Article

Caffe* Training on Multi-node Distributed-memory Systems Based on Intel® Xeon® Processor E5 Family

Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and one of the most popular community frameworks for image recognition. Caffe is often used as a benchmark together with AlexNet*, a neural network topology for image recognition, and ImageNet*, a database of labeled images.
Authored by Gennady F. (Blackbelt) Last updated on 07/05/2019 - 14:54
Article

Caffe* Scoring Optimization for Intel® Xeon® Processor E5 Series

    In continued efforts to optimize Deep Learning workloads on Intel® architecture, our engineers explore various paths leading to the maximum performance.

Authored by Gennady F. (Blackbelt) Last updated on 03/21/2019 - 12:28
Article

BigDL: Distributed Deep Learning on Apache Spark*

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...
Authored by Last updated on 03/11/2019 - 13:17
Article

BigDL – Scale-out Deep Learning on Apache Spark* Cluster

Learn how to install and use BigDL for training and testing some of the commonly used deep neural network models on Apache Spark.
Authored by Sunny G. (Intel) Last updated on 03/11/2019 - 13:17
Article

Using Intel® MPI Library on Intel® Xeon Phi™ Product Family

This document is designed to help users get started writing code and running MPI applications using the Intel® MPI Library on a development platform that includes the Intel® Xeon Phi™ processor.
Authored by Nguyen, Loc Q (Intel) Last updated on 03/21/2019 - 12:00
Article

TensorFlow* Sample Codes for Distributed Image Classification

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.

Authored by Michael Steyer (Intel) Last updated on 03/11/2019 - 13:17