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.
作者: Gennady F. (Blackbelt) 最后更新时间: 2019/07/05 - 14:54
Article

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.
作者: Nguyen, Loc Q (Intel) 最后更新时间: 2019/07/06 - 16:30
Article

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.
作者: Nguyen, Loc Q (Intel) 最后更新时间: 2019/07/06 - 16:30
Article

Gentle Introduction to PyDAAL: Vol 1 Data Structures

The Intel® Data Analytics Acceleration Library (Intel® DAAL) is written on Intel® architecture optimized building blocks and includes support for all data analytics stages. Data-driven decision making is empowered by Intel® DAAL with foundations for data acquisition, preprocessing, transformation, data mining, modeling and validation.
作者: Preethi Venkatesh (Intel) 最后更新时间: 2018/12/12 - 18:00
Article

Intel® Distribution for Python* versus Non-Optimized Python: Breast Cancer Classification

This case study compares the performance of Intel® Distribution for Python* to that of non-optimized Python using a breast cancer classification. This comparison was done using machine learning algorithms from the scikit-learn* package in Python.
作者: 管理 最后更新时间: 2018/12/12 - 18:00
Article

Optimizing OpenStack* Swift* Performance with PyPy*

Isolate and address challenges, understand different solutions, and learn best-known methods associated with adopting a PyPy* just-in-time interpreter for a leading cloud co

作者: Peter X. Wang (Intel) 最后更新时间: 2019/07/06 - 16:30
Article

Gentle Introduction to PyDAAL: Vol 4 Distributed and Online Processing

Introduction
作者: Preethi Venkatesh (Intel) 最后更新时间: 2018/12/12 - 18:00
Article

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*
作者: Nathan Greeneltch (Intel) 最后更新时间: 2019/07/31 - 12:11
Article

Analytics Speed with Ease: Visual Bag-Of-Words in Python* with Intel® Data Analytics Acceleration Library (Intel® DAAL) High Level API

In the companion article, we concluded that Intel® Data Analytics Acceleration Library (DAAL) efficiently utilizes all resources of your machine to perform faster analytics. Now we will show you how to take advantage of these faster analytics methods with simpler Python* commands, namely with Daal4py interface.
作者: Preethi Venkatesh (Intel) 最后更新时间: 2019/08/27 - 14:30
Article

Visual Bag-Of-Words in Python*: Speed Advantage of Intel® Data Analytics Acceleration Library (Intel® DAAL) over Scikit-learn*

Image recognition with machine learning techniques has achieved significant growth due to advances in recent years in both algorithmic efficiency and hardware performance. Even with these advances, image pre-processing of raw images remains a critical step, especially in larger datasets.
作者: Nathan Greeneltch (Intel) 最后更新时间: 2019/08/27 - 14:30