NVIDIA AI Blueprints show developers how to build complex solutions using NVIDIA software. In this hands-on training lab, you'll build an interactive AI-powered research assistant that includes: A stylized digital human avatar for interactivity. A retrieval-augmented generation (RAG) system for multimodal …

Harness Langflow and NVIDIA NeMo Framework to design and deploy powerful, multi-agent solutions. Join DataStax for an interactive training lab where you'll learn to design and deploy a custom shopping assistant using Langflow's intuitive drag-and-drop interface and NVIDIA NeMo Framework. From data …

Push the boundaries of edge computing by leveraging the power of NVIDIA Jetson alongside the seamless connectivity of AWS IoT Greengrass. In this hands-on lab, you’ll learn how to deploy generative AI microservices on Jetson devices, both directly and through the cloud using AWS IoT Greengrass. …

In this introductory, hands-on course, learners will explore NVIDIA Cosmos™, a platform of generative world foundation models (WFM), advanced tokenizers, guardrails, and an accelerated data processing and curation pipeline built to accelerate the development of physical AI. Prerequisite(s): An …

In this hands-on lab, we'll show you how to write custom analysis scripts, called recipes, to gain deeper insights into the performance of your application using the Analysis System of the Nsight Systems profiler. We'll walk through multiple examples to show the Nsight Analysis System functions that can …

Join our in-depth exploration of cutting-edge techniques to accelerate portfolio optimization on GPUs. We'll unpack how to formulate optimization problems in terms of risk-reward trade-offs under various constraints. We'll discuss how to transform traditionally sequential optimization algorithms into parallel…

In this hands-on lab, we'll using NVIDIA Omniverse and Isaac Sim to generate synthetic data for training AI models that control robotic manipulators. By the end of the lab, we'll provide hands-on experience in developing pipelines to create synthetic datasets, training perception models, and deploying these …

Learn how to use NVIDIA Nsight Graphics to profile and optimize both graphics and compute workloads, focusing on ray tracing applications. You’ll analyze GPU performance through trace data from CUDA, DirectX, and Vulkan, identifying bottlenecks at the source-code and GPU assembly levels. By …

Developing LLMs for non-English languages often faces the challenge of limited or imbalanced datasets. We present a cutting-edge approach to curating high-quality text corpora for languages like Spanish, French, and beyond, leveraging the power of GPU acceleration. We'll delve into the methodologies for …

Generating and understanding images are the two core areas of image-based workflows. We'll cover how the media & entertainment, retail/fashion, and healthcare industries are leveraging diffusion and transformer models together for Gen AI workflows. We'll emphasize emerging use cases while …

Powered by AI, co-pilots are revolutionizing the way industrial workers perform manual tasks, significantly improving operational efficiency and quality. This course will demonstrate how intelligent assistants use computer vision and natural language processing to monitor and optimize repetitive …

Gain next-level insights from PDF documents just by speaking to your data. This hands-on session shows participants how to build and run PDFSpeak, an innovative approach to interacting with complex PDF documents using NVIDIA's cutting-edge AI technologies through speech, vision, and text. …

We'll train you to scale your Gen AI workload and create a software-as-a-service (SaaS) serverless platform. We'll use NIMs of an open-source LLM, scaling it using open-source technologies like Kubernetes, Ray, and KServe, and we'll demonstrate the usage of NVCF. We'll show you how to obtain …

As Gen AI applications become increasingly prevalent, development practices must evolve to meet modern standards. Learn how to address the gap between data science and modern development practices. In this lab you’ll build a scalable and reproducible LLMOps development pipeline. Using off-…

This hands-on lab guides developers through the process of creating a cloud-based IoT application using NVIDIA Omniverse Kit and Microsoft Azure. Participants will learn to develop, deploy, and integrate a containerized Omniverse Kit application with a web-based front-end, focusing on data streaming and …

In this hands-on lab, we'll show you how to harness the power of NVIDIA Base Command Manager and Run:ai to fuel your AI factory. You'll start with a cluster managed by Base Command Manager and learn how to use it to install Run:ai, unlocking its full potential to increase utilization of your AI resources. By …

