Accelerate Data Science & AI Pipelines

AI is found in almost every industry vertical, with applications and use cases that are transforming businesses and conferring a competitive advantage.

The Intel® AI Analytics Toolkit gives developers, researchers, and data scientists 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 maximizes performance from preprocessing through machine learning.

Using this toolkit, you can:

• Deliver high-performance deep learning (DL) training on CPUs and integrate fast inference into your AI applications with Intel-optimized DL frameworks: TensorFlow* and PyTorch*, pretrained models, and low-precision tools. 
• Achieve drop-in acceleration for data analytics and machine learning workflows with compute-intensive Python* packages: Intel® Distribution of Modin*, NumPy, SciPy, scikit-learn*, and XGBoost*.
• Gain direct access to Intel analytics and AI optimizations to ensure that your software works together seamlessly.

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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.

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Optimized Deep Learning Frameworks

• 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 tools.

High-Performance Python*

• Take advantage of the most popular and fastest growing programming language for AI and data analytics.
• Use its drop-in, compute-intense packages—optimized for Intel® architecture and underlying instruction sets—to accelerate AI applications and maximize performance.
• Leverage Scalable Dataframe Compiler that extends Numba to compile and accelerate NumPy and Pandas.

Simplified Scaling across Multinode DataFrames

• Seamlessly scale Pandas workflows to multicores and multinodes with only one line of code change using the Intel Distribution of Modin, an extremely light-weight parallel DataFrame.
• Accelerate data analytics with high-performance backends, such as OmniSci

• 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 daal4py, a Python interface to oneAPI Data Analytics Library (oneDAL).

Intel® Optimization for TensorFlow*

In collaboration with Google, TensorFlow has been directly optimized for Intel® architecture using the primitives of 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 oneAPI Collective Communications Library (oneCCL) for efficient distributed training.

Intel® Distribution for Python*

Achieve fast math-intensive workload performance without code changes for data science and machine learning problems.

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® Distribution of Modin*

Scale data preprocessing across multinodes using this intelligent, distributed DataFrame library with an identical API to pandas. The library integrates with OmniSci in the backend for accelerated analytics. This component is available only via Anaconda distribution of the toolkit. To download and install, refer to the Installation Guide.