Start Creating Custom AI Models with the NVIDIA TAO Toolkit
NVIDIA TAO Toolkit Version 3.22.05: What’s New
NVIDIA TAO Toolkit is a low-code AI model development solution that uses the power of transfer learning to simplify and accelerate the creation of custom, production-ready AI models. The new release makes it easy to:
- “Bring your own” ONNX models weights into TAO for fine-tuning and optimizing.
- Deploy TAO Toolkit as-a-Service in a modern, cloud-native infrastructure on Kubernetes and integrate it with REST APIs.
- Visualize the model training progress such as training loss, validation loss, and histogram of model weights in TensorBoard.
- Access new vision pretrained models, including Point Cloud, Pose Action Classification, and 3D Pose Estimation.
- Take advantage of new speech pretrained models, including HiFi Gan and FastPitch for deploying custom text-to-speech applications.
Get Started With TAO Toolkit
Introductory Whitepaper
Learn how NVIDIA TAO Toolkit and pretrained models can transform your development efforts.
New Developer Blog
Fastrack AI-Powered Robotic Applications with Synthetic Data and Transfer Learning
GTC Webinar
Learn how to create and deploy custom, production-ready vision AI and conversational AI models without any expertise in AI.
LaunchPad
Test Drive the NVIDIA TAO Toolkit for free on NVIDIA LaunchPad.
Developer Starter Resources
Vision AI
Conversational AI
Developer Blogs | |
Sample Applications | |
Download Resources |
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Featured Video Tutorial
Import, Train, and Optimize ONNX Models with NVIDIA TAO Toolkit
Visualize AI Model Training Metrics with TensorBoard
Additional Resources
Blogs & Tutorials
- Creating a Real-Time License Plate Detection and Recognition App
- Fast-Tracking Hand Gesture Recognition AI Applications with Pretrained Models from NGC
- Implementing a Real-Time, AI-Based, Face Mask Detector Application for COVID-19
Featured Webinars
Data Generation And Labeling Partners
NVIDIA has partnered with several companies to bring data creation and annotation to accelerate training.
Product Support
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. 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.