In this training lab, we'll explore the latest techniques in 3D scene creation using neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS). These methods use machine learning to create detailed 3D scenes from just a few images. Learn how to model dynamic scenes where objects can move and …

Learn to use NVIDIA Nsight Systems to detect bottlenecks in AI pipelines and explore new features to optimize and scale the execution of your applications. We'll guide you through the performance analysis process of GPU-accelerated AI applications that run across multiple, possibly containerized, compute …

Dive into theoretical aspects and practical use cases for compression techniques used in creating more efficient versions of preferred models. Learn the main methods of compression — quantization, pruning, distillation — in detail, and compare trade-offs and options for jointly using multiple compression …

This advanced lab guides developers through creating captivating spatial experiences for Apple Vision Pro, leveraging SwiftUI and Xcode for frontend development while utilizing NVIDIA Omniverse as a powerful backend server. You’ll explore visionOS development techniques to develop immersive user …

Learn how to unlock the full potential of NVIDIA GPUs with the powerful profiling and analysis capabilities of Nsight Compute. AI workloads are rapidly increasing the demand for GPU computing, and ensuring that they efficiently utilize all available GPU resources is essential. Nsight Compute is the most …

Generative AI and foundational models are transforming healthcare and life sciences by enhancing efficiency, driving breakthroughs, and enabling tailored approaches to care. These technologies are creating a future where innovation changes how care is given, making patient outcomes and …

Learn how to choose the autoscaling hyperparameters for your LLM applications by understanding the key metrics during inference. Gain essential tools to optimize latency and throughput by running and dissecting LLM inference benchmarks. See how NVIDIA's benchmarking software can be leveraged to …

Explore the future of retail with spatial computing. In this hands-on lab, we'll have the opportunity to create an application for the Apple Vision Pro (AVP) that allows users to configure a photoreal, 3D asset. This includes how to develop an application and set it up to communicate with a product configurator …

Learn to build an agent framework to tackle the multi-turn complex task, how to add tools, and multi-turn agents behavior. Learn how to build conversational AI agents that can tackle complex tasks with ease, and explore new techniques for multi-turn conversations and persona-based interactions. …

"Dead pipelines" — abandoned scripts that once ran parts of a workflow — plague LLM development, causing errors, delays, and wasted resources. Learn how to automate the entire LLM life cycle using CI/CD pipelines, Kubernetes, NVIDIA NIMs, and NeMo Microservices in a cloud-agnostic approach. …

With ever-increasing amounts of data and limited compute resources, training competitive ML models end-to-end can take hours, days, or even weeks. Parallel computing offers a solution. Join us to learn how to reduce your end-to-end ML pipeline and increase model accuracy by parallelizing and distributing …

Robots hold massive potential to automate and scale difficult and repetitive tasks across industries and in our lives. Programming robots to conduct these tasks safely has historically been challenging, costly, and specialized. Robotics simulation enables developers to virtually train, test, and validate robots in …

Learn how to create AI agents using LangGraph and NVIDIA NIM to automate 5G network configurations. You'll deploy LLM agents to monitor real-time network quality of service (QoS) and dynamically respond to congestion by creating new network slices. LLM agents will process logs to detect when QoS falls …

This lab will introduce MatX and demonstrate how to accelerate latency-sensitive workflows with its easy-to-use C++ template library for numerical computing. MatX's powerful Tensor language enables rapid prototyping and a seamless transition to production. Using this library, we'll demonstrate a powerful …

This hands-on lab explores the creation of interactive avatar applications using the NVIDIA Tokkio pipeline, building on the next generation digital human blueprint. Participants will learn to customize the Tokkio pipeline with ACE microservices for updating the avatar character and the end-to-end web …

Learn how to run an multi-modal end-to-end pipeline generating 3D assets using your own voice. We'll use state-of-the-art NIM Agent LLMs with Langchain, Langgraph, and Langserve to create an agentic pipeline transforming from text to 2D to 3D, generating 3D assets to be imported via CAD Converter, …

