NVIDIA TAO

The NVIDIA TAO Toolkit is developed to streamline the creation of AI models for vision AI applications, ensuring both accuracy and customization.

Key Features

AutoML Capability: According to the vendor, the TAO Toolkit's AutoML capability eliminates the need for manual tuning, enabling efficient model training and potentially faster solutions.

State-of-the-Art Vision Transformers: The TAO Toolkit incorporates advanced vision transformers for computer vision tasks, potentially outperforming traditional CNN-based models in terms of accuracy and robustness.

Optimization for Inference: Developers can optimize models for inference throughput, potentially achieving up to 4X performance improvement compared to customization alone.

Deployment on Any Device: The TAO Toolkit allows for the deployment of optimized models on various devices, including GPUs, CPUs, MCUs, and more, according to the vendor.

AI-Assisted Data Annotations: The toolkit provides AI-assisted annotation capabilities, such as the Mask Auto Labeler (MAL), facilitating efficient and cost-effective labeling of segmentation masks and object detection bounding boxes.

Integration with Rest APIs: Developers can easily integrate TAO Toolkit into their applications using REST APIs, enabling automation and deployment in cloud-native infrastructures.

AutoML for Hyperparameter Tuning: The TAO Toolkit offers AutoML functionality, automatically fine-tuning hyperparameters to simplify and potentially speed up the AI training process.

Cloud Deployment: The TAO Toolkit is cloud-native, providing agility, scalability, and portability for managing and deploying AI applications, according to the vendor. It can be deployed on leading cloud providers and integrated with cloud machine learning services.

Inference Performance: According to the vendor, the TAO Toolkit leverages NVIDIA pretrained models to potentially unlock peak inference performance across platforms, from edge devices to cloud environments.