Get Started

Get Started with the Intel® AI Analytics Toolkit for Linux*

ID 766885
Date 3/31/2023

Get Started with the Intel® AI Analytics Toolkit

The following instructions assume you have installed the Intel® oneAPI software. Please see the Intel AI Analytics Toolkit page for installation options.

Follow these steps to build and run a sample with the Intel® AI Analytics Toolkit (AI Kit):

  1. Configure your system.
  2. Build and Run a Sample.

Standard Python installations are fully compatible with the AI Kit, but the Intel® Distribution for Python* is preferred.

No special modifications to your existing projects are required to start using them with this toolkit.

Components of This Toolkit

The AI Kit includes:

  • Intel® Optimization for PyTorch*: The Intel® oneAPI Deep Neural Network Library (oneDNN) is included in PyTorch as the default math kernel library for deep learning.
  • Intel® Extension for PyTorch:Intel® Extension for PyTorch* extends PyTorch* capabilities with up-to-date features and optimizations for an extra performance boost on Intel hardware.
  • Intel® Optimization for TensorFlow*: This version integrates primitives from oneDNN into the TensorFlow runtime for accelerated performance.
  • Intel® Extension for TensorFlow: Intel® Extension for TensorFlow* is a heterogeneous, high performance deep learning extension plugin based on TensorFlow  PluggableDevice  interface. This extension plugin brings Intel XPU (GPU, CPU, etc) devices into  the TensorFlow  open source community for AI workload acceleration.
  • Intel® Distribution for Python*: Get faster Python application performance right out of the box, with minimal or no changes to your code. This distribution is integrated with Intel® Performance Libraries such as the Intel® oneAPI Math Kernel Library and the Intel®oneAPI Data Analytics Library.
  • Intel® Distribution of Modin* (available through Anaconda only), which enables you to seamlessly scale preprocessing across multi nodes using this intelligent, distributed dataframe library with an identical API to pandas. This distribution is only available by Installing the Intel® AI Analytics Toolkit with the Conda* Package Manager.
  • Intel® Neural Compressor : quickly deploy low-precision inference solutions on popular deep-learning frameworks such as TensorFlow*, PyTorch*, MXNet*, and ONNX* (Open Neural Network Exchange) runtime.
  • Intel® Extension for Scikit-learn*: A seamless way to speed up your Scikit-learn application using the Intel® oneAPI Data Analytics Library (oneDAL).

    Patching scikit-learn makes it a well-suited machine learning framework for dealing with real-life problems.

  • XGBoost Optimized by Intel: This well-known machine-learning package for gradient-boosted decision trees includes seamless, drop-in acceleration for Intel® architectures to significantly speed up model training and improve accuracy for better predictions.

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