Developer Resources For Retail

A hub of news, SDKs, technical resources, and more for developers working on retail, CPG and restaurant AI solutions.

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App Frameworks and SDKs


NVIDIA Riva

Multimodal conversational AI


NVIDIA Riva is an SDK for building and deploying AI applications that fuse vision, speech and other sensors. It offers a complete workflow to build, train and deploy GPU-accelerated AI systems that can use visual cues such as gestures and gaze along with speech in context.


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NVIDIA Metropolis and DeepStream

AI-enabled video analytics


NVIDIA Metropolis is an application framework that simplifies the development, deployment and scale of AI-enabled video analytics applications from edge to cloud. It includes production ready pre-trained models and TAO Toolkit for training and optimization, DeepStream SDK for streaming analytics, other deployment SDKS, CUD-X libraries and the NVIDIA EGX platform.


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RAPIDS

Data science software


The 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® based on extensive hardware and data science experience. RAPIDS utilizes NVIDIA CUDA® primitives for low-level compute optimization, and exposes GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.


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NVIDIA Omniverse

Simulation and digital twins


NVIDIA Omniverse™ is a powerful multi-GPU real-time simulation and collaboration platform for 3D production pipelines based on Pixar's Universal Scene Description and NVIDIA RTX™technology.


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NVIDIA NGC

Learn More | Explore NGC Containers

NVIDIA NGC™ hosts a catalog of GPU-optimized software for AI practitioners to develop their AI solutions. It also provides access to various AI services including NVIDIA Base Command for model training, NVIDIA Fleet Command to deploy and monitor models, and the NGC Private Registry for securely accessing and managing proprietary AI software. Also, NVIDIA AI Enterprise customers can request support through the NGC portal.

NVIDIA TAO Toolkit

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The NVIDIA TAO Toolkit, built on TensorFlow and PyTorch, is a low-code version of the NVIDIA TAO framework that accelerates the model training process by abstracting away the AI/deep learning framework complexity. The TAO Toolkit lets you use the power of transfer learning to fine-tune NVIDIA pretrained models with your own data and optimize for inference—without AI expertise or large training datasets.

NVIDIA TensorRT

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NVIDIA® TensorRT™, an SDK for high-performance deep learning inference, includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for inference applications.

NVIDIA Triton Inference Server

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NVIDIA Triton™ Inference Server, part of the NVIDIA AI platform, is an open-source inference serving software that helps standardize model deployment and execution and delivers fast and scalable AI in production.It provides AI researchers and data scientists the freedom to choose the right framework for their projects without impacting production deployment. It also helps developers deliver high-performance inference across cloud, on-prem, edge, and embedded devices.

Automatic Mixed Precision

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Deep Neural Network training has traditionally relied on IEEE single-precision format, however with mixed precision, you can train with half precision while maintaining the network accuracy achieved with single precision. This technique of using both single- and half-precision representations is referred to as mixed precision technique.

NVIDIA ISAAC SDK

Robotics SDK | Learn More

Build and deploy commercial-grade, AI-powered robots. The NVIDIA Isaac SDK™ is a toolkit that includes building blocks and tools that accelerate robot developments that require the increased perception and navigation features enabled by AI.

NVIDIA Merlin

Recommendation system framework | Get Started

NVIDIA Merlin empowers data scientists, machine learning engineers, and researchers to build high-performing recommenders at scale. Merlin includes tools that democratize building deep learning recommenders by addressing common ETL, training, and inference challenges. Each stage of the Merlin pipeline is optimized to support hundreds of terabytes of data, all accessible through easy-to-use APIs. With Merlin, better predictions than traditional methods and increased click-through rates are within reach.

NVIDIA Morpheus

Cybersecurity app framework | Apply for early access

NVIDIA Morpheus is an open application framework that enables cybersecurity developers to create optimized AI pipelines for filtering, processing and classifying large volumes of real-time data. Developer kits in AWS, from Red Hat, or running on NVIDIA-certified servers support pre-trained AI models, allowing customers to continuously inspect network and server telemetry at scale. Bringing a new level of information security to data centers, Morpheus enables dynamic protection, real-time telemetry, and adaptive defenses for detecting and remediating cybersecurity threats.


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GTC Content

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AI in Supply Chain and Logistics

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E-Commerce and Recommendation Systems

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Retail Data Science

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Intelligent Stores

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Create Intelligent Places Using NVIDIA Vision AI Models & DeepStream SDK

This webinar will briefly introduce new features of DS5.0 and TAO Toolkit 2.0, and show an end-to-end demo using Peoplenet/DS/TAO Toolkit, for people counting/occupancy analytics, which can be used widely in retail stores or public spaces.


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Driving Agility in Retail with AI


AI is helping retailers not only keep employees and customers safe, but also improve business agility to increase e-commerce sales, improve contactless checkout in stores, reduce shrink, and accelerate distribution center automation. Learn how the most innovative retailers are using AI to deliver the greatest business value.


