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","bias","explainability","privacy","safetyAndSecurity",[],[322,323,324,325,326,327],[],[],[],{"_92":338,"_129":338,"_130":339,"_132":133},{"_92":141,"_129":142,"_130":337,"_132":133},{"_92":334,"_129":335,"_130":336,"_132":133},{"_92":332,"_129":332,"_130":333,"_132":133},{"_92":330,"_129":330,"_130":331,"_132":133},{"_92":328,"_129":328,"_130":329,"_132":133},"Smart Infrastructure",{"_134":133,"_129":328},"Smart City",{"_134":133,"_129":330},"NSPECT-76DN-OP7I",{"_134":133,"_129":332},"Metropolis","pltfm_metropolis",{"_134":144,"_129":335},{"_134":144,"_129":142},"CV",{"_134":133,"_129":338},{"_177":1510,"_179":1511},"ngcApps",[1463],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1444":1445,"_1446":1447,"_1448":1449,"_1450":1451,"_1452":351,"_209":375,"_1453":1454,"_1455":51,"_1456":156,"_1457":156,"_1458":1459,"_1460":156,"_1461":1462},"amplitude",{"_1441":1442,"_1048":-7,"_1443":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1435":1436,"_1437":1438,"_1439":1440},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1323":455,"_1324":1325,"_1326":1327,"_1328":1329},"announcements",[],"consentState",{"_412":1313,"_1314":156,"_1315":26,"_1316":26,"_1317":1318},"consentConfig",{"_394":395,"_396":397,"_398":399,"_400":401,"_402":403,"_404":405,"_406":407,"_408":409,"_410":411,"_412":413},"showContactAdmin",1783956618017,[],[],[389,390,391,392,393],{"_40":55,"_42":66,"_44":172,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":172,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":172,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":172,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":172,"_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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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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