Get Started
ID
766885
Date
11/07/2023
Public
Get Started with the AI Tools
The following instructions assume you have installed the AI Tools software. Please see the AI Tools page for installation options.
Follow these steps to build and run a sample with the AI Tools:
NOTE:
Standard Python installations are fully compatible with the AI Tools, but the Intel® Distribution for Python* is preferred.
No special modifications to your existing projects are required to start using them with these tools.
Components
The AI Tools include:
- 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® 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 AI Tools 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.