IntelSoftware/Numba_DPPY_Essentials

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IntelSoftware/Numba_DPPY_Essentials

IntelSoftware

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Data Parallel Python

ue4-parallel

Demo of parallel-for flocking algorithm on Unreal4

AIGamedevToolkit

Foundation layer for AI Gamedev Toolkit which can be built upon by dev community

OpenGLBestPracticesfor6thGenIntelProcessor

Game developers often use OpenGL to handle the rendering chores for graphics-intensive games. OpenGL is an application programming interface for efficiently rendering two- and three-dimensional vector graphics. The code samples are a series from Grahics API developer guide for for 6th generation Intel® Core™ processor (https://software.intel.com/en-us/articles/6th-gen-graphics-api-dev-guide) that demonstrates how to get the most out of OpenGL 4.4 and higher.

Machine-Learning-using-oneAPI

Machine Learning using oneAPI. Explores Intel Extensions for scikit-learn* and NumPy, SciPy, Pandas powered by oneAPI

Tutorial-Password-Manager-with-Intel-SGX

This sample code demonstrates a password manager utilizing Intel SGX.

unity-parallel-gpu

Jurassic

Jupyter Notebook

ForestFirePrediction

Forest fire prediction using finetuning on CPU with MODIS and NAIP aerial photos and resnet with acceleration using Intel Extensions for PyTorch

Jupyter Notebook

Intel-Extensions-for-Scikit-learn-Essentials-for-Machine-Learning

Jupyter Notebook

aigamedevtoolkit-starter-demos

Demos intended to be run with AI GameDev Toolkit (separate download)

Python-Loop-Replacement-with-NumPy-and-PyTorch

Python Loop Replacement with NumPy and PyTorch - Fancy Slicing, UFuncs and equivalent, Aggregations, Sorting and more

Jupyter Notebook

unity-parallel-cpu

particle_fountain_vulkan_gpu

PC-Skills-Framework

Introduction_to_Machine_Learning

Introduction to Machine Learning with focus on Scikit-learn* algorithms and how to accelerate those algorithms with a couple of line of code on CPU using Intel Extensions for Scikit-learn

Jupyter Notebook

particle_fountain_vulkan_cpu

NumPy_Optimizations

Exercises to replace loops with NumPy function equivalents to gain 10X to 100sX acceleration over simple minded python loop access

Jupyter Notebook

scikit-learn_essentials

Course demonstrating how to using SYCL context and Intel(R) Extensions for scikit-learn* to optimize selected sklearn algorithms and target them for gpu

Jupyter Notebook

PyTorch_Optimizations

Describe how Intel SIMD and Cache optimization provided by Intel oneMKL-DNN as well as the Intel Extensions for PyTorch can accelerate your pytorch workloads especially prior to training loop or during post processing. Also explore how to use Intel Extensions to PyTorch and how to access Intel GPU for PyTorch

Jupyter Notebook

DL-using-oneAPI

Focus will be on Deep Learning optimizations using oneAPI

Jupyter Notebook

Finetuning

Use Finetuning in PyTorch to derive GIS based model predictors using CPU with few iterations

Jupyter Notebook

Intel_oneAPI_MKL_Training

This is a series of sample exercises demonstrating how to use oneMKL

Jupyter Notebook

Intel_AI_2022_Webinar_Series

sd_ws

Material for the Diffusers Workshop on ITDC

Jupyter Notebook

AI-PC_Notebooks

Jupyter Notebook

SYCL_101

From zero to oneAPI Hero

GPU-Occupancy-Calculator

Intel GPU Occupancy Calculator for HPC Application Development