The Intel® oneAPI AI Analytics Toolkit (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® 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.
The Data Science Workstation lineup from Intel and Intel's OEM partners provides data scientists, data analysts, and developers productive and cost-effective AI development solutions to quickly generate insights for their organizations.
Open, optimized software tools are coupled with optimal compute and memory hardware configurations to deliver the best out-of-the-box developer experience, whether you are prototyping or developing production AI.
High-memory systems can fit large datasets for efficient preprocessing, considerably shortening the time required to sort, filter, label, and transform your data.
Familiar Python*‡ APIs deliver software accelerations of up to 10x to 100x for training and inference.
Together, the hardware and software bundle inside the workstations enables data scientists to easily iterate and analyze data at scale.
Central to the optimized AI software stack of the Data Science Workstation† is the AI Kit that accelerates end-to-end data science and machine-learning pipelines using Python-based tools and frameworks. The components of the toolkit are open and standards-based, while offering both drop-in acceleration with almost no code changes and seamless scaling to multiple nodes and architectures.
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.
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.
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.
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.
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).
In collaboration with Facebook*, this popular deep-learning framework is now directly combined with many optimizations from Intel to provide superior performance on Intel® architecture. This package provides the binary version of latest PyTorch release for CPUs, and further adds extensions and bindings from Intel with Intel® oneAPI Collective Communications Library (oneCCL) for efficient distributed training.
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.
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 architectures 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.
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.