NVIDIA TAO Toolkit
Looking for a faster, easier way to create highly accurate, customized, and enterprise-ready AI models to power your vision AI applications? The open-source TAO toolkit for AI training and optimization delivers everything you need, putting the power of the world’s best Vision Transformers (ViTs) in the hands of every developer and service provider. You can now create state-of-the-art computer vision models and deploy them on any device—GPUs, CPUs, and MCUs—whether at the edge or in the cloud.
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What Is the NVIDIA TAO Toolkit?
Eliminate the need for mountains of data and an army of data scientists as you create AI/machine learning models and speed up the development process with
transfer learning
. This powerful technique instantly transfers learned features from an existing neural network model to a new customized one.
The open-source NVIDIA TAO Toolkit, built on TensorFlow and PyTorch, uses the power of transfer learning while simultaneously simplifying the model training process and optimizing the model for inference throughput on practically any platform. The result is an ultra-streamlined workflow. Take your own models or pre-trained models, adapt them to your own real or synthetic data, then optimize for inference throughput. All without needing AI expertise or large training datasets.
NVIDIA TAO is available as a part of
NVIDIA AI Enterprise
, an enterprise-ready AI software platform with security, stability, manageability, and support to speed time to value while mitigating the potential risks of open-source software. Three exclusive foundation models trained on commercially viable datasets are included with NVIDIA AI Enterprise:
NV-DINOv2
is the only
commercially viable visual foundational model
trained using self-supervised learning on over 100M images. This model can be quickly fine-tuned for various vision AI tasks with only a handful of training data.
PCB classification
, built on NV-DINOv2, delivers high accuracy for detecting missing components on a PCB.
Retail recognition
, built on NV-DINOv2, can be used to identify large number of retail SKUs.
State-of-the-Art Vision Transformers
In general, transformer-based models can outperform traditional CNN-based models due to their robustness, generalizability, and being able to understand the context in the scene much better. This provides improved accuracy against image corruption and noise, and generalizes better on unseen objects. TAO Toolkit 5.0 features several state-of-the-art (SOTA) vision transformers for popular CV tasks.
Read the Blog
Deploy Models on Any Platforms
NVIDIA TAO Toolkit can help power AI across billions of devices. The new NVIDIA TAO Toolkit 5.0 supports model export in ONNX, an open format for better interoperability. This makes it possible to deploy a model trained with the NVIDIA TAO Toolkit on any computing platform.
Learn More About the Integration With STMicroelectronics
Learn More About the Integration With Edge Impulse
AI-Assisted Data Annotations
New AI-assisted annotation capabilities give you a faster and less expensive way to label segmentation masks. You can use the weakly supervised transformer-based segmentation architecture, Mask Auto Labeler (MAL), to assist in segmentation annotation and in fixing and tightening bounding boxes for object detection.
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Integrate TAO Toolkit in Your Application With Rest APIs
You now have an easier way to deploy TAO Toolkit in a modern cloud-native infrastructure using 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.
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Make AI Easier With AutoML
Training and optimizing AI is a time-consuming process, requiring intimate knowledge of what model to choose and what hyperparameters to tune. Now, you can easily train high-quality models with AutoML without the hassle of manually fine-tuning hundreds of parameters.
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Run on Your Favorite Cloud
The cloud-native technology in TAO provides the agility, scalability, and portability you need to more effectively manage and deploy AI applications. TAO services can be deployed on VMs from any leading cloud provider, as well as with managed Kubernetes services like
Amazon EKS
,
Google GKE
or
Azure AKS
. It can also be used with cloud machine learning services such as Google Colab, Google Vertex AI, and
Azure Machine Learning
to simplify infrastructure management and scaling.
TAO also enables integrations with several cloud and third-party MLOPs services to provide developers and enterprises with an optimized AI workflow. Developers can now track and manage their TAO experiment and manage models using the W&B or ClearML platform.
Learn More About Running TAO in Cloud
Learn More About Integrating TAO with MLOPs services
KoiReader
KoiReader developed an AI-powered machine vision solution using NVIDIA developer tools including TAO to help PepsiCo achieve precision and efficiency in dynamic distribution environments.
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Trifork
Trifork jumpstarted their AI model development with NVIDIA pretrained models and TAO Toolkit to develop their AI-based baggage tracking solution for airports.
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Transfer learning is the process of transferring learned features from one application to another. It’s a commonly used training technique where a model trained on one task is re-trained for use on a different task. You can apply transfer learning on
vision
, speech, and language-understanding models.
The TAO Toolkit supports 100+ permutations of NVIDIA-optimized model architectures and backbones. These include State-of-the-art Vision Transformers like FAN, DINO, and GC-ViT, along with tons of efficient CNNs such as EfficientDet, YOLOs, UNET, and many more.
You can find the full matrix of supported model architectures
here
.
Access to exclusive foundation models for vision AI
Validation and integration for NVIDIA AI open-source software
Access to AI solution workflows to speed time to production
Certifications to deploy AI everywhere
Enterprise-grade support, security, manageability, and API stability to mitigate potential risks of open source software
Yes, TAO can be deployed at the infrastructure level using VMs from the cloud or can be deployed in various cloud services like Amazon EKS, Azure AKS,
Google GKE
, Google Vertex AI, Azure Machine Learning, or Google Colab. Please refer to the
TAO Toolkit documentation
to learn more about running the TAO Toolkit on AWS, Azure, or GCP.
