Developer Resources for Energy

NVIDIA? CUDA-X, built on top of NVIDIA CUDA?, is a collection of libraries, tools, and technologies that deliver dramatically higher performance - compared to CPU-only alternatives - across multiple application domains, from artificial intelligence (AI) to high performance computing (HPC).

NVIDIA Omniverse? is an extensible, open platform built for 3D virtual collaboration and real-time physically accurate simulation. Omniverse combined with NVIDIA Modulus, a framework for developing physics machine learning neural network models, enables digital twins for autonomous vehicles, smart factories, and more.

The NVIDIA HPC Software Development Kit (SDK) includes the proven compilers, libraries, and software tools essential to maximizing developer productivity and the performance and portability of high-performance computing (HPC) applications.

AI researchers, developers, and data scientists are using NVIDIA AI Enterprise to rapidly deploy, manage, and scale AI workloads in the modern hybrid cloud running on on VMware vSphere with NVIDIA-Certified Systems?.

NVIDIA? Jetson? brings accelerated AI performance to the edge in a power-efficient and compact form factor. Together with NVIDIA JetPack? SDK, these Jetson modules open the door for you to develop and deploy innovative products across all industries.

The NVIDIA EGX? platform delivers the power of accelerated AI computing to the edge with an easy-to-deploy, cloud-native software stack, a range of validated servers and devices, Helm charts to ease deployment of AI applications, and a vast ecosystem of partners who offer EGX through their products and services.

With NGC?, you can quickly deploy AI frameworks with containers, get a head start with pre-trained models or model training scripts, and use domain-specific workflows and Helm charts for the fastest AI implementations, giving you faster time to solution.

NVIDIA Metropolis is an end-to-end application framework built on the NVIDIA EGX platform that simplifies the development, deployment and scale of AI-enabled intelligent video analytics applications, such as retail analytics, traffic management, and automated factory inspections.

The NVIDIA RAPIDS? suite of open-source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. Licensed under Apache 2.0, RAPIDS is incubated by NVIDIA and based on extensive hardware and data science experience.

Learn how to reduce complexity and improve portability and efficiency of your code by using a containerized environment for HPC application development.

Learn how to perform multiple analyses on large datasets using RAPIDS, a collection of libraries that allows end-to-end GPU acceleration for data science.

Learn how AI-based video analytics can unlock insights across many industries such as smart cities, retail space management, hospital health and safety monitoring, and manufacturing defect detection, among others.

Learn how to accelerate and optimize existing C/C++ CPU-only applications to leverage the power of GPUs using the most essential CUDA techniques and the NVIDIA Nsight? Systems profiler.

Learn how to deploy neural networks from a variety of frameworks onto a live Triton Server, measure GPU usage and other metrics with Prometheus, and send asynchronous requests to maximize throughput.