As per our Data Science Skills Study 2018, Python is the most used language by data scientists, with 44% of respondents using it for application building and scientific & numeric computing.
One of the main reasons for Python’s soaring popularity is that it has one of the largest programming communities in the world and offers a number of libraries which a data scientist can use to analyse large amounts of data. In terms of data visualization, Python offers a number of libraries like Pandas or Matplotlib. The study further revealed that 41% data scientists prefer Pandas over other libraries.
As Python inches towards supremacy, a lot of emphasis is now being laid on how to improve the platforms that run Python and the use of its machine learning libraries.
Traditionally, pip has been offering the services of Python packages since the beginning. Later, the introduction of Virtualenv and Anaconda brought in the usage of customised dependencies for web development and machine learning respectively.
Anaconda, especially, became popular as a platform that hosts a variety of machine learning tools. Now it has joined hands with Intel® to boost performance of Python. The collaboration of Intel® and Anaconda comes at a crucial intersection where speed stands as a major hurdle for training deep learning algorithms.
Intel® Distribution for Python was first publicly made available in 2017. Since then it has undergone many developments and today it supports a multitude of machine learning tasks for Python users.
Today, Intel® ‘s latest developments with Python can assist the following class of developers:
- Machine Learning Developers, Data Scientists and Analysts
- Numerical and Scientific Computing Developers
- High-Performance Computing (HPC) Developers
Intel® Distribution for Python is a binary distribution of Python interpreter and commonly used packages for computation and data intensive domains, such as scientific and engineering computing, big data, and data science.
Intel® Distribution for Python supports Python 2 and 3 for Windows, Linux, and macOS. The product simplifies Python installation by providing packages in a binary form so that everything is preconfigured and no compilation tools are needed, as well as contains all the dependencies for running on popular OS platforms.
How Does Intel® Boost Python’s Performance
Many Python numerical packages, such as NumPy and SciPy, take advantage of all available CPU cores by using multithreading inherently. However, performance can degrade during multithreading using Python.
Intel® ‘s composable parallelism helps resolve this by coordinating the threaded components—with little to no intervention from the user. This can lead to improved application performance.
Intel® has a list of benchmarks that it has set thanks to its ever improving results that combine its hardware with software.
The following benchmarks show the efficiency of optimized functions— for example, functions used for numerical computing, scientific computing and machine learning— and compare Intel® Distribution for Python to its respective open source Python packages.
All benchmarks measure Python against native C code equivalent, which is considered to be representative of optimal performance. The higher the efficiency, the faster the function and closer to native C speed.
Here are few comparisons to get a glimpse at how Intel® fares:
- Linear algebra1
2. Machine Learning2
Intel® Distribution for Python is also supported by their flagship product Intel® Parallel Studio XE, which is a powerful, robust suite of software development tools to write Python native extensions.
This helps boost application performance by taking advantage of the ever-increasing processor core counts and vector register widths available in processors based on Intel® technology and other compatible processors.
The packages have been optimized to take advantage of parallelism through the use of vectorization, multithreading and multiprocessing, as well as through the use of optimized communication across multiple nodes.
Python continues to reign as the tool of choice thanks to its versatility. While both Python and R are open source languages, Python is a more general-purpose language with a readable syntax.
Mostly used in data mining, analysis, scientific computing and machine learning, it contains powerful statistical and numerical packages for data analysis such as PyBrain, NumPy and MySQL.
It can automate mundane tasks, build web applications and websites from scratch, enable scientific and numeric computing, be used in robotics, and more. Python is known to be intuitive, easy to work with and solve complex computational problems.
Whereas, with Intel® ’s distribution for Python, the developers can:
- Achieve faster Python* application performance—right out of the box—with minimal or no changes to your code
- Accelerate NumPy*, SciPy* and Scikit-learn* with integrated Intel® Performance Libraries such as Intel® Math Kernel Library and Intel® Data Analytics Acceleration Library
- Access the latest vectorization and multithreading instructions, Numba* and Cython*, composable parallelism with Threading Building Blocks and more
The new release of Intel® ‘s Distribution for Python now offers many performance improvements, including:
- Faster machine learning with scikit-learn key algorithms accelerated with Intel® Data Analytics Acceleration Library
- The XGBoost package included in the Intel® Distribution for Python (Linux* only)
- The latest version3 has a new distributed model support for “Moments of low order” and “Covariance” algorithms through daal4py package.
Intel® ‘s Distribution for Python is also included in their flagship product, Intel® Parallel Studio XE, which contains a powerful, robust suite of software development tools that help write Python native extensions such as C and Fortran compilers, numerical libraries, and profilers. Intel® ‘s Python Distribution has an edge over open source Python platforms because of their ever-increasing processor core counts and vector register widths available in processors that help boost application performance.
It also comes with optimized deep learning software, Caffe and Theano, as well as classic machine learning libraries, like scikit-learn and pyDAAL.
Python packages have been accelerated with Intel® Performance Libraries, including Intel® Math Kernel Library (Intel® MKL), Intel® Threading Building Blocks (Intel® TBB), Intel® Integrated Performance Primitives (Intel® IPP), and Intel® Data Analytics Acceleration Library (Intel® DAAL).
From hardware that excels at training massive, unstructured data sets, to extreme low-power silicon for on-device inference, Intel® AI has been supporting cloud service providers, enterprises and research teams with a portfolio of multi-purpose, purpose-built, customizable and application-specific hardware.
Over the past couple of years, Intel® has optimized open source libraries like nGraph, which supports training and inference across multiple frameworks and hardware architectures; developed the Intel® Distribution of OpenVINO™ toolkit to quickly optimize pretrained models and deploy neural networks for video to a variety of hardware architectures; and created BigDL, distributed deep learning library for Apache Spark and Hadoop clusters.
Now, with their ever-improving Python packaging and distribution, it is safe to say that Intel now possesses a unique set of tools that are diverse and are well suited for any modern machine learning pipeline.
Get hands on with Intel® ‘s Distribution for Python here
Product & Performance Information
1 Source: Intel® Distribution for Python BUILT FOR SPEED AND SCALABILITY
2 Source: Intel® Distribution for Python BUILT FOR SPEED AND SCALABILITY
3Source: Intel® Distribution for Python* 2019 Update 4
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