The Intel® 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 maximizes performance from preprocessing through machine learning.
Using this toolkit, you can:
Deliver high-performance deep learning (DL) training on Intel® XPUs and integrate fast inference into your AI development workflow 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: Modin*, NumPy, Numba, scikit-learn*, and XGBoost* optimized for Intel.
Gain direct access to Intel analytics and AI optimizations 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.
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.
Machine Learning Performance Results for Deep Learning Training on a CPU
Reflecting the broad range of AI workloads, Intel submitted results for Machine Learning Performance Training Release v0.7 in June 2020 for three training topologies: MiniGo, DLRM, and ResNet-50 v1.5. Results in each case demonstrated that Intel continues to raise the bar for training on general purpose CPUs.
Harnessing the new bfloat16 capability in Intel® Deep Learning Boost, the team substantially improved PyTorch performance across multiple training workloads on 3rd generation Intel® Xeon® Scalable processors.
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.
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® Distribution of Modin*(Beta) 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 backend for accelerated analytics. This component is available only via Anaconda distribution of the toolkit. To download and install, refer to the Installation Guide.
NumPy, SciPy: These popular libraries are accelerated with Intel® oneAPI Math Kernel Library (oneMKL) and provide drop-in performance enhancement to the vast ecosystem of statistics, mathematical optimizations, and many other data-centric computations.
Numba*: This just-in-time compiler for decorated Python code allows the latest SIMD features and multicore execution to fully utilize modern CPUs. It is accelerated with Intel® oneAPI Threading Building Blocks (oneTBB).
XGBoost: 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.
scikit-learn*: This popular machine learning Python package is now prebuilt and accelerated with Intel® oneAPI Data Analytics Library (oneDAL), oneMKL, and oneTBB.
daal4py: A Python interface to oneDAL, this combines the one-liner API simplicity, similar to scikit-learn, with automatic scaling over multiple compute nodes.