These code samples are built to support the Intel(R) AI Analytics Toolkit powered by oneAPI.
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.
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.
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 | Tensorflow ResNet50 Inference | Python |
| Component | Intel Model Zoo | 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 |
The code samples are licensed under MIT license