Vector databases are a hot topic today for semantic search, and they're powering everything from generative AI workloads to recommender systems. This lab will provide hands-on instruction for optimizing vector database workloads on the GPU, including (1) optimizing ingest and index creation …

Using NVIDIA technologies, you will build configurable tools and workflows for agentic AI. Learn to deploy an agentic AI workflow, create tools for an AI agent, and quickly augment existing workflows with new tools. With these workflows and tools, you'll increase productivity when dealing with multiple …

The RAPIDS Accelerator for Apache Spark leverages the distributed big data processing framework of Spark along with the power of GPUs to greatly speed up ETL and analytics workloads while reducing cost. In this training lab, we'll walk through the RAPIDS Accelerator for Apache Spark, including running…

Explore how NVIDIA Earth-2 facilitates efficient weather and climate modeling. Learn how to run a large and growing stack of global AI weather forecasting models and how downscaling models generate super-resolution outputs. Discover use cases and applications benefiting the most from this emerging …

This training explores the principles behind multimodal generative AI, focusing on text-to-video diffusion models and their practical applications. We will begin by defining multimodal generative AI and examining the core mechanisms of text-to-video synthesis. Participants will learn how input …

Evaluate and optimize an LLM customer assistant, ensuring it performs robustly, accurately, and with no hallucinations in real-world scenarios. Leverage the NeMo Evaluator Microservice to create unit tests to track Gen AI model quality continuously during the development: Optimize accuracy in your local …

In recent years, the intersection of artificial intelligence and biology has opened new frontiers in scientific discovery. With the ability to process vast amounts of biological data, accelerated analysis and foundation model approaches are transforming digital biology, uncovering patterns that were …

In this hands-on lab, explore an end-to-end approach for building domain-specific large language models. Learn how to curate domain-specific datasets, design and train custom tokenizers, and execute the pre-training process to tailor LLMs for specialized applications. You'll gain practical skills and …

Learn how to accelerate ROS 2 workloads using NVIDIA’s latest GPU-powered libraries for AI and robotics. This hands-on lab will explore how to leverage generative AI and GPU-accelerated ROS 2 packages to enhance robotic workloads in real time. Gain experience with optimizing ROS 2 packages for GPU …

As adoption of LLMs and retrieval-augmented generation (RAG) becomes more prevalent in enterprise, the demand for robust evaluation arises, ensuring these new tools meet the high regulatory standards of various industries. Gain an in-depth understanding of the unique requirements as well as practical …

This training lab explores the cutting-edge capabilities of NVIDIA's suite of AI tools—NVIDIA Inference Microservices (NIMs) and NVIDIA AI Blueprints—in transforming digital health solutions. Participants will gain hands-on experience in developing sophisticated AI-driven applications that can converse with …

Clustering is commonly applied across industries for applications such as recommendation systems and fraud detection. In this lab, through examples, we'll discuss techniques to accelerate common clustering algorithms and tips/tricks to derive the highest performance. Prerequisite(s): Python, beginner …

In this hands-on lab, learn to develop an AI-powered application that combines the capabilities of NVIDIA NIM microservices with the NVIDIA Omniverse platform to create real-time, AI-driven experiences. You’ll start by building an Omniverse Kit-based application and then integrate NIM microservices like Edify3D…

Embark on an exploration with CUDA-Q and learn how to use it to bring together quantum algorithms with machine learning and generative AI to elevate quantum computing. This hands-on session facilitates smooth navigation between classical and quantum domains, driving innovation in hybrid …

Learn how to integrate large language models (LLMs) with NVIDIA Inference Microservices (NIM) and cuGraph to create cutting-edge, graph-based AI solutions for handling complex, interconnected data. We'll cover fine-tuning techniques, Langchain agents, and GPU-accelerated graph analytics to enhance…

Vision-language models (VLM) are taking computer vision by storm, offering scalability and robust zero-shot solution for countless industries. In this lab, you will learn how to use VLM NIMs and the Video Search and Summarization AI Blueprint (VSS). You will get hands on experience calling VLM NIM APIs …

