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  • Computer Vision / Video Analytics

    NVIDIA Releases Updates to CUDA-X AI Software

    NVIDIA CUDA-X AI are deep learning libraries for researchers and software developers to build high performance GPU-accelerated applications for conversational AI, recommendation systems and computer vision.

    Learn what’s new in the latest releases of CUDA-X AI libraries.

    Refer to each package’s release notes in documentation for additional information.

    NVIDIA Riva Open Beta 

    NVIDIA Riva is an application framework for multimodal conversational AI services that delivers real-time performance on GPUs. This version of Riva includes:

    • ASR, NLU, and TTS models trained on thousands of hours of speech data.
    • Transfer Learning Toolkit with zero coding approach to re-train on custom data.
    • Fully accelerated deep learning pipelines optimized to run as scalable services.
    • End-to-end workflow and tools to deploy services using one line of code.

    Transfer Learning Toolkit 3.0 Developer Preview

    NVIDIA released new pre-trained models for computer vision and conversational AI that can be easily fine-tuned with Transfer Learning Toolkit (TLT) 3.0 with a zero-coding approach. 

    Key highlights: 

    • New vision AI pre-trained models: license plate detection and recognition, heart rate monitoring, gesture recognition, gaze estimation, emotion recognition, face detection, and facial landmark estimation?
    • Newly added support for automatic speech recognition (ASR) and natural language processing (NLP)?
    • Choice of training with popular network architectures such as EfficientNet, YoloV4, and UNET
    • Support for NVIDIA Ampere GPUs with third-generation tensor cores for performance boost?

    Triton Inference Server 2.7 

    Triton Inference Server is an open source multi-framework, cross platform inference serving software designed to simplify model production deployment. Version 2.7 includes:

    • Model Analyzer – automatically finds best model configuration to maximize performance based on user-specified requirements?
    • Model Repo Agent API? – enables custom operations to be performed to models being loaded (such as decrypting, checksumming, applying TF-TRT optimization, etc)
    • Added support for ONNX Runtime backend in Triton Windows build
    • Added an example Java and Scala client based on GRPC-generated API

    Read full release notes here

    TensorRT 7.2 is Now Available

    NVIDIA TensorRT is a platform for high-performance deep learning inference. This version of TensorRT includes:

    • New Polygraphy toolkit, assists in prototyping and debugging deep learning models in various frameworks
    • Support for Python 3.8

    Merlin Open Beta

    Merlin is an application framework and ecosystem that enables end-to-end development of recommender systems, accelerated on NVIDIA GPUs. Merlin Open Beta highlights include:

    • NVTabular and HugeCTR inference support in Triton Inference Server
    • Cloud configurations and cloud support (AWS/GCP)
    • Dataset analysis and generation tools
    • New PythonAPI for HugeCTR similar to Keras with no JSON configuration anymore

    DeepStream SDK 5.1

    NVIDIA DeepStream SDK is a streaming analytics toolkit for AI-based multi-sensor processing. 

    Key highlights for DeepStream SDK 5.1 (General Availability) 

    • New Python apps for using optical flow, segmentation networks, and analytics using ROI and line crossing
    • Support for audio analytics with a sample application highlighting audio classifier usage
    • Support for NVIDIA Ampere GPUs with third-generation tensor cores and various performance optimizations

    nvJPEG2000 0.2 

    nvJPEG2000 is a new library for GPU-accelerated JPEG2000 image decoding. This version of nvJPEG2000 includes:

    NVIDIA NeMo 1.0.0b4

    NVIDIA NeMo is a toolkit to build, train and fine-tune state-of-the-art speech and language models easily. Highlights of this version include:

    • Compatible with Riva 1.0.0b2 public beta and TLT 3.0 releases

    Deep Learning Examples

    Deep Learning Examples provide state-of-the-art reference examples that are easy to train and deploy, achieving the best reproducible accuracy and performance with NVIDIA CUDA-X AI software stack running on NVIDIA Volta, Turing, and Ampere GPUs.

    New Model Scripts available from the NGC Catalog:

    • nnUNet/PyT: A Self-adapting Framework for U-Net for state-of-the-art Segmentation across distinct entities, image modalities, image geometries, and dataset sizes, with no manual adjustments between datasets.?
    • Wide and Deep/TF2: Wide & Deep refers to a class of networks that use the output of two parts working in parallel – wide model and deep model – to make a binary prediction of CTR.?
    • EfficientNet PyT & TF2: A model that scales the depth, width, and resolution to achieve better performance across different datasets. EfficientNet B4 achieves state-of-the-art 82.78% top-1 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet.
    • Electra: A novel pre-training method for language representations which outperforms existing techniques, given the same compute budget on a wide array of Natural Language Processing (NLP) tasks.
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