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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
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Thread Parallelism in Cython*

Cython* is a superset of Python* that additionally supports C functions and C types on variable and class attributes. Cython generates C extension modules, which can be used by the main Python program using the import statement.
Authored by Nguyen, Loc Q (Intel) Last updated on 07/06/2019 - 16:30
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Code Sample: Exploring MPI for Python* on Intel® Xeon Phi™ Processor

Learn how to write an MPI program in Python*, and take advantage of Intel® multicore architectures using OpenMP threads and Intel® AVX512 instructions.
Authored by Nguyen, Loc Q (Intel) Last updated on 07/06/2019 - 16:30
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