Start Creating Custom AI Models with the NVIDIA
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:
- Automatically fine-tune your hyperparameters with AutoML
- Experiment with TAO on Google Colab
- Experience turnkey deployment of TAO into various cloud services–Azure ML, Google Vertex AI , Azure Kubernetes Services, Amazon EKS, and more
- Integrate TAO with third-party MLOPs services
- Access TAO source code and model weights for pretrained models without encryption using NVIDIA AI Enterprise software suite
- Explore new transformer-based vision models (CitySemSegformer, Peoplenet Transformer) and pretrained models for retail and smart city tasks (Retail Object Detection, Retail Object Recognition, and ReIdentificationNet.)
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.
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.
Learn how to create and deploy custom, production-ready vision AI and conversational AI models without any expertise in AI.
Developer Starter Resources
|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.
|Sample Deployment Applications|
Featured Video Tutorial
Blogs & Tutorials
- Train Like an AI Pro Using the New AutoML Feature in TAO (New)
- Create Custom AI Models With TAO in Azure ML (New)
- 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
Data Generation And Labeling Partners
NVIDIA has partnered with several companies to bring data creation and annotation to accelerate training.
- Rocketboots optimizes workforce management with vision AI
- SenSen fastracks AI model development for truck management
- Nota uses vision AI to make roadways safer
- Onecup AI brings AI to automated precision ranching
DLI Training Courses
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.