1 Navigate machine learning With InTEL ® DISTRIBUTION FOR Python* Victoriya

competitive advantage across industries
strategy provides the foundation for success using

AI

Intel® Math Kernel Library (Intel® MKL & MKL-DNN)

Intel® Data Analytics Acceleration Library (Intel® DAAL)

+Network
+Memory +Storage

Datacenter

Endpoint

Solutions for reference across industries
Tools/Platforms to accelerate deployment
Optimized Frameworks to simplify development
Libraries/Languages featuring optimized building blocks
Hardware Technology portfolio that is broad and cross-compatible

Intel® Deep Learning SDK for Training & Deployment

Intel® Distribution for Python*

of Java/C++ programmers …
OR
Have team of Python programmers to deploy

optimized Python in production

Python is among the most popular programming languages

Challenge #1:
Domain specialists are not professional software programmers

* L.Prechelt, An empirical comparison of seven programming languages, IEEE Computer, 2000, Vol. 33, Issue 10, pp. 23-29
** RedMonk - D.Berkholz, Programming languages ranked by expressiveness

Versions: Intel® Distribution for Python 2017 Beta, icc 15.0; Hardware:

Intel® Xeon® CPU E5-2698 v3 @ 2.30GHz (2 sockets, 16 cores each, HT=OFF), 64 GB of RAM, 8 DIMMS of 8GB@2133MHz; Operating System: Ubuntu 14.04 LTS.

Up to 100x faster

Up to 10x faster!

Up to 10x faster!

Up to 60x faster!

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

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

decompositions

Not so basic functions
SciKit-learn
Linear regression
NOTE: Only Python 2.7 and 3.5

are supported for now

Performance Libraries
Mpi4py*
py
DAAL
Scikit-learn*

1)
System info: 32x Intel® Xeon® CPU E5-2698 v3 @ 2.30GHz,

disabled HT, 64GB RAM; Intel® Distribution for Python* 2017 Gold; Intel® MKL 2017.0.0; Ubuntu 14.04.4 LTS; Numpy 1.11.1; scikit-learn 0.17.1. See Optimization Notice.

Speedup

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

Speedup

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

(De-)Compression
(De-)Serialization

PCA
Outlier detection
Normalization
Math functions
Sorting



Statistical moments
Quantiles
Distances
Variance matrix
Distances
QR, SVD, Cholesky
Apriori
Optimization solvers

Regression
Linear
Ridge
Classification
Naïve Bayes
SVM
Classifier boosting
kNN
Decision Forest
Clustering
Kmeans
EM GMM
Collaborative filtering
ALS

Neural Networks

Quality metrics

Available also in open source: https://software.intel.com/en-us/articles/opendaal

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)

2.2x

66x

Balanced read and compute

60% faster CSV read

from smaller ones
If each component is threaded there can be

too much!
Intel TBB dynamically balances thread loads and effectively manages oversubscription

> python -m TBB application.py

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

full installer from
https://software.intel.com/en-us/intel-distribution-for-python
> conda config --add channels intel
> conda install

intelpython3_full
> conda install intelpython3_core

docker pull intelpython/intelpython3_full

user might want to buy depending on his/her preferences

Linux* built Python*: Python 2.7.5 (default, Feb 11 2014), NumPy

1.7.1, SciPy 0.12.1, multiprocessing 0.70a1 built with gcc 4.8.2; Hardware: 24 CPUs (HT ON), 2 Sockets (6 cores/socket), 2 NUMA nodes, Intel(R) Xeon(R) X5680@3.33GHz, RAM 24GB, Operating System: Red Hat Enterprise Linux Server release 7.0 (Maipo)

Items similarity assessment (similarity matrix computation) is the main hotspot

Linux* built Python*: Python 2.7.5 (default, Feb 11 2014), NumPy

1.7.1, SciPy 0.12.1, multiprocessing 0.70a1 built with gcc 4.8.2; Hardware: 24 CPUs (HT ON), 2 Sockets (6 cores/socket), 2 NUMA nodes, Intel(R) Xeon(R) X5680@3.33GHz, RAM 24GB, Operating System: Red Hat Enterprise Linux Server release 7.0 (Maipo)

This loop is major bottleneck. Use appropriate technologies (NumPy/SciPy/Scikit-Learn or Cython/Numba) to accelerate

~5% of all the execution time

Configuration info: 96 CPUs (HT

ON), 4 Sockets (12 cores/socket), 1 NUMA nodes, Intel(R) Xeon(R) E5-4657L v2@2.40GHz, RAM 64GB, Operating System: Fedora release 23 (Twenty Three)

“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.