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Using Pre-Trained Models and TAO Toolkit 3.0 for Robotics

Learn how to train your own gesture recognition deep learning pipeline. We’ll start with a pre-trained detection model, repurpose it for hand detection, and use it together with the purpose-built gesture recognition model.


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Getting Started with DeepStream for Video Analytics on Jetson Nano

You’ll learn how to:


  • Set up your Jetson Nano and (optional) camera
  • Build end-to-end DeepStream pipelines to convert raw video input into insightful annotated video output
  • Configure multiple video streams simultaneously

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Fundamentals of Accelerated Data Science with RAPIDS

You’ll learn how to:


  • Use cuDF, Dask, and BlazingSQL to manipulate massive datasets directly on the GPU
  • Utilize a wide variety of GPU-accelerated machine learning algorithms including XGBoost, cuGRAPH, and several cuML algorithms to perform data analysis at massive scale
  • Perform multiple analysis tasks on several massive datasets in an effort to stave off a simulated epidemic outbreak affecting the entire UK population

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Building Intelligent Recommender Systems

You’ll learn how to:


  • Build a content-based recommender system using the open-source cuDF library and Apache Arrow
  • Optimize performance for both training and inference using large, sparse datasets
  • Deploy a recommender model as a high-performance web service

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AI Workflows for Intelligent Video Analytics with Deep Stream

You’ll learn how to:


  • Deploy DeepStream pipeline for parallel, multi-stream video processing and deliver applications with maximum throughput at scale
  • Configure the processing pipeline and create intuitive, graph-based applications.
  • Leverage multiple deep network models to process video streams and achieve more intelligent insights

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Interactively Visualizing a DriveTime Radius from Any Point in the US

Retailers who understand these factors have an advantage over their competitors and can thrive. In this blog post, we’ll explore how RAPIDS’ cuDF, cuGraph, cuSpatial, and Plotly Dash with NVIDIA GPUs can be used to solve these complex geospatial analytics problems interactively.



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Best Practices of Using AI to Develop an Accurate Forecasting Solution

Learn about the best practices of using AI and data science to improve forecasting in retail. This blog explains the Instacart Market Basket Analysis Kaggle competition, how to explore the data visually, train the model and run a forecasting predictio.



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Beginner’s Guide to GPU- Accelerated Event Stream Processing in Python

Learn about the various aspects of RAPIDS that allow its users solve ETL (Extract, Transform, Load) problems, build ML (Machine Learning) and DL (Deep Learning) models, explore expansive graphs, process geospatial, signal, and system log data, or use SQL language via BlazingSQL to process data.


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PROGRAMS FOR YOU

Developer Resources

The NVIDIA Developer Program provides the advanced tools and training needed to successfully build applications on all NVIDIA technology platforms. This includes access to hundreds of SDKs, a network of like-minded developers through our community forums, and more.

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Technical Training

NVIDIA Deep Learning Institute (DLI) offers hands-on training in AI, accelerated computing, and accelerated data science to solve real-world problems. Powered by GPUs in the cloud, training is available as self-paced, online courses or live, instructor-led workshops.

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Accelerate Your Startup

NVIDIA Inception—an acceleration platform for AI, data science, and HPC startups—supports over 7,000 startups worldwide with go-to-market support, expertise, and technology. Startups get access to training through the DLI, preferred pricing on hardware, and invitations to exclusive networking events.

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NVIDIA Retail News

How to Train a Defect Detection Model Using Synthetic Data with NVIDIA Omniverse Replicator

How to Train a Defect Detection Model Using Synthetic Data with NVIDIA Omniverse Replicator

Learn how to train an object detection model entirely with synthetic data, improve its accuracy with limited ground truth real data, and validate it against images that model has never seen before.

AutoDMP Optimizes Macro Placement for Chip Design with AI and GPUs

AutoDMP Optimizes Macro Placement for Chip Design with AI and GPUs

Macro placement has a tremendous impact on the landscape of the chip, directly affecting many design metrics, such as area and power consumption.

Physics-Informed Machine Learning Platform NVIDIA Modulus Is Now Open Source

Physics-Informed Machine Learning Platform NVIDIA Modulus Is Now Open Source

Physics-informed machine learning (physics-ML) is transforming high-performance computing (HPC) simulation workflows across disciplines, including computational fluid dynamics, structural mechanics, and computational chemistry. Because of its broad applications, physics-ML is well suited for modeling physical systems and deploying digital twins across industries ranging from manufacturing to climate sciences. NVIDIA Modulus is a state-of-the-art physics-ML platform that … Continued

Power Your AI Inference with New NVIDIA Triton and NVIDIA TensorRT Features

Power Your AI Inference with New NVIDIA Triton and NVIDIA TensorRT Features

NVIDIA Triton now offers native Python support with PyTriton, model analyzer support for model ensembles, and more.



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