Discover how to create your own visually perceptive AI agents to analyze video. In this session, you'll construct an agentic retrieval-augmented generation (RAG) pipeline by using an Agentic AI Toolkit to integrate NVIDIA Riva Speech Services NIM and the VSS blueprint. We’ll demonstrate a practical …

With datasets growing rapidly in volume, velocity, and veracity, the demand for more efficient data processing has never been higher. This lab walks through how to apply open-source GPU accelerators from the NVIDIA RAPIDS project for common Python data science workflows. Prerequisite(s): …

Simulation-based reinforcement learning greatly benefits the field of robotics, enabling models to learn complex tasks quickly. NVIDIA Isaac Lab offers a reinforcement learning environment to train and test robots in realistic simulated environments using physics-based rendering. We'll dive deeper into …

First we'll discuss what an LLM is and some of the strengths and weaknesses of these models, looking at a handful of models and approaches. We'll cover the difference between pre-training and fine-tuning and discuss input processing by showing how to take an input string and tokenize it into input ids. We'll …

This hands-on training lab demonstrates how to build end-to-end medical AI workflows using the latest tools: MONAI Label, VISTA-3D, MAISI, and VILA-M3. Learn to combine AI-assisted annotation, interactive segmentation, synthetic data generation, and advanced visual-language understanding to create …

This lab will introduce the NVIDIA Base Command Manager software and describe the best practices for managing AI infrastructure. You’ll gain hands-on experience with provisioning cluster nodes, managing software images, creating users and groups, deploying Kubernetes, running a containerized workload, …

In this course, we will be expanding on the concepts of assets from our previous Learn OpenUSD courses and applying them within the domain of robotics. We’ll learn about the benefits of a robot asset structure in OpenUSD and learn best practices by studying the asset structure utilized by the URDF …

Universities are at the forefront of nurturing the next generation in the emerging technologies of accelerated computing, data science, robotics and AI. NVIDIA Deep Learning Institute (DLI) Teaching Kits lower the barrier of incorporating AI and GPU computing in coursework with freely downloadable …

Along with the demand for real-time insights and autonomous decision-making comes the need for scalable edge solutions that can process AI-enabled sensor data at the source and scale out to on-premises or cloud. However, several formidable challenges lie ahead: Real-time latency requirements. …

Feature engineering is an important component in (tabular) machine learning solutions, which can be easily integrated into existing models and boost model accuracy. The tabular data structure limits the models' capabilities to learn the relationships between features, so adding handcrafted features can …

Agents have been all the rage recently, with many frameworks advertising agentic capabilities, new use-cases popping up left and right, and some truly amazing software innovations that keep forcing us to redefine limits and expectations. We'll boil down the agent abstraction to its roots, discuss how the …

Explore more training options offered by the NVIDIA Deep Learning Institute (DLI). Choose from an extensive catalog of self-paced, online courses or instructor-led virtual workshops to help you develop key skills in AI, HPC, graphics & simulation, and more.Ready to validate your skills? Get NVIDIA …

Explore more training options offered by the NVIDIA Deep Learning Institute (DLI). Choose from an extensive catalog of self-paced, online courses or instructor-led virtual workshops to help you develop key skills in AI, HPC, graphics & simulation, and more.Ready to validate your skills? Get NVIDIA …

Explore more training options offered by the NVIDIA Deep Learning Institute (DLI). Choose from an extensive catalog of self-paced, online courses or instructor-led virtual workshops to help you develop key skills in AI, HPC, graphics & simulation, and more.Ready to validate your skills? Get NVIDIA …

In this hands-on lab, learn how to take advantage of Universal Scene Description (OpenUSD) to accelerate your Extended Reality (XR) development and enhance visual fidelity like never before. This session will equip you with the skills and tools necessary to build, customize, and stream your own …

High-fidelity simulations in science and engineering are computationally expensive and time-prohibitive for quick iterative use cases, from design analysis to optimization. NVIDIA PhysicsNeMo, the physics machine learning platform, turbocharges such use cases by building physics-based deep learning …

