Using NumPy, SciPy and scikit-learn? Now Get Blazing Fast Python Performance!  
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Get Blazing Fast Python Performance!

Whether you are a seasoned high-performance developer or a data scientist looking to speed up your workflows, the Intel Distribution for Python powered by Anaconda delivers an easy-to-install, performance-optimized Python experience to meet even your most demanding requirements.

The all-included, out-of-the box distribution accelerates core Python packages including NumPy, SciPy, pandas, scikit-learn, Jupyter, matplotlib, and mpi4py.

High-Performance Tools for Python

Supercharge all your Python applications on modern Intel platforms with Intel® Distribution for Python*.

The FREE Intel® Distribution for Python powered by Anaconda can be used as a drop-in replacement for your current Python environment to get high performance out of the box. Your Python applications immediately gain significant performance and can further be tuned to extract every last bit of performance using the Intel® VTune™ Amplifier.

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Intel® VTune™ Amplifier XE for performance profiling of Python/C/C++

Available as part of the Intel® Parallel Studio XE 2017 Cluster Edition, this performance analyzer accurately identifies obvious and non-obvious performance bottlenecks in your Python code with native extensions, with low overheads and line-level detail.

It provides efficient profiling techniques can help dramatically improving the performance of your Python* code by detecting time, CPU, and memory bottlenecks.

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Learn More in a Free October 4 Webinar

This webinar highlights the significant performance speed-ups achieved by implementing multiple Intel tools and techniques for high performance Python on collaborative filtering methods benchmarked on the latest Intel® platforms.

A combination of performance profiling with Intel® VTune™ Amplifier XE, accelerated machine learning algorithms in Intel® Data Analytics Acceleration Library and Intel® Distribution for Python*, and enhanced thread scheduling, showcase the individual strengths and combined computation power to drive performance on large scale machine learning workloads.

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