Intel® oneAPI AI Analytics Toolkit (AI Kit)

Achieve End-to-End Performance for AI Workloads

Accelerate Data Science & AI Pipelines

The Intel® oneAPI AI Analytics Toolkit gives data scientists, AI developers, and researchers familiar Python* tools and frameworks to accelerate end-to-end data science and analytics pipelines on Intel® architectures. The components are built using oneAPI libraries for low-level compute optimizations. This toolkit maximizes performance from preprocessing through machine learning, and provides interoperability for efficient model development.

Using this toolkit, you can:

  • Deliver high-performance, deep-learning training on Intel® XPUs and integrate fast inference into your AI development workflow with Intel®-optimized, deep-learning frameworks for TensorFlow* and PyTorch*, pretrained models, and low-precision tools.
  • Achieve drop-in acceleration for data preprocessing and machine-learning workflows with compute-intensive Python* packages, Modin*, scikit-learn*, and XGBoost, optimized for Intel.
  • Gain direct access to analytics and AI optimizations from Intel to ensure that your software works together seamlessly.

Develop in the Cloud

Get what you need to build, test, and optimize your oneAPI projects for free. With an Intel® DevCloud account, you get 120 days of access to the latest Intel® hardware—CPUs, GPUs, FPGAs—and Intel oneAPI tools and frameworks. No software downloads. No configuration steps. No installations.

Get Access

 

Download the Toolkit

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Benchmarks

These benchmarks illustrate the performance capabilities of the Intel oneAPI AI Analytics Toolkit.

In the News

CERN Uses Intel® Deep Learning Boost & oneAPI to Juice Inference without Accuracy Loss

Researchers at CERN and Intel showcase promising results with low-precision optimizations that exploit heterogeneous operations on CPUs for convolutional Generative Adversarial Networks (GAN).

Learn More

LAIKA Studios* & Intel Join Forces to Expand the Possibilities in Stop-Motion Film Making

See how LAIKA Studios* and Intel’s Applied Machine Learning team used tools from the Intel oneAPI AI Analytics Toolkit to realize the limitless scope of stop-motion animation.

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Accelerate PyTorch* with oneAPI Libraries

Harnessing Intel® Deep Learning Boost and oneAPI libraries, Intel and Facebook* collaboratively improved PyTorch CPU performance across multiple training and inference workloads.

PyTorch with oneDNN

PyTorch with oneCCL

MLPerf Results for Deep Learning Training and Inference

Reflecting the broad range of AI workloads, Intel submitted results for MLPerf Release v.0.7 for training and inference. Results in each use case demonstrated that Intel continues to improve standards for Intel® Xeon® Scalable processors as universal platforms for CPU-based machine learning training and inference.

MLPerf Training | MLPerf Inference

An Open Road to Swift DataFrame Scaling

This podcast looks at the challenges of data preprocessing, especially time-consuming, data-wrangling tasks. It discusses how Intel and OmniSci* are collaborating to provide integrated solutions that improve dataframe scaling.

Listen

Superior Machine-Learning Performance on the Latest Intel® Xeon® Scalable Processors

Intel® Extension for Scikit-learn* gives data scientists the performance and ease-of-use they need to run machine-learning algorithms with a simple drop-in replacement for the stock scikit-learn. This article showcases the speedups achieved on the latest Intel Xeon Scalable processors when compared to processors from NVIDIA and AMD. 

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Optimize Performance of Gradient Boost Algorithms

Intel has been constantly improving training and inference performance for XGBoost algorithms. The following blogs compare the training performance of XGBoost 1.1 on a CPU with third-party GPUs, and showcase how to speed up inference with minimal code changes and no loss of quality.

Training | Inference

Intel® AI-Based Solution Helps Accelerate Diagnosis of Lung Diseases

Accrad developed CheXRad, an AI-powered solution to rapidly detect COVID-19 and 14 other thoracic diseases in the clinics and hospitals of Africa. With the help of Intel, they were able to train, optimize, and deploy in less time and at a lower operational cost than available alternatives.

Learn More

Features

Optimized Deep Learning 

  • Leverage popular, Intel-optimized frameworks—including TensorFlow and PyTorch—to use the full power of Intel architecture and yield high performance for training and inference.
  • Expedite development by using the open-source, pretrained, machine-learning models that are optimized by Intel for best performance. 
  • Take advantage of automatic accuracy-driven tuning strategies along with additional objectives like performance, model size, or memory footprint using low-precision optimizations.
     

