In this sponsored post, Intel highlights how libraries like the Intel Math Kernel Library can make Python code a success at the start, with no recoding needed.
Built-in vectorization, multithreading, and parallelization let you take advantage of all the cores in each server – or in multiple servers in a cluster. (Photo: Shutterstock/By Casimiro PT)
Developers are increasingly besieged by the big data deluge. Business units want to find the actionable information buried in all that data, and dev teams are more frequently turning to Python to deliver the answers business users are seeking.
But there’s one big drawback: Interpreted Python code just doesn’t have the speed of compiled code, forcing many developers to consider re-writing their big data and machine learning applications in C.
Intel® Distribution for Python, which is absolutely free, uses tried-and-true libraries like the Intel Math Kernel Library (Intel MKL) and the Intel Data Analytics Acceleration Library (Intel DAAL) to make Python code scream right out of the box – no recoding required. Here’s some of the benefits dev teams can expect:
First, number crunching packages like NumPy, SciPy, and scikit-learn are all executed natively, rather than being interpreted. That’s one huge speed boost.
On top of that, daal4py provides screamingly fast machine learning algorithms like K-Means Clustering, Random Forest, Logistic Regression, KNN, SVM, and many more, further speeding processing for data analytics code and enabling analysis in near-real time of all kinds of feeds.
Why does Intel give this away? Because they want coders to get the very most out of their hardware — plain and simple. Built-in vectorization, multithreading, and parallelization let you take advantage of all the cores in each server, or in multiple servers in a cluster.
What kind of performance improvements can you expect? Intel has demonstrated one to two orders of magnitude speed increases – over 200x in some cases. That in itself should have every Python developer running to download IDP.
Yes, it works with Numba and Cython; yes, it works with Intel Threading Building Blocks (Intel TBB) library; and yes, it’s developed from open-source Python – so your existing Python code will just work faster right out of the box.
Every machine learning developer, every data scientist, every analyst who uses Python, every numerical and scientific computer developer who just wants to accelerate compute intensive Python packages like NumPy and mpi4py, every HPC developer looking to unlock the power of modern hardware – actually anyone using Python in production, needs Intel’s Distribution for Python.
Visit Intel Developer Zone, and check out the free tools Intel provides to help make your job easier.
