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TAO Toolkit

TAO Toolkit

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Features
Description
Docker containers distributed as part of the TAO Toolkit package
Publisher
NVIDIA
Latest Tag
5.5.0-data-services-base
Modified
March 13, 2025
Compressed Size
9.33 GB
Multinode Support
Yes
Multi-Arch Support
No
5.5.0-data-services-base (Latest) Security Scan Results

Linux / amd64

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What is TAO Toolkit?

TAO (Train Adapt Optimize) is a python based AI toolkit that's built on TensorFlow and PyTorch. It provides transfer learning capability to adapt popular neural network architectures and backbones to your data, allowing you to train, fine-tune, prune, quantize and export highly optimized and accurate AI models for edge deployment.

The purpose built pre-trained models accelerate the AI training process and reduce costs associated with large scale data collection, labeling, and training models from scratch. Transfer learning with pre-trained models can be used for AI applications in smart cities, retail, healthcare, industrial inspection and more. TAO supports training for CV, 3D Point cloud, ASR, NLP and TTS modalities.

TAO packages a collection of containers, python wheels, models and helm chart. AI training tasks run either on TensorFlow or PyTorch depending upon the entrypoint for the model.

For deployment, TAO models can be deployed to DeepStream for video analytics applications, Riva for Conversational AI applications or Triton for inference serving use cases.

TAO Containers

All containers needed to run TAO can be pulled from this location. See the list below for all available containers in this registry.

TAO Container Type container_name:tag What's it used for?
TAO TensorFlow v1 container nvcr.io/nvidia/tao/tao-toolkit:5.0.0-tf1.15.5 Older CV networks like YOLOs, FasterRCNN, DetectNet_v2, MaskRCNN, UNET and more
TAO TensorFlow v2 container nvcr.io/nvidia/tao/tao-toolkit:5.5.0-tf2 CV networks like EfficientDet, EfficientNet and more
TAO PyTorch container nvcr.io/nvidia/tao/tao-toolkit:5.5.0-pyt Newer CV networks like Deformable-DETR, SegFormer and more as well as all ConvAI networks
TAO Deploy container nvcr.io/nvidia/tao/tao-toolkit:5.5.0-deploy Container used to TensorRT engine, INT8 calibration from a trained TAO model and evaluation on said TensorRT engine
TAO Data Service nvcr.io/nvidia/tao/tao-toolkit:5.5.0-dataservice Container for AI-assisted annotation and few other data services
TAO API container nvcr.io/nvidia/tao/tao-toolkit:5.5.0-api Front-end services container that can be used to host a TAO REST API server for remote execution of model training tasks. Useful for building higher level services

How to run TAO?

To get started with TAO, download the sample tutorials from here and follow the quick start instructions.

License

TAO Toolkit getting Started License for TAO containers is included in the banner of the container. License for the pre-trained models are available with the model cards on NGC. 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

  • Access the latest in Vision AI development workflows with NVIDIA TAO Toolkit 5.0
  • Improve accuracy and robustness of vision ai models with vision transformers and NVIDIA TAO
  • Train like a ‘pro’ without being an AI expert using TAO AutoML
  • Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning
  • Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO
  • Learn endless ways to adapt and supercharge your AI workflows with TAO - Whitepaper
  • Customize Action Recognition with TAO and deploy with DeepStream
  • 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

Suggested reading

  • More information on about TAO Toolkit and pre-trained models can be found at the NVIDIA Developer Zone
  • TAO documentation
  • Read the TAO getting Started guide and release notes.
  • If you have any questions or feedback, please refer to the discussions on TAO Toolkit Developer Forums
  • Deploy your models for video analytics application using DeepStream. Learn more about DeepStream SDK
  • Deploy your models in Riva for ConvAI use case.

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.