Nvidia Transfer Learning API - Helm Chart

Nvidia Transfer Learning (NVTL) API is a cloud service that enables building end-to-end AI models using custom datasets. In addition to exposing NVTL 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. NVTL jobs run on GPUs within a multi-node cloud cluster.

\nNvidia Transfer Learning API overview

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

The API allows you to create datasets and upload their data to the service or pull data from a public cloud link directly to the service without uploading. You 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 using API calls. For each action, you can request the action's default parameters, update said parameters to your liking, then pass them while running the action. The specs are in the JSON format.

The service exposes a Job API endpoint that allows you to cancel, download, and monitor jobs. Job API endpoints also provide useful information such as epoch number, accuracy, loss values, and ETA. Further, the service demarcates different users inside a cluster and can protect read-write access.

\nTAO Toolkit Workflow

The NVTL 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 platforms officially supported are AWS EKS, Azure AKS, Google GCP and Bare-Metal.

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

Setting up the NVTL API

\n
Follow the instructions mentioned in the NVTL 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 NVTL 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 NVTL API container image
host, tlsSecret, corsOrigin and authClientID are for future ingress rules assuring security and privacy
imagePullSecret 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
maxNumGpuPerNode 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 nvtl-api https://helm.ngc.nvidia.com/nvidia/tao/charts/nvtl-api-5.3.0.tgz --namespace default\n

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

kubectl get pods -n default\n

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

kubectl describe pods nvtl -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**Nvidia Transfer Learning API overview**
\n\nOne can develop client applications on top of the provided API, such as a Web-UI application, or use the provided NVTL remote client CLI.\n\nThe API allows you to create datasets and upload their data to the service or pull data from a public cloud link directly to the service without uploading. You then create models and can create experiments by linking models to train, eval, and inference datasets.\n\nActions such as train, evaluate, prune, retrain, export, and inference can be spawned using API calls. For each action, you can request the action's default parameters, update said parameters to your liking, then pass them while running the action. The specs are in the JSON format.\n\nThe service exposes a Job API endpoint that allows you to cancel, download, and monitor jobs. Job API endpoints also provide useful information such as epoch number, accuracy, loss values, and ETA. Further, the service demarcates different users inside a cluster and can protect read-write access.\n\n

\n**TAO Toolkit Workflow**
\n\nThe NVTL 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 platforms officially supported are AWS EKS, Azure AKS, Google GCP and Bare-Metal.\n\nThis instance contains an easy to deploy helm chart for TAO Toolkit APIs.\n\n### Setting up the NVTL API\n\n1. Follow the instructions mentioned in the [NVTL 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 NVTL 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 NVTL API container image\n* ``host``, ``tlsSecret``, ``corsOrigin`` and ``authClientID`` are for future ingress rules assuring security and privacy\n* ``imagePullSecret`` 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* ``maxNumGpuPerNode`` 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 nvtl-api https://helm.ngc.nvidia.com/nvidia/tao/charts/nvtl-api-5.3.0.tgz --namespace default\n```\n\nYou can validate your deployment. Check for the Ready or Completed states.\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 nvtl -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\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.","displayName","NVIDIA Transfer Learning API - Helm Chart",["D",1711390310775],"publisher","","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/helm-charts/nvtl-api","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_260":1563,"_262":1564},"ngcApps",[1516],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1497":1498,"_1499":1500,"_1501":1502,"_1503":1504,"_1505":400,"_291":424,"_1506":1507,"_1508":57,"_1509":239,"_1510":239,"_1511":1512,"_1513":239,"_1514":1515},"amplitude",{"_1494":1495,"_1096":-7,"_1496":32},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1488":1489,"_1490":1491,"_1492":1493},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1371":362,"_1372":1373,"_1374":1375,"_1376":1377},"announcements",[],"consentState",{"_461":1361,"_1362":239,"_1363":32,"_1364":32,"_1365":1366},"consentConfig",{"_443":444,"_445":446,"_447":448,"_449":450,"_451":452,"_453":454,"_455":456,"_457":458,"_459":460,"_461":462},"showContactAdmin",1784202981238,[],[],[438,439,440,441,442],{"_46":60,"_48":70,"_50":255,"_52":-5,"_54":-5,"_56":57},{"_46":60,"_48":68,"_50":255,"_52":-5,"_54":-5,"_56":57},{"_46":60,"_48":61,"_50":255,"_52":-5,"_54":-5,"_56":57},{"_46":58,"_48":59,"_50":255,"_52":-5,"_54":-5,"_56":57},{"_46":47,"_48":49,"_50":255,"_52":-5,"_54":-5,"_56":57},"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 cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the 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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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.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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