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AI FRAMEWORKS
Get performance gains ranging up to 10x to 100x for popular deep-learning and
machine-learning frameworks through drop-in Intel® optimizations.
* Deep-Learning Frameworks
* Machine Learning Frameworks
AI frameworks provide data scientists, AI developers, and researchers the
building blocks to architect, train, validate, and deploy models, through a
high-level programming interface. All major frameworks for deep learning and
classical machine learning have been optimized by using oneAPI libraries that
provide optimal performance across Intel® CPUs and XPUs. These Intel® software
optimizations help deliver orders of magnitude performance gains over stock
implementations of the same frameworks. As a framework user, you can reap all
performance and productivity benefits through drop-in acceleration without the
need to learn new APIs or low-level foundational libraries.
Performance Gains
DEEP-LEARNING FRAMEWORKS
INTEL® OPTIMIZATION FOR TENSORFLOW*
TensorFlow* is a widely used deep-learning framework that's based on Python*.
It's designed for flexible implementation and extensibility on modern deep
neural networks.
Intel is collaborating with Google* to optimize its performance on platforms
based on the Intel® Xeon® processor. The platforms use the Intel® oneAPI Deep
Neural Network Library (oneDNN), an open-source, cross-platform performance
library for deep-learning applications. These optimizations are directly
upstreamed and made available in the official TensorFlow release via a simple
flag update, which enables developers to seamlessly benefit from the Intel®
optimizations.
The latest version of Intel® Optimization for TensorFlow* is included as part of
the Intel® oneAPI AI Analytics Toolkit (AI Kit). This kit provides a
comprehensive and interoperable set of AI software libraries to accelerate
end-to-end data science and machine-learning workflows.
oneDNN
AI Kit
Download as Part of the AI Kit
ADDITIONAL DOWNLOAD OPTIONS
Anaconda*
Docker*
PIP*
DOCUMENTATION
Installation Guide
Performance Guide
INTEL® OPTIMIZATION FOR PYTORCH*
The PyTorch* for Python* package provides one of the fastest implementations of
dynamic neural networks to achieve speed and flexibility. Intel and Facebook*
extensively collaborated to:
* Include many Intel optimizations in this popular framework
* Provide superior PyTorch performance on Intel® architectures, most notably
Intel® Xeon® Scalable processors
The optimizations are built using oneDNN to provide cross-platform support and
acceleration.
Intel also provides Intel® Extension for PyTorch* for more capabilities that
have not yet been upstreamed, including:
* Support for automatic mixed precision
* Customized operators
* Fusion patterns
This optimization adds bindings with Intel® oneAPI Collective Communications
Library (oneCCL) for efficient distributed training and is a consolidated
package. It provides the best out-of-box experience to get all of the
performance benefits from PyTorch. The package has the latest versions of:
* Stock PyTorch with Intel® optimizations
* Intel Extension for PyTorch
* oneCCL
Intel® Optimization for Pytorch* is made available as part of the AI Kit that
provides a comprehensive and interoperable set of AI software libraries to
accelerate end-to-end data science and machine-learning workflows.
Intel Extension for PyTorch
oneCCL
AI Kit
Download as Part of the AI Kit
ADDITIONAL DOWNLOAD OPTIONS
Anaconda
Docker
DOCUMENTATION
Installation Guide
APACHE MXNET*
This open-source, deep-learning framework is highly portable, lightweight, and
designed to offer efficiency and flexibility through imperative and symbolic
programming. MXNet* includes built-in support for Intel optimizations to achieve
high performance on Intel Xeon Scalable processors.
ADDITIONAL DOWNLOAD OPTIONS
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PIP
DOCUMENTATION
Installation Guide
Optimization Techniques
Performance Tips
PADDLEPADDLE*
This open-source, deep-learning Python* framework from Baidu* is known for
user-friendly, scalable operations. Built using oneDNN, this popular framework
provides fast performance on Intel Xeon Scalable processors and a large
collection of tools to help AI developers.
DOWNLOAD OPTIONS
Docker
PIP
DOCUMENTATION
Get Started Guide
MACHINE-LEARNING FRAMEWORKS
INTEL® EXTENSION FOR SCIKIT-LEARN*
Scikit-learn* is one of the most widely used Python packages for data science
and machine learning. Intel provides a seamless way to speed up the many
algorithms of scikit-learn on Intel® CPUs and GPUs through the Intel® Extension
for Scikit-learn*. This extension package dynamically patches scikit-learn
estimators to use Intel® oneAPI Data Analytics Library (oneDAL) as the
underlying solver. It achieves the speedup for machine learning algorithms on
Intel architectures, both single and multi-nodes.
The latest version of Intel Extension for Scikit-learn is also included as part
of the AI Kit. It provides a comprehensive and interoperable set of AI software
libraries to accelerate end-to-end data science and machine-learning workflows.
Intel Extension for Scikit-learn
oneDAL
AI Kit
Download as Part of the AI Kit
ADDITIONAL DOWNLOAD OPTIONS
Anaconda
Conda*-Forge
PIP
DOCUMENTATION
Installation Guide
More Details
XGBOOST OPTIMIZED BY INTEL
This is a well-known machine-learning package for gradient-boosted decision
trees. It includes seamless, drop-in acceleration for Intel architectures to
significantly speed up model training and improve accuracy for better
predictions. In collaboration with XGBoost community, Intel has been directly
upstreaming many optimizations to provide superior performance on Intel CPUs.
The latest version of XGBoost that Intel optimizes is included as part of the AI
Kit. It provides a comprehensive and interoperable set of AI software libraries
to accelerate end-to-end data science and machine-learning workflows.
AI Kit
Download as Part of the AI Kit
ADDITIONAL DOWNLOAD OPTIONS
Anaconda
DOCUMENTATION
Installation Guide
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