Data Analytics and Machine-Learning Acceleration

  • Increase machine-learning model accuracy and performance with algorithms in scikit-learn and XGBoost, optimized for Intel architectures.
  • Scale out efficiently to clusters and perform distributed machine learning by using Intel Extension for Scikit-learn.

High-Performance Python*

  • Take advantage of the most popular and fastest growing programming language for AI and data analytics with underlying instruction sets optimized for Intel® architectures.
  • Process larger scientific data sets more quickly using drop-in performance enhancements to existing Python code.
  • Achieve highly efficient multithreading, vectorization, and memory management, and scale scientific computations efficiently across a cluster.

 

Simplified Scaling across Multi-node DataFrames

  • Seamlessly scale and accelerate pandas workflows to multicores and multi-nodes with only one line of code change using the Intel® Distribution of Modin*, an extremely lightweight parallel DataFrame.
  • Accelerate data analytics with high-performance back ends, such as OmniSci.

What’s Included

Intel® Optimization for TensorFlow*

In collaboration with Google*, TensorFlow has been directly optimized for Intel architecture using the primitives of Intel® oneAPI Deep Neural Network Library (oneDNN) to maximize performance. This package provides the latest TensorFlow binary version compiled with CPU enabled settings (--config=mkl).

 

Intel® Optimization for PyTorch*

In collaboration with Facebook*, this popular deep learning framework is now directly combined with many Intel optimizations to provide superior performance on Intel® architecture. This package provides the binary version of latest PyTorch release for CPUs, and further adds Intel extensions and bindings with Intel® oneAPI Collective Communications Library (oneCCL) for efficient distributed training. 

 

Model Zoo for Intel® Architecture

Access pretrained models, sample scripts, best practices, and step-by-step tutorials for many popular open-source, machine-learning models optimized by Intel to run on Intel Xeon Scalable processors.

 

Intel® Low Precision Optimization Tool

Provide a unified, low-precision inference interface across multiple deep-learning frameworks optimized by Intel with this open-source Python library.

 

Intel Extension for Scikit-learn

Seamlessly speed up your scikit-learn* applications on Intel® CPUs and GPUs across single-node and multi-nodes. This extension package dynamically patches scikit-learn estimators to use Intel® oneAPI Data Analytics Library (oneDAL) as the underlying solver, while achieving the speed up for your machine-learning algorithms. The toolkit also includes stock scikit-learn to provide a comprehensive Python environment installed with all required packages. The extension supports up to the last four versions of scikit-learn, and provides flexibility to use with your existing packages.

 

XGBoost Optimized for Intel® Architecture

In collaboration with XGBoost community, Intel has been directly upstreaming many optimizations to provide superior performance on Intel CPUs. This well-known, machine-learning package for gradient-boosted decision trees now includes seamless, drop-in acceleration for Intel architectures to significantly speed up model training and improve accuracy for better predictions. 

 

Intel® Distribution of Modin*

Accelerate your Pandas workflows and scale data preprocessing across multi-nodes using this intelligent, distributed DataFrame library with an identical API to pandas. The library integrates with OmniSci in the back end for accelerated analytics. This component is available only via the Anaconda* distribution of the toolkit. To download and install, refer to the Installation Guide.

 

Intel® Distribution for Python*

Achieve greater performance through acceleration of core Python numerical and scientific packages that are built using Intel® Performance Libraries. This package includes Numba Compiler*, a just-in-time compiler for decorated Python code that allows the latest Single Instruction Multiple Data (SIMD) features and multicore execution to fully use modern CPUs. You can program multiple devices using the same programming model, DPPy (Data Parallel Python) without rewriting CPU code to device code.

Specifications

Processors:

  • Intel® Xeon® processors
  • Intel® Xeon® Scalable processors
  • Intel® Core™ processors

Language:

  • Python

Operating systems:

  • Linux*

Development environments:

  • Compatible with Intel® compilers and others that follow established language standards
  • Linux: Eclipse* IDE

Distributed environments:

  • MPI (MPICH-based, Open MPI)

Support varies by tool. For details, see the system requirements.

 

Get Help

Your success is our success. Access these support resources when you need assistance.

For additional help, see our general oneAPI Support.

 

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.

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