Learn how to get started with AI-assisted annotation using MONAI Label. MONAI Label is an open-source image labeling and learning tool that allows researchers to create novel AI models and collaborate with a clinical team. You’ll get a short introduction to MONAI Label and then dive deep into creating your own …

Graph neural networks (GNNs) are an increasingly popular class of artificial neural networks designed to process data that can be represented as graphs. The two prominent GNN frameworks are the Deep Graph Library (DGL) and PyTorch Geometric (PyG). The RAPIDS cuGraph effort has been working on …

Vision transformers (ViTs) are taking computer vision by storm, offering incredible accuracy and robust solutions for countless industries. However, there are many practical challenges to deploying ViTs, including pre-training, fine tuning, deploying, and managing the complexity of such a large model. …

The new wave of interest in large language models (LLMs) brought vector databases to the forefront of technologies for AI applications. We'll clearly need accurate and performant query results from vector databases to meet an ever-growing demand for data and sophisticated applications, which is where …

The growth in AI is driving the need for substantial compute infrastructure in data centers to train and deploy models. The right cluster management tools are critical for managing this infrastructure at scale and ensuring its optimal utilization. This lab will introduce the NVIDIA Base Command Manager …

Learn how to use NVIDIA AI Workbench as the focal point in generative AI workflows. We'll explain how other elements of NVIDIA AI Enterprise interoperate with AI Workbench. Within an AI workflow, we'll give a step-by-step example of how AI Workbench can be used to execute a project on a Lenovo …

Join AI experts to learn how to build a production-ready application that can reduce spear phishing — one of the largest and most costly cyberthreats to organizations. Customized for individuals and usually very convincing, spear phishing emails are difficult to defend against due to a lack of training data. …

To use insights from proprietary data and conversations with customers worldwide to build better products and serve customers, companies deploy large pre-trained language models and external retrieval and search mechanisms into their conversational applications such as chatbots, virtual …

Large language models (LLMs) have recently captured significant attention in healthcare. Their stellar performance across a range of applications highlights their potential for vital tasks like medical question-answering. However, LLMs can produce "hallucination" errors, generating overly confident answers …

Join us for this hands-on lab and learn how to harness the power of large language models (LLMs) and Jetson AGX Orin to build next-generation AI applications that can see, hear, and speak. You'll have the opportunity to work with LLMs in a real-world setting, fine-tuning and using them for tasks like image …

Join NVIDIA specialists for a hands-on lab where we'll introduce the basics of the Omniverse development platform. You'll learn how to get started building 3D applications and tools that deliver the functionality needed to support industrial use cases and workflows for aggregating and reviewing large facilities …

Kinetica, in partnership with NVIDIA, introduces a large language model engine that seamlessly translates natural language requests into SQL queries. Using the power of GPUs, their database offers advanced geospatial capabilities. You'll discover the magic behind creating an LLM for user-friendly …

First, we'll discuss what a large language model (LLM) is and list some of the strengths and weaknesses of these models, looking at a handful of models and approaches. We'll explain the difference between pre-training and fine-tuning. We'll discuss Input processing by showing the steps of taking an input …

Building vision AI applications for the edge often requires long, costly development cycles. A powerful new collection of NVIDIA Metropolis APIs and microservices helps you accelerate the development and deployment of vision AI applications on NVIDIA Jetson Orin. We'll dive deeper into these microservices …

Multimodal generative AI has recently seen significant advancements, enabling the creation of realistic images from textual or other inputs. Due to the complexity of these models, understanding how they function and how to apply them in practical settings can be challenging. We'll walk you through the …

Join us for an exciting introduction to foundation models in generative AI! In this training lab, we'll explore the basics of foundation models, their significance, applications, and the latest developments in the field of AI. Whether you're an academic, industry professional, or anyone eager to dive into the …

