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TAO Toolkit

NVIDIA TAO Toolkit Version 4.0: What’s New

The TAO Toolkit simplifies and accelerates the model training process by abstracting away the complexity of AI models and the deep learning framework. You can use the power of transfer learning to fine-tune NVIDIA pretrained models with your own data and optimize the model for inference throughput. The 4.0 release makes it even easier to get started and create high-accuracy models without needing any AI expertise. Here are the core features for this release:

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Get Enterprise-Ready with NVIDIA AI Enterprise

NVIDIA AI Enterprise is an end-to-end, secure, cloud-native suite of AI software. It delivers validation and integration for NVIDIA AI open-source software, access to AI solution workflows to speed time to production, certifications to deploy AI everywhere, and enterprise-grade support, security, and API stability.

Use TAO Toolkit with NVIDIA AI Enterprise to get full access to the source code of TAO Toolkit and model weights for the pretrained models, as well as enterprise support that provides guaranteed response times, priority security notifications, and AI experts from NVIDIA.

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Get Started With TAO Toolkit on Google Colab

New Developer Blog

Learn how to create custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning.

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New Developer Blog

Train Like an AI Pro Using the New AutoML Feature in TAO

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GTC Webinar

Learn how to create and deploy custom, production-ready vision AI and conversational AI models without any expertise in AI.

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Developer Starter Resources

Vision AI

Developer Blogs
Training Notebooks & Containers
Sample Deployment Applications

To convert TAO Toolkit model (etlt) to an NVIDIA TensorRT™ engine for deployment with DeepStream, select the appropriate TAO-converter for your hardware and software stack.

Additional Resources

Product Support

Ethical AI

NVIDIA 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. Also, 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.