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 Jarvis Open Beta
NVIDIA Jarvis is an application framework for multimodal conversational AI services that delivers real-time performance on GPUs. This version of Jarvis 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.
- 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 is a new library for GPU-accelerated JPEG2000 image decoding. This version of nvJPEG2000 includes:
- Support for multi-tile and multi-layer decoding.
- Partial decode by specifying an area of interest for increased efficiency
- New API for commonly used GDAL interface for geospatial images
- 4x faster lossless decoding for 5-3 wavelet decoding and 7x faster loss decoding for 9-7 wavelet transform. Achieve further speed up by pipelining decoding of multiple images.
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 Jarvis 1.0.0b2 public beta and TLT 3.0 releases
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.