TAO Toolkit API - Helm Chart

TAO Toolkit API is a Kubernetes service that enables building end-to-end AI models using custom datasets. In addition to exposing TAO Toolkit functionality through APIs, the service also enables a client to build end-to-end workflows - creating datasets, models, obtaining pretrained models from NGC, obtaining default specs, training, evaluating, optimizing and exporting models for deployment on edge. It can be easily installed on a Kubernetes cluster (local / AWS EKS) using a Helm chart along with minimal dependencies. TAO toolkit jobs can be run using GPUs available on the cluster and can scale to a multi-node setting.

\nTAO Toolkit API overview

One can develop client applications on top of the provided API, such as a Web-UI application, or use the provided TAO remote client CLI.

The API allows users to create datasets and upload their data to the service. Users then create models and can create experiments by linking models to train, eval and inference datasets. Actions such as train, evaluate, prune, retrain, export and inference can be spawned through simple API calls. For each action, the user can obtain default specs using a HTTP GET and POST the spec they prefer for that action. The specs are in the JSON format. Another unique feature of the Service is the ability to chain jobs. For example, a user can run train and evaluate using a single API call. This abstracts away complex directory manipulations and dependency checks. The service exposes a Job API which allows a user to cancel, download and monitor jobs. Job APIs also provide useful information such as epoch number, accuracy, loss values and ETA information. Further, the service demarcates different users inside a cluster and can protect read-write access.

\nTAO Toolkit Workflow

The TAO remote client is an easy to use Command line interface that uses API calls to expose an interface similar to TAO Launcher CLI.

The API service can run on any Kubernetes platform. The two platforms officially supported are AWS EKS and Bare-Metal.

This instance contains an easy to deploy helm chart for TAO Toolkit APIs.

Setting up the TAO Toolkit API

\n
Follow the instructions mentioned in the TAO Toolkit API documentation to setup a bare-metal kubernetes instance or an AWS EKS instance
\n
\n
Once you have setup the kubernetes instance, you may deploy the TAO Toolkit API by following the instructions in this section.
\n

You may update the values.yaml of the chart before deployment.

image is the location of the TAO Toolkit API container image
host, tlsSecret, corsOrigin and authClientID are for future ingress rules assuring security and privacy
ngcImagePullSecretName is the secret name that you setup to access Nvidia's nvcr.io registry
imagePullPolicy is set to Always fetch from nvcr.io instead of using locally cached image
storageClassName is the storage class created by your K8s Storage Provisioner. On bare-metal deployment it is nfs-client, and on AWS EKS can be standard. Not providing a value would make your deployment use your K8s cluster's default storage class
storageAccessMode is set to ReadWriteMany to reuse allocated storage between deployemnts, or ReadWriteOnce to create a new storage at every deployement
storageSize is ignored by many Storage Provisioners. But here would be where to set your shared storage size
backend is the platform used for training jobs. Defaults to local-k8s
numGpu is the number of GPU assigned to each job. Note that multi-node training is not yet supported, so one would be limited to the number of GPUs within a cluster node for now

helm install tao-api chart/ --set ngc_api_key={your_ngc_personal_key} --namespace default \n

You can validate your deployment. Check for the Ready or Completed states.

\n
Note: You can replace tao-api with your preferred release name. However, please ensure that your chosen name does not exceed 11 characters, as there is limitation on kubernetes resource names which we use release name to construct.
\n

kubectl get pods -n default\n

You can debug your deployment. Look for events toward the bottom.

kubectl describe pods tao-toolkit-api -n default\n

Common issues are:

GPU Operator or Storage Provisioner pods not in Ready or Completed states
Missing or invalid imagepullsecret

License

TAO Toolkit getting Started\nLicense for TAO containers is included within the container at workspace/EULA.pdf. License for the pre-trained models are available with the model files. By pulling and using the Train Adapt Optimize (TAO) Toolkit container to download models, you accept the terms and conditions of these licenses.

Technical blogs

Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
Learn how to train real-time License plate detection and recognition app with TAO and DeepStream.
Model accuracy is extremely important, learn how you can achieve state of the art accuracy for classification and object detection models using TAO
Learn how to train Instance segmentation model using MaskRCNN with TAO
Learn how to improve INT8 accuracy using Quantization aware training(QAT) with TAO
Read the technical tutorial on how PeopleNet model can be trained with custom data using Transfer Learning Toolkit
Learn how to train and deploy real-time intelligent video analytics apps and services using DeepStream SDK

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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\n**TAO Toolkit API overview**
\n\nOne can develop client applications on top of the provided API, such as a Web-UI application, or use the provided TAO remote client CLI.\n\nThe API allows users to create datasets and upload their data to the service. Users then create models and can create experiments by linking models to train, eval and inference datasets. Actions such as train, evaluate, prune, retrain, export and inference can be spawned through simple API calls. For each action, the user can obtain default specs using a HTTP GET and POST the spec they prefer for that action. The specs are in the JSON format. Another unique feature of the Service is the ability to chain jobs. For example, a user can run train and evaluate using a single API call. This abstracts away complex directory manipulations and dependency checks. The service exposes a Job API which allows a user to cancel, download and monitor jobs. Job APIs also provide useful information such as epoch number, accuracy, loss values and ETA information. Further, the service demarcates different users inside a cluster and can protect read-write access.\n\n

