NVIDIA Time Series Prediction Platform

NVIDIA Time Series Prediction Platform (TSPP) is a tool designed to compare easily and experiment with arbitrary combinations of forecasting models, time-series datasets, and other configurations. The NVIDIA-TSPP provides functionality to explore the hyperparameter search space, run accelerated model training using distributed training and Automatic Mixed Precision (AMP), and deploy and run inference on accelerated model formats on the NVIDIA Triton Inference Server.\nThe notebooks cover the following topics:

NVDIA-TSPP Overview
Triton Inference
Add a new Dataset
Multi-GPU Training and Hyper Parameter Search

NVIDIA-TSPP official release is available on the Nvidia Deep Learning Examples github. Our blog presents the technical overview of the platform.

Get Started with Training

To help you get started, we have created sample Jupyter Notebooks that can be easily deployed on Vertex AI using NGC’s One Click Deploy feature. This feature automatically sets up the Vertex AI instance with an optimal configuration, preloads the dependencies, runs the software from NGC without any need to set up the infrastructure.

Simply click on the button that reads “Deploy to Vertex AI” and follow the instructions.

Note: A customized kernel for the Jupyter Notebook is used as the primary mechanism for deployment. This kernel has been built on the Pytorch Container. For more information on the container itself, please refer to this link for more information: NVIDIA-TSPP github

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The NVIDIA-TSPP provides functionality to explore the hyperparameter search space, run accelerated model training using distributed training and Automatic Mixed Precision (AMP), and deploy and run inference on accelerated model formats on the NVIDIA Triton Inference Server. \nThe notebooks cover the following topics:\n1. NVDIA-TSPP Overview\n2. Triton Inference\n3. Add a new Dataset\n4. Multi-GPU Training and Hyper Parameter Search\n\nNVIDIA-TSPP official release is available on the [Nvidia Deep Learning Examples github](https://github.com/NVIDIA/DeepLearningExamples/tree/master/Tools/PyTorch/TimeSeriesPredictionPlatform). Our [blog](https://developer.nvidia.com/blog/time-series-forecasting-with-the-nvidia-time-series-prediction-platform-and-triton-inference-server/) presents the technical overview of the platform.\n\n\n\n## Get Started with Training\nTo help you get started, we have created sample Jupyter Notebooks that can be easily deployed on Vertex AI using *NGC’s One Click Deploy* feature. This feature automatically sets up the Vertex AI instance with an optimal configuration, preloads the dependencies, runs the software from NGC without any need to set up the infrastructure. \n\nSimply click on the button that reads “Deploy to Vertex AI” and follow the instructions. \n\n*Note: A customized kernel for the Jupyter Notebook is used as the primary mechanism for deployment. This kernel has been built on the Pytorch Container. For more information on the container itself, please refer to this link for more information: [NVIDIA-TSPP github](https://github.com/NVIDIA/DeepLearningExamples/tree/master/Tools/PyTorch/TimeSeriesPredictionPlatform)*","displayName","NVIDIA Time Series Prediction Platform",["D",1709059864867],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/resources/nvidia_tspp_notebooks","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Tutorials.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[266,267,268,269,270,271,272,273],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_235":299,"_275":300,"_276":301,"_278":87},{"_235":296,"_275":297,"_276":298,"_278":87},{"_235":294,"_275":294,"_276":295,"_278":87},{"_235":291,"_275":292,"_276":293,"_278":87},{"_235":287,"_275":288,"_276":289,"_278":87},{"_235":284,"_275":284,"_276":285,"_278":87},{"_235":280,"_275":281,"_276":282,"_278":87},{"_235":274,"_275":274,"_276":277,"_278":87},"Time series forecast","value","filter",{"_279":87,"_275":274},"category","field","Retail","indus_retail",{"_279":283,"_275":281},"industry","PyTorch",{"_279":286,"_275":284},"framework","ML","soln_ml",{"_279":290,"_275":288},"technology","Inference","soln_inference",{"_279":290,"_275":292},"forecasting",{"_279":87,"_275":294},"DL","soln_dl",{"_279":290,"_275":297},"AI","soln_ai",{"_279":290,"_275":300},{"_152":1474,"_154":1475},"ngcApps",[1427],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1409":1410,"_1411":1412,"_1413":1414,"_1415":1416,"_1417":313,"_109":337,"_1418":1419,"_1407":59,"_1420":90,"_1421":90,"_1422":1423,"_1424":90,"_1425":1426},"amplitude",{"_1405":1406,"_1407":59,"_1009":-7,"_1408":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1399":1400,"_1401":1402,"_1403":1404},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1284":213,"_1285":1286,"_1287":1288,"_1289":1290},"announcements",[],"consentState",{"_375":1274,"_1275":34,"_1276":34,"_1277":90,"_1278":1279},"consentConfig",{"_357":358,"_359":360,"_361":362,"_363":364,"_365":366,"_367":368,"_369":370,"_371":372,"_373":374,"_375":376},"showContactAdmin",1783219040967,[],[],[351,352,353,354,355,356],{"_48":63,"_50":73,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":65,"_54":66,"_56":71,"_58":59},{"_48":63,"_50":68,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":57,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"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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