Get Started with the Intel® AI Analytics Toolkit (Beta)

Follow these steps for the Intel® AI Analytics Toolkit (AI Kit):

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

Migrating Existing Projects

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

Components of This Toolkit

The AI Kit includes:

  • PyTorch*: The Intel® oneAPI Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) is included in PyTorch as the default math kernel library for deep learning. See this article on the Intel® Developer Zone for more details.
  • Intel® Optimization for TensorFlow*: This version integrates primitives from the Intel® oneAPI Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) into the TensorFlow runtime for accelerated performance.
  • 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. The distribution also includes daal4py, a Python module integrated with the Intel® oneAPI Data Analytics Library.

    Note

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

Although not required to run projects, additional programming options and instructions specific to other programming languages are available through the Intel® Data Analytics Acceleration Library.

For more complete information about compiler optimizations, see our Optimization Notice.