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最大限度提升 CPU 上的 TensorFlow* 性能:推理工作负载的注意事项和建议

本文将介绍使用面向 TensorFlow 的英特尔® 优化* 进行 CPU 推理的性能注意事项
Authored by Nathan Greeneltch (Intel) Last updated on 08/09/2019 - 02:02
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面向英特尔® 架构优化的 Caffe*:使用现代代码技巧

This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
Authored by Last updated on 07/06/2019 - 16:40
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Maximize TensorFlow* Performance on CPU: Considerations and Recommendations for Inference Workloads

This article will describe performance considerations for CPU inference using Intel® Optimization for TensorFlow*
Authored by Nathan Greeneltch (Intel) Last updated on 07/31/2019 - 12:11
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Caffe* Optimized for Intel® Architecture: Applying Modern Code Techniques

This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
Authored by Last updated on 07/06/2019 - 16:40
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Modernizing Software with Future-Proof Code Optimizations

by Henry A. Gabb, Sr. Principal Engineer, Intel Software and Services Group

Authored by Henry Gabb (Intel) Last updated on 07/06/2019 - 17:10
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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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Ways to Speed up your Cloud Environment and Workload Performance on Intel® Architecture

Setting up a cloud environment is complicated, and it involves multiple elements such as database, network infrastructure, security, etc., (depending on the need).  How do you increase the p

Authored by Thai Le (Intel) Last updated on 07/04/2019 - 17:05