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We mentioned about NVIDIA TAO Toolkit before in the 2 Best Tools For Accelerating Your AI Model Development post. Now, we will go deeper about it.
Developing AI models necessitated vast amounts of data and a dedicated team of data scientists in the traditional paradigm. However, with transfer learning and pre-trained models, one can leverage pre-existing knowledge from trained models, cutting down the need for new data and time. Even one able to access these resources coding skills are mostly required.
What is NVIDIA TAO Toolkit?
You can develop faster, easier way to create highly accurate, customized, and enterprise-ready AI models using TAO Toolkit without coding. It delivers everything you need for AI training and optimization. You can create computer vision models, export and deploy them on any device one of GPUs, CPUs, and MCUs.
The NVIDIA TAO Toolkit stands as a testament to the advancements in AI and machine learning, drastically reducing the barriers to entry that once existed for developing potent AI models. NVIDIA’s TAO Toolkit, an open-source platform grounded in TensorFlow and PyTorch, harnesses this potential, offering a comprehensive suite for easy model training and optimization for inference.
Whether you are starting with a fresh model or tweaking a pre-existing one, this toolkit facilitates adaptation to varying data types, be it real or synthetic, and ensures optimal inference performance. Thus, even without a deep-seated understanding of AI or access to vast datasets, the NVIDIA TAO Toolkit provides the tools necessary to craft efficient AI solutions.
NVIDIA TAO Toolkit Main Features
No-Code AI Model Training
The no-code AI model training feature of the NVIDIA TAO Toolkit marks a revolutionary shift in how AI models are developed and implemented. Breaking down traditional barriers, this feature offers individuals, regardless of their coding expertise, an intuitive platform to train and refine AI models.
By eliminating the necessity for intricate code, the TAO Toolkit democratizes AI model training, making it accessible and manageable for a broader audience. Users can now leverage the sophisticated capabilities of the toolkit through an interactive interface, sidestepping the complexities of traditional coding processes.
TAO Toolkit provides an extensive model zoo containing pretrained models for computer vision use cases. 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. All models are trained on thousands of proprietary images and achieve very high accuracy on NVIDIA test data.
Some of the purpose-built pre-trained models are TrafficCamNet, PeopleNet, DashCamNet, PeopleSegNet, FaceDetect, Licence Plate Recognition, Gaze Estimation, Facial Landmark, Heart Rate Estimation, Gesture Estimation, Emotion Recognition, BodyPoseNet.
Additionally, general-purpose computer vision models are available. You can train using one of the available architectures such as ResNet, EfficientNet, VGG, MobileNet, GoogLeNet, SqueezeNet, or DarkNet for classification. You can train using YOLOv3/v4/v4-tiny, FasterRCNN, SSD, RetinaNet, and DSSD architectures, as well as NVIDIA’s own DetectNet_v2 architectures for object detection. You can use MaskRCNN for instance segmentation or UNET for semantic segmentation.
Transfer learning accelerates AI model development by adapting pre-trained networks. NVIDIA’s TAO Toolkit allows users to refine their datasets with renowned architectures or use pre-existing high-quality models for various applications. The toolkit also streamlines model training and optimization for inference using methods like pruning and quantization.
NVIDIA TAO Toolkit lets you take your own custom dataset and fine-tune it with one of the many popular network architectures to produce a task-specific model. Or you can get on the fast track with readily available, production-quality models for use cases in smart city, retail, robotics, and more.
Automated Machine Learning (AutoML)
Automated Machine Learning, commonly known as AutoML, represents a significant stride in the realm of AI, acting as a bridge between intricate model development and optimal performance. Traditional model creation often requires meticulous manual efforts in selecting the ideal model architecture and adjusting countless hyperparameters to achieve the desired Key Performance Indicator (KPI).
AutoML streamlines this process, algorithmically identifying and tuning the best-fit models for a specified KPI, thereby abstracting many of the complexities traditionally associated with AI development. Within the framework of NVIDIA’s TAO Toolkit, AutoML comes with a robust configurability feature, enabling the automatic optimization of hyperparameters.
This eradicates much of the trial and error that typically goes into manual tuning. With this innovation, both AI aficionados and those relatively new to the field can benefit from a more efficient and user-friendly model development process.
Deploy on Any Device
Deploying AI models on diverse platforms has never been more accessible, thanks to the NVIDIA TAO Toolkit. Designed to catalyze the distribution of AI across billions of devices, the latest NVIDIA TAO Toolkit 5.0 now incorporates support for the ONNX model export. As an open standard, ONNX paves the way for enhanced interoperability, eliminating the barriers typically associated with proprietary formats.
Consequently, any model nurtured and trained within the NVIDIA TAO Toolkit ecosystem can seamlessly find its place across any computing environment, ensuring a broader and more versatile AI deployment landscape. You can only train with TAO toolkit on an x86 system. You can, however, deploy the optimized models on a Jetson solution.
The NVIDIA TAO Toolkit emerges as a transformative tool in the AI landscape, bridging the gap between intricate model creation and efficient deployment. Leveraging the power of transfer learning, it ensures accelerated AI model development without the traditionally required vast datasets.
Its versatility, underscored by features like AutoML and support for the open standard ONNX, guarantees seamless model optimization and deployment across a myriad of platforms. Whether for AI novices or seasoned professionals, the TAO Toolkit stands as a testament to NVIDIA’s commitment to simplifying and democratizing the world of artificial intelligence, offering a streamlined, user-friendly pathway to harnessing the full potential of AI across varied applications.
You can follow TAO Toolkit Documentation to getting started with TAO Toolkit.