\n**TAO Toolkit Workflow**
\n\nThe TAO remote client is an easy to use Command line interface that uses API calls to expose an interface similar to TAO Launcher CLI.\n\nThe API service can run on any Kubernetes platform. The two platforms officially supported are AWS EKS and Bare-Metal.\n\nThis instance contains an easy to deploy helm chart for TAO Toolkit APIs.\n## Setting up the TAO Toolkit API\n\n1. Follow the instructions mentioned in the [TAO Toolkit API documentation](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_api/api_setup.html) to setup a bare-metal kubernetes instance or an AWS EKS instance\n\n2. Once you have setup the kubernetes instance, you may deploy the TAO Toolkit API by following the instructions in this [section](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_api/api_deployment.html).\n\nYou may update the ``values.yaml`` of the chart before deployment.\n\n* ``image`` is the location of the TAO Toolkit API container image\n* ``host``, ``tlsSecret``, ``corsOrigin`` and ``authClientID`` are for future ingress rules assuring security and privacy\n* ``ngcImagePullSecretName`` is the secret name that you setup to access Nvidia's nvcr.io registry\n* ``imagePullPolicy`` is set to Always fetch from nvcr.io instead of using locally cached image\n* ``storageClassName`` is the storage class created by your K8s Storage Provisioner. On bare-metal deployment it is nfs-client, and on AWS EKS can be standard. Not providing a value would make your deployment use your K8s cluster's default storage class\n* ``storageAccessMode`` is set to ReadWriteMany to reuse allocated storage between deployemnts, or ReadWriteOnce to create a new storage at every deployement\n* ``storageSize`` is ignored by many Storage Provisioners. But here would be where to set your shared storage size\n* ``backend`` is the platform used for training jobs. Defaults to local-k8s\n* ``numGpu`` is the number of GPU assigned to each job. Note that multi-node training is not yet supported, so one would be limited to the number of GPUs within a cluster node for now\n\n```sh\nhelm install tao-api chart/ --set ngc_api_key={your_ngc_personal_key} --namespace default \n```\n\nYou can validate your deployment. Check for the Ready or Completed states.\n\n> **Note:** You can replace `tao-api` with your preferred release name. However, please ensure that your chosen name does not exceed 11 characters, as there is limitation on kubernetes resource names which we use release name to construct.\n\n\n```sh\nkubectl get pods -n default\n```\n\nYou can debug your deployment. Look for events toward the bottom.\n\n```sh\nkubectl describe pods tao-toolkit-api -n default\n```\n\nCommon issues are:\n\n* GPU Operator or Storage Provisioner pods not in Ready or Completed states\n* Missing or invalid imagepullsecret\n\n## License \n[TAO Toolkit getting Started](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html)\nLicense for TAO containers is included within the container at `workspace/EULA.pdf`. License for the pre-trained models are available with the model files. By pulling and using the Train Adapt Optimize (TAO) Toolkit container to download models, you accept the terms and conditions of these [licenses](https://developer.nvidia.com/tao-toolkit-software-license-agreement).\n\n## Technical blogs \n\n* Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - [Part 1](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-1) | [Part 2](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-2)\n* Learn how to train [real-time License plate detection and recognition app](https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app) with TAO and DeepStream. \n* Model accuracy is extremely important, learn how you can achieve [state of the art accuracy for classification and object detection models](https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tao-toolkit/) using TAO\n* Learn how to train [Instance segmentation model using MaskRCNN with TAO](https://developer.nvidia.com/blog/training-instance-segmentation-models-using-maskrcnn-on-tao-toolkit/)\n* Learn how to improve INT8 accuracy using [Quantization aware training(QAT) with TAO](https://developer.nvidia.com/blog/improving-int8-accuracy-using-quantization-aware-training-and-tao-toolkit/)\n* Read the technical tutorial on how [PeopleNet model can be trained with custom data using Transfer Learning Toolkit](https://devblogs.nvidia.com/training-custom-pretrained-models-using-tlt/)\n* Learn how to [train and deploy real-time intelligent video analytics apps and services using DeepStream SDK](https://devblogs.nvidia.com/building-iva-apps-using-deepstream-5.0/)\n\n## Suggested reading \n\n* More information on about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)\n* Read the [TAO getting Started](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).