Computer Vision / Video Analytics

New NVIDIA Deep Learning Software Tools for Developers

May 10, 2017
By Brad Nemire
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AI-Generated Summary
  • NVIDIA SDK received a major update adding tools, libraries, and CUDA programming model enhancements for AI and HPC application development.
  • CUDA 9 introduces support for Volta GPUs, up to 5x faster library performance, a new thread management programming model, and updated debugging and profiling tools.
  • TensorRT 3 delivers 3.5x faster deep learning inference with built-in optimization for Caffe and TensorFlow models, enabling faster deployment of trained neural networks.
  • Volta optimizations across frameworks including Caffe2, Microsoft Cognitive Toolkit, MXNet, PyTorch, and TensorFlow provide 2.5x faster deep learning training for engineers and data scientists.

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Aided by developers’ requests, NVIDIA announced a significant update to the NVIDIA SDK, which includes tools, libraries and enhancements to the CUDA programming model to help developers accelerate and build the next generation of AI and HPC applications.

The level of interest in GPU computing has exploded, fueled by advancements in AI.

The latest SDK updates introduce new capabilities and performance optimizations for GPU-accelerated applications:

  • New CUDA 9 speeds up HPC and deep learning applications with support for Volta GPUs, up to 5x faster performance for libraries, a new programming model for thread management, and updates to debugging and profiling tools.
  • Developers of end-user applications such as AI-powered web services and embedded edge devices benefit from 3.5x faster deep learning inference with the new TensorRT 3. With built-in support for optimizing both Caffe and TensorFlow models, developers can take trained neural networks to production faster than ever.
  • Engineers and data scientists can benefit from 2.5x faster deep learning training using Volta optimizations for frameworks such as Caffe2, Microsoft Cognitive Toolkit, MXNet, PyTorch and TensorFlow.

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Computer Vision / Video Analytics | CUDA | cuDNN | TensorRT | News | Accelerated Computing Libraries | Image / Video Detection & Recognition | Machine Learning & Artificial Intelligence

About the Authors

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About Brad Nemire
Brad Nemire leads the Developer Communications team at NVIDIA. Prior to NVIDIA, he worked at Arm on the Developer Relations team. Brad graduated from San Diego State University and currently resides in Silicon Valley.

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