Retrieval-augmented generation (RAG) systems show great promise for business applications, but a naive approach simply pairing a vector database retriever with a general-purpose large language model (LLM) rarely leads to high-quality results. Learn techniques that can take your RAG system from an …

Autonomous mobile robots (AMRs) are a key element of industrial automation. Training AMRs in the real world is often a difficult, expensive process and prevents many from taking advantage of their tremendous potential. Combining remote control car platforms such as F1Tenth and JetRacer with …

Learn to take your ISO C++ application to the next level by using the C++ parallel algorithms to accelerate your code on GPUs and multicore CPUs. C++ is one of the most widely-used programming languages and provides a rich set of parallel building blocks for your application. Learn how to …

The RAPIDS Accelerator for Apache Spark leverages the distributed big data processing framework of Spark along with the power of GPUs to greatly speed up extract-transform-load and analytics workloads while reducing cost. In this training lab, we'll walk through the RAPIDS Accelerator for Apache Spark, …

Our introduction to the Isaac Robot Operating System will include: A hands-on tutorial showing how to use the Isaac ROS dev container; How to run Isaac ROS perception packages on Jetson; and A demo of how to test a robot in simulation with hardware in the loop, using Isaac SIM and Isaac ROS. …

Join AI experts to learn how to use the NVIDIA cuOpt cloud service to minimize vehicle routing inefficiencies by finding the most optimal routes for a heterogeneous fleet of vehicles making deliveries, pickups, dispatching jobs, and more. Bring your laptop to get access to the latest NVIDIA enterprise …

Get ready to master the art of steering AI towards accuracy in the medical domain. In this training lab, we’ll learn how to develop “guardrails” to ensure your AI stays on track and delivers trustworthy results. We’ll uncover how Nemo Guardrails can avert AI hallucinations by employing robust filters, including …

In this training lab, you will learn the benefits of using reinforcement learning to train robot behaviors. Then you'll learn how to train robots quickly and easily with NVIDIA Isaac Sim in AWS Cloud. Finally, you'll learn to scale training for robots across multiple GPU nodes to expedite performance using AWS …

The demand for accelerated large language models (LLMs) has surged with the growing popularity of generative models. These models, often boasting billions of parameters, hold immense potential, but also pose challenges during large-scale deployments. Join us as we delve into the world of accelerated …

In a world where AI's evolution within the metaverse, digital twins, and simulations is accelerating, there is a widespread eagerness to learn how to navigate this rapidly advancing landscape. The technological landscape has transformed significantly since the initial introduction of Omniverse. Join us to navigate …

Robotics applications have grown in popularity recently, with robots being used in industries including manufacturing, healthcare, and agriculture (among others) to increase production, quality, efficiency, and safety. And with the help of other technologies such as artificial intelligence, sensor technologies, …

Enterprises are increasingly seeking ways to automate their data management processes to save time and reduce manual labor. This training lab will focus on the practical use of multimodal Retrieval-Augmented Generation (RAG) agents to automate routine tasks involving databases such as MySQL, …

The potential clinical benefits of combining the power of mRNA expression with the rich context of tissue morphology are considerable. Equally impressive is the amount of data preparation and processing needed to perform this analysis. In the absence of well-established pipelines dedicated to this analysis,…

Transformer-based large language models (LLMs) have revolutionized the ways to understand and explore massive datasets and enable us to generate and augment relevant data efficiently. Initially applied for natural language processing tasks, LLMs have extended applicability in understanding …

Retrieval augmented generation (RAG) pipelines are already changing every aspect of modern enterprise operation. There are countless online tutorials demonstrating proof-of-concept-level naïve RAG applications incapable of dealing with large volumes of traffic and large document volumes. This …

Create a synthetic version of a real-world object. Synthetic datasets help increase training and effectiveness of neural networks, but sometimes creating them to mimic the complex real world is difficult. Learn how to capture a complex physical object, filter the image capture for best results, …

In this hands-on lab, you’ll unlock the power of OpenUSD to build a real-time configurator in Omniverse. Along the way you’ll learn about workflows, asset considerations, and USD composition concepts that you can apply directly to your own development process. In the lab you’ll author a Variant set, add …