\n* If you have any questions or feedback, please refer to the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n* Deploy your model on the edge using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n\n## Ethical AI \n\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.","TAO Toolkit API Helm",["D",1774285177429],"/api/image/nvidia/tao/helm-charts/tao-toolkit-api","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png",889438,"bias","explainability","privacy","safetyAndSecurity",[],[324,325,326,327,328,329],[],[],[],{"_92":340,"_129":340,"_130":341,"_132":133},{"_92":141,"_129":142,"_130":339,"_132":133},{"_92":336,"_129":337,"_130":338,"_132":133},{"_92":334,"_129":334,"_130":335,"_132":133},{"_92":332,"_129":332,"_130":333,"_132":133},{"_92":330,"_129":330,"_130":331,"_132":133},"Smart Infrastructure",{"_134":133,"_129":330},"Smart City",{"_134":133,"_129":332},"NSPECT-76DN-OP7I",{"_134":133,"_129":334},"Metropolis","pltfm_metropolis",{"_134":144,"_129":337},{"_134":144,"_129":142},"CV",{"_134":133,"_129":340},{"_178":1316,"_180":1317},"ngcApps",[1269],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1250":1251,"_1252":1253,"_1254":1255,"_1256":1257,"_1258":353,"_210":379,"_1259":1260,"_1261":51,"_1262":156,"_1263":156,"_1264":1265,"_1266":156,"_1267":1268},"amplitude",{"_1247":1248,"_893":-7,"_1249":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1241":1242,"_1243":1244,"_1245":1246},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1788597217081,"1.15.0","defaultSearchSuggestions",{"_1133":460,"_1134":1135,"_1136":1137,"_1138":1139},"announcements",[],"consentState",{"_418":1123,"_1124":156,"_1125":26,"_1126":26,"_1127":1128},"consentConfig",{"_400":401,"_402":403,"_404":405,"_406":407,"_408":409,"_410":411,"_412":413,"_414":415,"_416":417,"_418":419},"showContactAdmin",1788597217082,[],[],[393,394,395,396,397,398,399],{"_40":55,"_42":66,"_44":67,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":67,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":62,"_44":67,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":67,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":67,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":67,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":67,"_46":-5,"_48":-5,"_50":51},"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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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674],[621,622,623,624,625],{"_458":534,"_92":534,"_91":460,"_432":645},{"_458":508,"_92":508,"_91":460,"_432":640},{"_458":635,"_92":635,"_91":460,"_432":636},{"_458":630,"_92":630,"_91":460,"_432":631},{"_458":626,"_92":626,"_91":460,"_432":627},"nvidia.partners",[628],{"_276":629,"_465":626,"_466":26,"_91":502},"edge","app.hushly.com",[632],{"_276":633,"_465":630,"_466":26,"_91":634},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[637],{"_276":638,"_465":635,"_466":26,"_91":639},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761],[713,714,715,716],{"_458":728,"_92":728,"_91":460,"_432":729},{"_458":723,"_92":723,"_91":460,"_432":724},{"_458":521,"_92":521,"_91":460,"_432":720},{"_458":635,"_92":635,"_91":460,"_432":717},[718],{"_276":719,"_465":635,"_466":156,"_91":639},"_gh_sess",[721],{"_276":722,"_465":521,"_466":156,"_91":528},"YSC","bizible.com",[725],{"_276":726,"_465":723,"_466":26,"_91":727},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[730],{"_276":731,"_465":728,"_466":26,"_91":732},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_276":803,"_465":580,"_466":26,"_91":804},{"_276":788,"_465":802,"_466":156,"_91":789},{"_276":771,"_465":583,"_466":26,"_91":696},{"_276":771,"_465":684,"_466":26,"_91":696},{"_276":800,"_465":580,"_466":26,"_91":801},{"_276":788,"_465":586,"_466":156,"_91":789},{"_276":798,"_465":580,"_466":26,"_91":799},{"_276":788,"_465":583,"_466":156,"_91":789},{"_276":796,"_465":683,"_466":26,"_91":797},{"_276":788,"_465":705,"_466":156,"_91":789},{"_276":768,"_465":583,"_466":26,"_91":770},{"_276":794,"_465":580,"_466":156,"_91":795},{"_276":792,"_465":580,"_466":26,"_91":793},{"_276":790,"_465":580,"_466":156,"_91":791},{"_276":788,"_465":679,"_466":156,"_91":789},{"_276":786,"_465":580,"_466":26,"_91":787},{"_276":784,"_465":580,"_466":26,"_91":785},{"_276":782,"_465":580,"_466":156,"_91":783},{"_276":780,"_465":688,"_466":26,"_91":781},{"_276":768,"_465":679,"_466":26,"_91":770},{"_276":778,"_465":580,"_466":26,"_91":779},{"_276":776,"_465":580,"_466":156,"_91":777},{"_276":774,"_465":580,"_466":26,"_91":775},{"_276":772,"_465":580,"_466":26,"_91":773},{"_276":771,"_465":580,"_466":26,"_91":696},{"_276":768,"_465":769,"_466":26,"_91":770},{"_276":766,"_465":580,"_466":26,"_91":767},{"_276":764,"_465":580,"_466":156,"_91":765},{"_276":762,"_465":580,"_466":26,"_91":763},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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