Intel(R) AI Analytics Toolkit Samples

These code samples are built to support the Intel(R) AI Analytics Toolkit powered by oneAPI.

AI Analytics Toolkit Features

Optimized Deep Learning Frameworks

Deep learning frameworks provide a high-level programming language to architect, train, and validate deep neural networks. Popular frameworks, such as TensorFlow and PyTorch, are directly optimized to fully use the power of Intel(R) architecture and yield high performance for training and inference.

High-Performance Python

Python has become the most popular and fastest growing programming language for AI and data analytics. Intel(R) Distribution for Python includes accelerated compute intensive packages that are heavily used in machine learning and data science, such as NumPy, SciPy, scikit-learn, XGBoost. The algorithms are optimized for Intel(R) architectures and take advantage of the underlying instruction set to maximize performance. The distribution also includes daal4py, a pythonic interface to Intel’s oneAPI Data Analytics Library.

Data Analytics

Implement data science and analytics pipelines—preprocessing through machine learning—and scale-out efficiently using the high-performing oneAPI Data Analytics Library, part of the foundational Intel(R) oneAPI Base Toolkit. The library’s set of high-speed algorithms (such as analysis functions, math functions, and training and prediction functions) enable applications to analyze large data sets with available compute resources and make better predictions faster.

Ai Analytics Toolkit contains different examples listed in below table:

Type	Name	Language
Component	Tensorflow Hello World	Python
Component	PyTorch Hello World	Python
Component	oneAPI Data Analytics Library Hello World	Python
Component	TensorFlow with int 8	Python
Segment	Distributed Linear Regression using oneAPI Data Analytics Library	Python
Segment	Distributed K-means using oneAPI Data Analytics Library	Python

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