Презентация IDP for Machine Learning онлайн

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Содержание слайда: Machine Learning: Your Path to Deeper Insight Driving increasing innovation and competitive advantage across industries strategy provides the foundation for success using AI

Содержание слайда: Motivation

Содержание слайда: Intel® Distribution for Python* Advancing Python performance closer to native speeds

Содержание слайда: Performance Gain from MKL (Compare to “vanilla” SciPy)

Содержание слайда: Out-of-the-box Performance with Intel® Distribution for Python* Mature AVX2 instructions based product Configuration Info: apt/atlas: installed with apt-get, Ubuntu 16.10, python 3.5.2, numpy 1.11.0, scipy 0.17.0; pip/openblas: installed with pip, Ubuntu 16.10, python 3.5.2, numpy 1.11.1, scipy 0.18.0; Intel Python: Intel Distribution for Python 2017 Hardware: Xeon: Intel Xeon CPU E5-2698 v3 @ 2.30 GHz (2 sockets, 16 cores each, HT=off), 64 GB of RAM, 8 DIMMS of 8GB@2133MHz

Содержание слайда: Out-of-the-box Performance with Intel® Distribution for Python* New AVX512 instructions based product Configuration Info: apt/atlas: installed with apt-get, Ubuntu 16.10, python 3.5.2, numpy 1.11.0, scipy 0.17.0; pip/openblas: installed with pip, Ubuntu 16.10, python 3.5.2, numpy 1.11.1, scipy 0.18.0; Intel Python: Intel Distribution for Python 2017 Hardware: Intel Intel® Xeon Phi™ CPU 7210 1.30 GHz, 96 GB of RAM, 6 DIMMS of 16GB@1200MHz

Содержание слайда: WORKSHOP: BASIC functions

Содержание слайда: Examples of Basic Functions NumPy, SciPy Matrix multiplication Random number generation Vector Math Linear algebra decompositions Not so basic functions SciKit-learn Linear regression NOTE: Only Python 2.7 and 3.5 are supported for now

Содержание слайда: Intel Python Landscape

Содержание слайда: Scikit-Learn* optimizations with Intel® MKL Speedups of Scikit-Learn* Benchmarks (2017 Update 1)

Содержание слайда: More Scikit-Learn* optimizations with Intel® DAAL Speedups of Scikit-Learn* Benchmarks (2017 Update 2) Accelerated key Machine Learning algorithms with Intel® DAAL Distances, K-means, Linear & Ridge Regression, PCA Up to 160x speedup on top of MKL initial optimizations

Содержание слайда: Intel® DAAL: Heterogeneous Analytics Targets both data centers (Intel® Xeon® and Intel® Xeon Phi™) and edge-devices (Intel® Atom™) Perform analysis close to data source (sensor/client/server) to optimize response latency, decrease network bandwidth utilization, and maximize security Offload data to server/cluster for complex and large-scale analytics

Содержание слайда: Performance Example : Read And Compute SVM Classification with RBF kernel Training dataset: CSV file (PCA-preprocessed MNIST, 40 principal components) n=42000, p=40 Testing dataset: CSV file (PCA-preprocessed MNIST, 40 principal components) n=28000, p=40 System Info: Intel® Xeon® CPU E5-2680 v3 @ 2.50GHz, 504GB, 2x24 cores, HT=on, OS RH7.2 x86_64, Intel® Distribution for Python* 2017 Update 1 (Python* 3.5)

Содержание слайда: WORKSHOP: PyDAAL

Содержание слайда: pyDAAL Getting Started https://github.com/daaltces/pydaal-getting-started DAAL4PY: Tech Preview https://software.intel.com/en-us/articles/daal4py-overview-a-high-level-python-api-to-the-intel-data-analytics-acceleration-library

Содержание слайда: Intel® TBB: parallelism orchestration in Python ecosystem Software components are built from smaller ones If each component is threaded there can be too much! Intel TBB dynamically balances thread loads and effectively manages oversubscription

Содержание слайда: Profiling Python* code with Intel® VTune™ Amplifier Right tool for high performance application profiling at all levels Function-level and line-level hotspot analysis, down to disassembly Call stack analysis Low overhead Mixed-language, multi-threaded application analysis

Содержание слайда: Installing Intel® Distribution for Python* 2017 Stand-alone installer and anaconda.org/intel OR

Содержание слайда: Intel® Distribution for Python

Содержание слайда: backup

Содержание слайда: Collaborative Filtering Processes users’ past behavior, their activities and ratings Predicts, what user might want to buy depending on his/her preferences

Содержание слайда: Training: Profiling pure python*

Содержание слайда: Training: Profiling pure Python*

Содержание слайда: Training: Python + Numpy (MKL) Much faster! The most compute-intensive part takes ~5% of all the execution time

Содержание слайда: Legal Disclaimer & Optimization Notice INFORMATION IN THIS DOCUMENT IS PROVIDED “AS IS”. NO LICENSE, EXPRESS OR IMPLIED, BY ESTOPPEL OR OTHERWISE, TO ANY INTELLECTUAL PROPERTY RIGHTS IS GRANTED BY THIS DOCUMENT. INTEL ASSUMES NO LIABILITY WHATSOEVER AND INTEL DISCLAIMS ANY EXPRESS OR IMPLIED WARRANTY, RELATING TO THIS INFORMATION INCLUDING LIABILITY OR WARRANTIES RELATING TO FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR INFRINGEMENT OF ANY PATENT, COPYRIGHT OR OTHER INTELLECTUAL PROPERTY RIGHT. Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information about compiler optimizations, see our Optimization Notice at https://software.intel.com/en-us/articles/optimization-notice#opt-en. Copyright © 2017, Intel Corporation. All rights reserved. Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries. *Other names and brands may be claimed as the property of others.

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