Enterprise Software Tools for AI

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This article is part of a special insideHPC report that explores trends in machine learning and deep learning. The complete report, available here, covers how businesses are using machine learning and deep learning, differentiating between AI, machine learning and deep learning, what it takes to get started, software tools for AI and more.

There are three exemplary members of the AI software stack available as deep learning frameworks: Caffe, MXNet and TensorFlow.

Caffe is a well-known and widely used deep learning framework which was developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. It focuses more on the image classification problem and it supports multiple GPUs within a node.

MXNet, jointly developed by collaborators from multiple universities and companies, is a lightweight, portable and flexible deep learning framework designed for both efficiency and flexibility. This framework scales to multiple GPUs within a node and across nodes.

TensorFlow, developed by Google’s Brain team, is a library for numerical computation using data flow graphs. TensorFlow also supports multiple GPUs and can scale to multiple nodes.

Enterprise software tools for AI also includes NVIDIA DIGITS , which puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train highly accurate deep neural networks (DNNs) for image classification, segmentation and object detection tasks.

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DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real time with advanced visualizations, and selecting the best performing model. The system is completely interactive, so that data scientists can focus on designing and training networks rather than programming and debugging.

You can download the complete report, “insideHPC Research Report on Riding the Wave of Machine Learning & Deep Learning,” courtesy of Dell EMC and Nvidia. 

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