Developer Resources for Energy
A hub of SDKs, technical resources, and more for developers working in the energy industry.
App Frameworks and SDKs
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.
Explore HPC SDK
NVIDIA AI Enterprise
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.
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Demystifying AI for Petroleum Engineers
Find out AI fundamentals, techniques, and tips for petroleum engineers to build scalable models and powerful applications from SPE Digital Energy Technical Section and NVIDIA.
Generate Business Value for Utilities with Data Science
AES, a leading renewable energy producer, deployed 50 AI/ML models in 2 years to accelerate predictive maintenance, energy trading, shipping, logistics, and more. Learn how they used an MLOps solution from Dell Technologies, Domino Data Lab, and NVIDIA.
Expediting Seismic Facies Analysis
Learn how to use NVIDIA DGX A100 and RAPIDS for high-performance machine learning to expedite unsupervised seismic facies analysis.
Advancing the Future of Energy with High-Performance AI
Beyond Limits and NVIDIA share insights on transparent Cognitive AI, and implementation of novel solutions in upstream and downstream using GPU-accelerated model-free, deep reinforcement learning.
Optimizing Power Grids and Renewable Energy with Dynamic Modeling
Veritone’s edge-deployed AI iterative model generator increased optimization and performance of the distributed energy resources (DERs) under supervisory control by Veritone’s predictive edge controllers.
Accelerate Information Discovery in Energy with AI-Powered NLP
Learn how i2k Connect is unlocking the value of unstructured data in the energy industry using AI, natural language processing, and BERT-based models.
Rock On: Scientists Use AI to Improve Sequestering Carbon Underground
A novel model called U-FNO accelerates carbon sequestration simulations and paves a pathway to climate change mitigation. Explore the results from Stanford, Caltech, Purdue University and NVIDIA.
Siemens Gamesa Taps Digital Twins to Accelerate Clean Energy Transition
Siemens Gamesa is using NVIDIA Modulus and NVIDIA Omniverse to create physics-informed digital twins of wind farms, enabling wake effect simulations 4,000x faster than traditional methods.
Light Me Up: Innovators Redefine Energy Meters for a More Efficient Grid
Utilidata and Anuranet are bringing AI intelligence to the edge of the power grid, unlocking opportunities to lower energy costs, enhancing grid reliability, and accelerating decarbonization progress.
Startup Surge: Utility Feels Power of Computer Vision to Track Its Lines
FirstEnergy and Noteworthy AI used NVIDIA Jetson-powered smart cameras on utility trucks to show how edge computing can automate inspections of millions of power lines, poles, and mounted devices.
In Pursuit of Smart City Vision, Startup Two-i Keeps an AI on Worker Safety
Two-i, a member of NVIDIA inception, created an AI-enabled intelligent video analytics application to detect when individuals near a danger zone and immediately alert others to take quick action, including energy giant ExxonMobil.
Siemens Energy Taps NVIDIA to Develop Industrial Digital Twin of Power Plant in Omniverse
Learn how Siemens Energy is leveraging NVIDIA Omniverse™ and NVIDIA Modulus to build a digital twin that could help save $1.7 billion per year in predictive maintenance.
NVIDIA Deep Learning Institute
The NVIDIA Deep Learning Institute (DLI) offers hands-on training in AI and accelerated computing to solve real-world problems. Training is available as self-paced, online courses or in-person, instructor-led workshops.
High Performance Computing with Containers
Learn how to reduce complexity and improve portability and efficiency of your code by using a containerized environment for HPC application development.
Fundamentals of Accelerated Data Science with RAPIDS
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.
Building Real-Time Video AI Applications
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.
Fundamentals of Accelerated Computing with CUDA C/C++
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.
Deploying a Model for Inference at Production Scale
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.