What Is the NVIDIA TAO Toolkit?
Creating an AI/machine learning model from scratch requires mountains of data and an army of data scientists. Now, you can speed up the model development process with transfer learning —a popular technique that extracts learned features from an existing neural network model to a new customized one.
The NVIDIA TAO Toolkit, built on TensorFlow and PyTorch, is a low-code version of the NVIDIA TAO framework that accelerates the model training process by abstracting away the AI/deep learning framework complexity. The TAO Toolkit lets you use the power of transfer learning to fine-tune NVIDIA pretrained models with your own data and optimize for inference—without AI expertise or large training datasets.
Train Models Easily
The TAO toolkit is a low-code solution that lets you train models with Jupyter notebooks, eliminating the need for AI framework expertise.
Build Highly Accurate AI
Use NVIDIA pretrained models and model architectures to create highly accurate and custom AI models for your use-case.
Optimize For Inference
Go beyond customization and achieve up to 4X performance by optimizing the model for inference.
What Are Pretrained AI Models?
Pretrained AI/deep learning models have been trained on representative datasets and fine-tuned with weights and biases. You can quickly and easily customize these models with only a fraction of real-world or synthetic data, compared to training from scratch.
Pretrained Models For Vision AI
Create custom deep learning models for computer vision tasks like image processing and classification, object detection, and semantic segmentation using 100+ NVIDIA-optimized model architectures.
You can also use task-based models to recognize human actions and poses, detect people in crowded spaces, detect and classify vehicles and license plates, and much more.
Pretrained Models for Conversational AI
Design personalized real-time call center experiences, smart kiosks, and other conversation AI services by fine-tuning Automatic Speech Recognition (ASR) , Natural Language Processing (NLP), and Text-to-Speech (TTS)-based modes.Download Jupyter notebooks from NGC
Integrate TAO Toolkit in Your Application with Rest APIs
Deploy TAO Toolkit in a modern cloud-native infrastructure on Kubernetes and integrate it in your application with REST APIs. Build a new AI service or integrate the TAO Toolkit into your existing service and enable automation between disparate tools.Learn more
Import ONNX Model Weights
Simply import your own classification and segmentation model weights from an ONNX model into TAO Toolkit for fine-tuning and optimization.See demo
Augment Your Data
Improve your model performance with data augmentation features such as spatial and color transformations to diversify your existing image data set.Learn more
Visualize with TensorBoard
Understand how your model converges by visualizing scalars such as training and validation loss, accuracy, model weights, and predicted images.Learn more
Optimize for Inference
Achieve up to 4X speed up in inference with pruning and INT8 quantization while maintaining comparable accuracy.Read blog
Power DeepStream and Riva
The optimized models produced by TAO Toolkit can be easily integrated with NVIDIA DeepStream SDK for vision AI and Riva for speech AI, letting you unlock greater performance and accelerating your development for your applications.Integrate with DeepStream Integrate with Riva
Create AI Anywhere with NVIDIA AI Enterprise
The TAO toolkit is now an integral part of the NVIDIA AI Enterprise, an end-to-end, cloud-native suite of AI and data analytics software. It’s optimized, certified, and supported by NVIDIA so enterprises can create and deploy AI—certified to deploy anywhere—with enterprise support to keep AI projects on track.
You can now experience all the features of the TAO Toolkit for free through NVIDIA LaunchPadSign up here
Unlock peak inference performance with NVIDIA pre-trained models across platforms from the edge with NVIDIA Jetsons to the cloud with NVIDIA Ampere architecture GPUs. For more details on batch size and other models, check the detailed performance datasheet.
OneCup AI’s computer vision system tracks and classifies animal activity using NVIDIA pretrained models and TAO Toolkit, significantly reducing their development time from months to weeks.
RocketBoots uses NVIDIA TAO toolkit and pretrained models to improve the inference performance in their automated workforce management solution.
Floatbot uses NVIDIA Riva and NVIDIA TAO for their customized Singaporean English voice AI applications, automating call centers for insurance carriers and finance clients globally.
There are two versions of TAO.
- TAO Toolkit, which is the low-code version of TAO
- A no-code, GUI-based solution currently in development. Apply for early access here.
The full matrix of supported model architectures can be found here.
- Vision models can be deployed through DeepStream
- Speech Models can deployed through Riva
- You can also deploy the models through Triton Inference Server.
Refer to the documentation section for deployment details.
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