ActiveState’s Python taps Intel MKL to speed data science and machine learning
Last year Intel became a Python distributor, offering its own edition of the language outfitted with Intels Math Kernel Library (MKL). MKL accelerates data-science-related tasks by using Intel-specific processor extensions to speed up certain operations, a fine fit for a language that has become a staple in machine learning and math-and-stats circles. The Intel Distribution of Python, a repackaging of Continuum Analyticss Anaconda distribution, incorporated MKL support to give Python data science and machine learning packages a boost. Now ActiveState, producers of an enterprise-grade Python, (as well as Ruby, Node.js, and Golang distributions)has brought MKL into its own Python distro. [ Roundup: TensorFlow, Spark MLlib, Scikit-learn, MXNet, Microsoft Cognitive Toolkit, and Caffe machine learning and deep learning frameworks. | Get a digest of the day’s top tech stories in the InfoWorld Daily newsletter. ] The latest versions of ActivePython, for Python 2.7.13 and Python 3.5.3/3.6.0, now use MKL to accelerate NumPy, SciPy, Scikit-learn, Matplotlib, Theano, and other popular Python libraries for number-crunching work and machine intelligence research. The default edition of Python comes without third-party libraries, and the larger and more complex onesespecially the data science and machine learning packagescan be tricky to install and maintain. ActivePython, like other third-party distributions, simplifies the process by bundling the most common libraries with the distribution or automating the installation of those libraries. Most of the packages in ActiveState that benefit from MKL are staple elements in data science workflows: NumPy, for speeding up matrix-related math; Pandas, for working with data sets; and SciPy, which leverages both NumPy and Pandas for more complex work than can be addressed by those packages alone.
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