How to Learn AI Skills on Windows 11 PC
December 7, 2022
NVIDIA now has options for those with Windows 11 PCs to access its AI platform, which has been adopted by universities around the world and has made it easier for STEM students to learn artificial intelligence skills.
Students need access to the same technology used by research and industry as they are students in STEM (science, technology, engineering, and math) disciplines, such as computer science, data science, data science, and economics. In this way the NVIDIA RAPIDS software package, now coming to Windows 11 Powered by NVIDIA GeForce RTX GPUs, students can perform many mathematical operations at once, for example, they can perform the calculations necessary for AI, and other functions What’s included in the platform are open source software libraries and APIs for GPU-accelerated data science.
The open source software and APIs that make up a RAPIDS suite, gives you the ability to run end-to-end data science and analytics pipelines entirely on GPUs. He also has an Apache 2.0 license and extensive experience in hardware and data science, which is incubated by NVIDIA®.
Using NVIDIA CUDA® primitives, RAPIDS achieves low-level computing optimization and exposes GPU parallelism as well as high-bandwidth memory speed through easy-to-use Python interfaces.
It focuses on common data preparation tasks for data science and analytics, such as a familiar data framework API which integrates with a variety of machine learning algorithms that achieves end-to-end pipeline speedups without the need to pay typical serialization costs, and RAPIDS also includes support for multi-node and multi-GPU deployments, which makes for much faster processing and training on larger data set sizes.
Apache Arrow and GoAi are based on a columnar in-memory data structure, which were the first RAPIDS projects, and have fast and efficient data exchange, along with flexibility to support complex data models.
LIBRARIES AND APIS OVERVIEW
cuDF is a pandas-like data frame manipulation library; cuGraph is an accelerated graph analysis library similar to NetworkX; and cuML is a collection of machine learning libraries that provides GPU versions of algorithms available in scikit-learn. These are some of the RAPIDS projects that are included and with development following a 6-week release schedule, providing new features and libraries all the time.
INTEGRATION WITH DEEP LEARNING LIBRARIES
Data stored in Apache Arrow can be seamlessly sent to deep learning frameworks that accept array_interface or work with DLPack, since RAPIDS has native support for array_interface, and can work with Chainer, MXNet, and PyTorch.
The Python approach makes RAPIDS work well with most data science visualization libraries, resulting in higher performance. Also, work is underway to achieve deeper integration with these libraries, as some high-performance, high-FPS data display capabilities, which are by a native GPU in-memory data format.
Students: Learn AI and Data Science on GeForce RTX
AI is becoming one of the most important technologies in the world every day as it impacts the global economy and students must pay attention to this and have experience in artificial intelligence. In 2022, AI is one of the most in-demand technological skills, and according to Forbes, it is estimated that in the next four years there will be almost 100 million AI-related jobs, which means that there are new courses and educational programs focused on these skills in both universities and colleges.
NVIDIA announced support for the RAPIDS software suite for machine learning, data analysis, and other AI/ML-related techniques, now available on Windows 11 PCs, including NVIDIA GeForce RTX GPU technology. Students can design, develop, and test models and applications for deep learning, from machine learning, to data analysis, to graph analysis, that are accelerated by powerful GPUs, and from a Windows 11 device.
How GPUs Help Students
GPUs are parallel processors, which can do many mathematical operations at the same time, it can also render video games and perform the calculations necessary for artificial intelligence. They are also credited as one of the three key innovations that led to modern AI. Using laptops with GeForce RTX GPUs, students can finish coursework faster and not rely on computer labs.
Announcing New Technology and Learning Tools
Now using Windows 11 running on Windows Subsystem for Linux you have support for a core software package and a key learning resource, which were previously only available on Linux.
- NVIDIA RAPIDS: Based on the popular PyData stack, as well as NVIDIA’s open source software libraries and APIs for GPU-accelerated data science, including data analysis, graph analysis, geospatial analysis, signal analysis, and learning traditional automatic.
- Learning Deep Learning: Part of the NVIDIA Deep Learning Institute (DLI), with foundational training content in the book, which students can use to augment or direct their learning of these powerful techniques.
How Students Can Get Started with NVIDIA’s AI Platform on GeForce RTX PCs
Here are some resources students can use to set up their own PC to learn and develop for AI:
- Setting up: instructions to help you quickly set up your Windows 11 desktop or laptop powered by RTX.
- Learning Deep Learning: an essential book by Magnus Ekman takes you from basic techniques to applications of deep learning, and can be found in print or electronically.
- Learning RAPIDS: has tools and additional information on using RAPIDS in WSL.