AI Industrial applications present unique challenges, including the need for near-perfect accuracy (99%+) and high throughput. These already demanding requirements are further complicated by the widespread issue of data scarcity in the industrial sector, attributed to factors such as data ownership, …

Learn how NVIDIA's profiling tools, Nsight Systems and Nsight Compute, can help you accelerate modern compute workloads. In the first half of this lab, you'll get hands-on experience with optimizing CUDA applications using Nsight Systems, which is a system-wide performance analysis tool that helps you …

In this introductory level training lab, we will cover the fundamentals of working with Universal Scene Description (OpenUSD) - an open and extensible ecosystem for describing, composing, simulating, and collaborating within 3D worlds. Learn how you can use USD for non-destructive workflows, how layers …

DeepVariant is one of the most popular variant callers in genomics right now. It uses image-based deep learning methods to predict mutations in a genome. DeepVariant comes with a pre-trained model out of the box that works very well across a wide set of scenarios, but it's possible to train new models for …

Synthetic data generation (SDG) is a data augmentation technique necessary for increasing the robustness of models by supplying additional data to train models. We'll explore the use of Transformers for synthetic tabular data generation in the context of credit card transactions. We'll use the Megatron …

The goal of the vehicle routing problem (VRP) is to identify an optimal set of routes for a fleet of vehicles visiting multiple delivery locations. The changing economic and geopolitical scenario has made last-mile delivery one of the most challenging and expensive tasks of the logistics fulfillment cycle. In this …

"How much data is enough?" is a common question when fine-tuning or training your own object detection models. In cases where data collection is a limiting factor, we can utilize synthetic generated data. Omniverse Replicator makes generating this 3D data streamlined in a single application, with the…

Learn how to build and run an AI model while being aware of the AI medical project life cycle. You'll get a short introduction on the MONAI Deploy architecture, understanding how it's focused on defining the journey from research innovation to clinical production environments in hospitals. You'll walk through …

With the arrival of NVIDIA RTX and real-time ray tracing APIs like DXR and Vulkan Ray Tracing, it's now easier than ever to create stunning visuals at interactive frame rates. Learn how to utilize NVIDIA Nsight Graphics to profile and optimize 3D applications that use ray tracing. Using an example application, …

This hands-on training lab teaches how to accelerate HPC applications using the portable parallelism and concurrency features of the C++17 and newer standards, without any language or vendor extensions, such that a single version of the code is portable to multi-core CPU and to heterogeneous accelerated …

Transformer-based large language models (LLMs) have revolutionized the ways to understand and explore massive datasets and enable us efficiently generate and augment relevant data. Now, these transformer models have extended applicability in understanding languages of the molecules of life — …

Gain experience with NVIDIA debugger and correctness tools by using CUDA-GDB and compute-sanitizer's functionalities on multiple small CUDA sample applications. Learn the basics of Nsight Visual Studio Code Edition and CUDA-GDB — how to build a project, how to start the debugger, detect and …

Learn the basics of working with digital humans in Omniverse Create as you embark on a journey to populate a virtual world. Omniverse Create provides cutting-edge capabilities for 3D content simulation and visualization, forming an important first step in the digital human creation pipeline. Learn about …

Learn how to get started with AI-assisted annotation using MONAI Label and OHIF or 3D Slicer as your image viewer, and walk through creating your own MONAI Label application. MONAI Label is an open-source image labeling and learning tool that allows researchers to create novel AI models and …

Write your first graph neural network, complete with automatic feature engineering, visualization, and deployment, in this lab using popular open source libraries: PyGraphistry[AI], NVIDIA RAPIDS (cuDF, cuGraph, cuML), and GPU neural network ecosystems (DGL, PyG, TF). Graph neural networks are in a …

Universal Scene Description (USD) is transforming 3D data modeling across various industries and is poised to be the open standard that enables the 3D evolution of the internet — the metaverse. In this hands-on training, we'll present what makes USD unique and the fundamentals for describing, …

