TLT 3.0

    Highlights:
  • Pretrained models and training for computer vision:
  • Body Pose estimation
  • Emotion recognition
  • Facial landmark
  • License plate detection and recognition
  • Heart rate estimation
  • Gesture recognition
  • Gaze estimation
  • People segmentation
  • Introducing ASR and NLP models with inference samples for:
  • Speech to Text
  • Named Entity Recognition (NER)
  • Question/Answering
  • Punctuation
  • Text classification
  • Turnkey training support on AWS, GCP and Azure.
  • TLT 3.0 brings support for NVIDIA Ampere GPUs with third generation tensor core additions and various performance optimizations
  • Improved PeopleNet model to detect difficult scenarios such as people sitting down, rotated/ warped objects
  • Train with popular networks: EfficientNet, ResNet, YOLOV3/V4, FasterRCNN, SSD, DetectNet_v2, MaskRCNN and UNET
  • Out of the box deployment on NVIDIA Triton and DeepStream SDK for vision AI and Riva for conversational AI
  • Enable faster training with jobs split up across multi-GPUs

Share TLT and AI models update with your network:
Developer news article for TLT3.0 General Availability

Operating System
  • Ubuntu 18.04
Dependencies
  • Driver version >= 455
  • Docker-ce > 19.03
  • Nvidia-docker2
  • Docker-API 1.40

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Getting Started Resources


Install TLT launcher Python package

Conversational AI

Vision AI

Platform Compute Download
x86 + GPU CUDA 10.2 / cuDNN 8.0 / TensorRT 7.1 Download
x86 + GPU CUDA 10.2 / cuDNN 8.0 / TensorRT 7.2 Download
x86 + GPU CUDA 11.0 / cuDNN 8.0 / TensorRT 7.1 Download
x86 + GPU CUDA 11.0 / cuDNN 8.0 / TensorRT 7.2 Download
x86 + GPU CUDA 11.1 / cuDNN 8.0 / TensorRT 7.2 Download
x86 + GPU CUDA 11.2 / cuDNN 8.1 / TensorRT 7.2 Download
Jetson Jetpack 4.4 Download
Jetson Jetpack 4.5 Download
Clara AGX CUDA 11.1 / CuDNN 8.0.5 / TensorRT 7.2.2 Download

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NVIDIA’s 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. 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.