As the leading framework for Distributed ML, the addition of deep learning to the super-popular Spark framework is important, because it allows Spark developers to perform a wide range of data analysis tasks—including data wrangling, interactive queries, and stream processing—within a single framework. Three important features offered by BigDL are rich deep learning support, High Single Node Xeon...
深度学习作为分布式机器学习的主要框架，将其添加至颇为常用的 Spark 框架具有重要意义，有助于 Spark 开发人员在单个框架内处理各种数据分析任务—包括数据处理、交互式查询和数据流处理。BigDL 提供三个重要特性，分别是丰富的深度学习支持、较高的单节点至强性能以及利用 spark 架构实现高效的横向扩展。
Learn how to install and use BigDL for training and testing some of the commonly used deep neural network models on Apache Spark.
The latest version of MXNet includes built-in support for the Intel® Math Kernel Library (Intel® MKL) 2017. The latest version of the Intel MKL includes optimizations for Intel® Advanced Vector Extensions 2 (Intel® AVX2) and AVX-512 instructions which are supported in Intel® Xeon® processor and Intel® Xeon Phi™ processors.
This article showcases the implementation of an agent to play the game Pong* using an Intel® architecture-optimized neon™ framework, and to serve as an introduction to the Policy Gradients algorithm.
In this article we delve into the theory, training procedure, and performance of binary neural networks (BNNs).
The TensorFlow* image classification sample codes below describe a step-by-step approach to modify the code in order to scale the deep learning training across multiple nodes of HPC data centers.
How developers can use to take advantage of the new Intel® AVX512-Deep Learning Boost (Intel® AVX512-DL Boost) instructions.