🗊Презентация IDP for Machine Learning

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

Motivation

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

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

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

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

WORKSHOP: BASIC functions

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

Intel Python Landscape

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

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

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

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)

WORKSHOP: PyDAAL

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

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

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

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

Intel® Distribution for Python

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

Training: Profiling pure python*

Training: Profiling pure Python*

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

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.