During this training lab, you'll learn how to move video processing workflows to the GPU to benefit from hardware acceleration with the NVIDIA Video Codec SDK and NVIDIA DALI. We'll use JAX and PyTorch, but you'll be able to apply the techniques presented in a broad set of frameworks and technologies. You'll …

Federated learning has emerged as a promising solution to the problem of data privacy and locality in training robust AI models. By enabling distributed model training without data sharing, federated learning allows researchers and data scientists to train generalizable models across diverse, …

Session-based recommendation (SBR) has been an important task in online services like ecommerce and news portals, where users may have very distinct interests in different sessions. SBR models have been proposed to model the sequence of interactions within the current user session. They have gained …

DeepVariant is one of the most popular variant callers in genomics right now. It uses image-based deep learning methods to predict mutations in a genome. DeepVariant comes with a pre-trained model out of the box that works very well across a wide set of genomes; however, for more advanced applications, …

Master NVIDIA's profiling tools for CUDA and AI workloads. After learning how to use Nsight Systems to identify and solve inefficiencies and scalability issues on the application level, you'll deep-dive into the challenges of tuning your CUDA kernels for maximum efficiency with Nsight Compute. Take a tour …

This hands-on training lab teaches how to accelerate HPC applications using the portable parallelism and concurrency features of the C++17 and newer standards, without any language or vendor extensions, such that a single version of the code is portable to multi-core CPUs and to heterogeneous accelerated …

Join Mark III, an NVIDIA Elite Partner, for an Introduction to machine learning (ML) tutorial and an interactive, practical lab on using ML techniques to see anomalies in sensor and machine data and predict when maintenance failures might occur. You'll receive access to a Jupyter Notebook guided lab …

NVIDIA Holoscan is a highly optimized platform for processing high-speed, low-latency streaming data generated by sensors at the edge. It can be used to build streaming AI pipelines for a variety of domains, including medical devices, high performance computing at the edge, industrial inspection, …

Learn about the basics of Omniverse and get started on your journey to generate realistic life-like simulations as a first step to creating your digital twin. Start to integrate our powerful and unique PhysX features into your workflow. Take it a step further and explore the magic of Omniverse Warp. Warp and …

In recent years, deep learning has shown impressive performance in medical imaging analysis. However, for a model to be useful in the real world, it needs to be reliable, besides being valid and interpretable. The uncertainty quantification (UQ) methods determine the calibrated level of a model's confidence in …

Learn about Auto3DSeg, a low-code framework for building 3D medical image segmentation models using MONAI. Researchers and data scientists need a common foundational framework to perform training experiments and compare against the state of the art. MONAI provides domain-specific …

This training introduces the book "Learning Deep Learning" (LDL) from NVIDIA Deep Learning Institute (DLI). "LDL" was published by Pearson in 2021 and teaches deep learning from scratch to people with a programming or computer science/engineering background. Without requiring any prior machine …

NVIDIA RAPIDS accelerates Python data science workflows from visualization and discovery to production inference. Its tremendous coverage across a wide range of important algorithms gives data scientists amazing superpowers. However, superheroes sometimes prefer to chart their own courses, prototyping…

Omniverse Replicator augments costly, laborious human-labeled real-world data, which can be error prone and incomplete, with the ability to create large and diverse physically accurate data tailored to the needs of autonomous vehicle and robotics developers. It also enables generating ground-truth data …

Explore new techniques for building simulation tools with a focus on usability for the industrial end user. The industrial automation space is in desperate need of better simulation tools, but most people interacting with automation are simulation and programming novices. This highlights the need for …

Deep learning (DL) has shown impressive capabilities across various fields. However, the growing size of DL models is outpacing hardware capacity, resulting in a demand for large models to be trained and inferenced more efficiently and easily. We'll introduce a user-friendly DL system, Colossal-AI, that …

Training robots requires high-fidelity simulation. In addition, it requires a multitude of data extracted from a broad variety of scenes. Generating this training data can require considerable compute resources running simulations in parallel. In this session, get hands on experience running NVIDIA …