GPU Computing
CUDA, accelerated computing, inference optimization, and GPU-centric AI workflows.
Explore how to use Numba—the just-in-time, type-specializing Python function compiler—to create and launch CUDA kernels to accelerate Python programs on massively parallel NVIDIA GPUs.
Building Real-Time Video AI Applications
NVIDIA DLI
Learn the skills you need to enable real-time transformation of raw video data from widely-deployed camera sensors into deep learning-based insights.
An Even Easier Introduction to CUDA
NVIDIA DLI
An interactive accompaniment to Mark Harris's popular blog post An Even Easier Introduction to CUDA.
Learn to write simple, portable, parallel-first applications using only standard C++ language features that can be compiled without modification to take advantage of NVIDIA GPU-accelerated environments.
Organizations analyze large amounts of tabular data to uncover insights, improve products and services, and achieve efficiency.
Explore how to use Numba—the just-in-time, type-specializing Python function compiler—to create and launch CUDA kernels to accelerate Python programs on massively parallel NVIDIA GPUs.
Learn to write simple, portable, parallel-first applications using only standard C++ language features that can be compiled without modification to take advantage of NVIDIA GPU-accelerated environments.
Learn how to use two powerful NVIDIA developer tools: Nsight Systems and Nsight Compute.
Learn how to write, compile, and run GPU-accelerated code, leverage CUDA core libraries to harness the power of massive parallelism provided by modern GPU accelerators, optimize memory migration between CPU and GPU, and implement your own algorithms.
Fundamentals of Accelerated Data Science
NVIDIA DLI
Data science is about using scientific methods, processes, algorithms, and systems to analyze and extract insights from data.
Data science is about using scientific methods, processes, algorithms, and systems to analyze and extract insights from data.
Learn how foundation models can reveal novel biological insights, while leveraging NVIDIA’s platform to accelerate the entire workflow for fast, scalable results.
Learn to build custom Python analysis scripts using the Nsight Analysis System to programmatically identify and summarize application performance bottlenecks.
Learn how to build responsive, interactive dashboards for large datasets using GPU-accelerated Python libraries.
Learn to profile and optimize AI pipelines using NVIDIA Nsight Systems to identify and eliminate performance bottlenecks in GPU-accelerated applications.
Master the essential model compression techniques to deploy high-performance, cost-efficient Large Language Models at scale.