Trilha Machine Learning - Portfólio Intel para Inteligência Artificial: Usando Python e Tensorflow em Machine e Deep Learning

1. Intel´s Artificial Intelligence Portfolio: Machine and Deep
Learning
Optimize Tensorflow and Python using Intel MKL

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Agenda
 Objective
 Road to high performance
 Optimize Frameworks through libraries
 Case: Tensorflow on Keras API

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• Show how to optimize Python´s AI Frameworks
• Present Intel Math Kernel Library (MKL)
• Show how it is done with Tensorflow
• Aware how important is to configure before running
Objective

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• Today, we make software like a production line;
• The new wave: Intelligent Systems >> More Software!
• This Software uses Math, a lot of Math;
• We need hardware to execute calculations more faster!
Road to high performance

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• We have free access to several Frameworks to start developing
• Python dominates! (https://spectrum.ieee.org/computing/software/the-
2017-top-programming-languages)
• Question: How about:
• Vectorization?
• Multithreading?
• Distributed computing?
Road to high performance

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Vectorization

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Multithreading
MKL_NUM_THREADS, KMP_AFFINITY, OPEN MP THREADS

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Distributed Computing

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PARALLEL SOLUTION

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Case: Tensorflow on Keras
• Running a low workload for neural network
• Running the script unaware of threads used
• Adjustments on thread usage by script
• New results

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Conclusion
• When you develop for AI, you should to consider hardware
performance
• You do not need to refactor your code, you can signal
hardware before execution
• If you seek for more results, try using libs focused on
performance

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References
• Intel MKL: https://software.intel.com/en-us/mkl
• Intel Anaconda Repo: https://anaconda.org/intel/repo
• Intel Nervana: https://www.intelnervana.com/
• Intel Vtune: https://software.intel.com/en-us/intel-vtune-
amplifier-xe

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Questions?

14. THANK
YOU
diegoX.duarte.menescal@intel.com