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These three tools are powerful enough to make it easier for developers to build solutions on NVIDIA's platform and to match superior hardware with great software.
DIGITS 4: A new object detection workflow NVIDIA DIGITS 4 uses a new object detection workflow that enables data scientists to find deep faces, pedestrians, traffic signs, vehicles and other objects in massive images by training deep neural networks. . This workflow enables developers to develop advanced deep learning solutions such as object tracking, security and surveillance in satellite imagery, advanced driver assistance systems, and medical diagnostic screening.
When training deep neural networks, researchers must repeatedly adjust various parameters to achieve a very high precision of the trained model. DIGITS 4 automatically trains neural networks with a range of tuning parameters, dramatically reducing the time required to create the most accurate solution.
The DIGITS 4 candidate will be available this week for free download by members of the NVIDIA Developer Program. For more information, please visit the DIGITS website.
cuDNN 5.1: Accelerating VGG and ResNet Neural Networks NVIDIA cuDNN provides high-performance building blocks for deep learning, which are used by all leading deep learning frameworks. Version 5.1 accelerates the training of deep neural networks, such as Oxford University's VGG and Microsoft's ResNet, and these two deep neural networks won the 2016 ImageNet Challenge.
The performance of each new version of cuDNN is higher than the previous version, thus speeding up the latest advances in deep learning neural networks and machine learning algorithms.
The cuDNN 5.1 candidate is available today for free download by members of the NVIDIA Developer Program. To learn more about or download the software, please visit cuDNN's official website.
GPU Inference Engine: High Performance Deep Learning Reasoning Solution GIE is a high performance deep learning reasoning solution for production environments. GIE optimizes trained deep neural networks for efficient runtime performance, enabling 16x higher watts per performance on NVIDIA Tesla M4 GPU systems than today's pure CPU systems.
The time and power consumption to complete the reasoning work are the two most important considerations for deploying deep learning applications. These two factors determine the quality of the user experience and the cost of deploying the application.
With GIE, cloud service providers can process images, video and other data more efficiently with extremely high throughput in their very large data center production environments. Automakers and embedded solution providers can deploy powerful neural network models with high performance in their low-power platforms.
For more information, please visit the NVIDIA GIE website.
NVIDIA Software Development Kit (SDK)
The NVIDIA Deep Learning Platform is part of a broader NVIDIA SDK that integrates the most important technologies in today's computing industry, such as artificial intelligence, virtual reality, and parallel computing, into a single program.
These software libraries, application programming interfaces (APIs) and tools are widely adopted by popular game engines, hundreds of games, and GPU-accelerated applications, and developers use them to cloud platforms such as Amazon AWS, IBM Softlayer, and Microsoft Azure. And build applications and services on the best-performing supercomputers in the US and around the world.
The tools and libraries in the NVIDIA SDK are organized by application domain, enabling developers to get the content they need quickly and easily
To help data scientists and developers take advantage of opportunities in the deep learning space, NVIDIA has released three major updates to its deep learning software platform: NVIDIA DIGITS 4, CUDA Deep Neural Network Library (cuDNN) 5.1 and new GPUs. Inference Engine (GIE).