Intel® AI Analytics Toolkit (AI Kit)
Achieve End-to-End Performance for AI Workloads Powered by oneAPI
Accelerate Data Science & AI Pipelines
The AI Kit gives data scientists, AI developers, and researchers familiar Python* tools and frameworks to accelerate end-to-end data science and analytics pipelines on Intel® architecture. 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.
- Gain direct access to analytics and AI optimizations from Intel to ensure that your software works together seamlessly.
Download the Toolkit
Accelerate end-to-end machine learning and data science pipelines with optimized deep learning frameworks and high-performing Python* libraries.
Try the AI Tools Selector (preview), which allows you to quickly choose and then download the tools you need.
Develop in the Cloud
Build and optimize oneAPI multiarchitecture applications using the latest optimized Intel® oneAPI and AI tools, and test your workloads across Intel® CPUs and GPUs. No hardware installations, software downloads, or configuration necessary. Free for 120 days with extensions possible.
What's New
- Speed up deep learning with Intel® Extension for PyTorch* support for PyTorch 2.0 and Intel® Arc™ A-series graphics cards (beta support), along with new CPU optimizations in Intel® Extension for TensorFlow*.
- Combine the latest advancements in pandas 2.0 with the benefits of parallel and distributed computing in Intel® Distribution for Modin*.
- Accelerate classical machine learning with Intel® oneAPI Data Analytics Library (oneDAL), distributed algorithms in Intel® Extension for Scikit-learn*, and support for Intel® Data Center GPU Max Series in Intel® Optimization for XGBoost*.
- Increase model compression productivity with quantization accuracy debugging and built-in validation for large language models (LLMs) in Intel® Neural Compressor.
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 architecture.
- 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 architecture.
- 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*.
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).
LAIKA Studios* & Intel Join Forces to Expand the Possibilities in Stop-Motion Film Making
See how LAIKA Studios* and the Intel Applied Machine Learning team used tools from the AI Kit to realize the limitless scope of stop-motion animation.
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 Intel® oneAPI Collective Communications Library (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.
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.
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*.
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.
Accelerate Lung Disease Diagnoses with Intel® AI
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.
What’s Included
PyTorch* Optimizations from Intel
Intel is one of the largest contributors to PyTorch*, providing regular upstream optimizations to the PyTorch deep learning framework that provide superior performance on Intel architectures. The AI Kit includes the latest binary version of PyTorch tested to work with the rest of the kit, along with Intel® Extension for PyTorch*, which adds the newest Intel optimizations and usability features.
TensorFlow* Optimizations from Intel
TensorFlow* has been directly optimized for Intel architecture, in collaboration with Google*, using the primitives of Intel® oneAPI Deep Neural Network Library (oneDNN) to maximize performance. The AI Kit provides the latest binary version compiled with CPU-enabled settings, along with Intel® Extension for TensorFlow*, which seamlessly plugs into the stock version to add support for new devices and optimizations.
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.
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 nodes 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.
Intel® Optimization for XGBoost*
In collaboration with the 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 architecture to significantly speed up model training and improve accuracy for better predictions.
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 it, 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.
Data Science Workstations Powered by the AI Kit
Original equipment manufacturer (OEM) partners offer Intel®-based data science workstations, which are laptop, desktop, or tower configurations that include:
- Intel® Core™ or Intel® Xeon® processors that are matched for data science work
- Large memory capacities to enable in-memory processing of large datasets, which shortens the time required to sort, filter, label, and transform your data
- Intel® Optane™ persistent memory that provides an affordable alternative to DRAM for extremely large-capacity workloads and in-memory databases
- AI Kit software with applications and libraries that accelerate end-to-end AI and data analytics pipelines on Intel architecture
The AI Kit comes preinstalled on select OEM data science workstations. Use the Installation Guide to download the AI Kit on your workstation.
- Dell Precision* data science workstations: See the Installation Guide.
- Z by Hewlett Packard Enterprise* data science workstations: AI Kit components are preloaded through the Z by HP Data Science Stack, an application for customizing data science environments.
- Lenovo ThinkStation* and ThinkPad* P series workstations: factory installed.
Documentation & Code Samples
- Installation Guides:
Intel | Anaconda | Docker* | Dell Precision Data Science Workstation - Package Managers: Conda | APT | YUM/DNF/Zypper
- Get Started Guides:
Linux | Windows | Containers | scikit-learn - Release Notes
- Maximize TensorFlow Performance on CPUs: Considerations and Recommendations for Inference Workloads
Training
Specifications
Processors:
- Intel Xeon processors
- Intel Xeon Scalable processors
- Intel Core processors
- Intel Data Center GPU Flex Series
- Intel Data Center GPU Max Series
Language:
- Python
Operating systems:
- Linux
- Windows
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.
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