1787581527630,[],[],[323,324,325,326,327,328],{"_45":60,"_47":71,"_49":109,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":109,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":67,"_49":109,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":109,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":109,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":109,"_51":-5,"_53":-5,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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These cookies may store a unique ID so that our system will remember you when you return. 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