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Используемые фильтры

This sample illustrates how to set up and train an XGBoost* model on datasets for prediction. It uses Intel optimized XGBoost from AI Kit.

This sample code shows how to get started with scaling out the training of a neural network to multiple compute nodes in a cluster using Intel TensorFlow and Horovod in AI Kit

This sample code shows how to scale DL training to multi-nodes using Intel PyTorch and oneCCL bindings, part of AI Kit

This sample demonstrates the performance advantage of using Intel TF from AI Kit vs Stock TF

This sample shows how to use Intel Modin from AI Kit.

This sample shows how to use Intel Low Precision Optimization Tool from AI Kit.

This sample will demonstrate how to take advantage of Auto mixed-precision which is part of Intel optimization for PyTorch

Part 3 of a quick start guide for provisioning of Intel® Optane™ persistent memory modules. This part focuses on provisioning within Windows

Part 2 of a quick start guide for provisioning of Intel® Optane™ persistent memory modules. This part focuses on provisioning within Linux*.

Part 1 of a quick start guide for provisioning of Intel® Optane™ persistent memory modules.

Intel Optane Persistent Memory and DAOS break the world record for filesystem performance - ranks #1 on the Virtual Institute for I/O IO500.

TensorFlow* and Intel® oneAPI Deep Neural Network Library (oneDNN) optimized with Open MPI*, Horovod*, and Jupyter Notebook*; CentOS*.