Developer Resources For Retail
A hub of news, SDKs, technical resources, and more for developers working in retail.
App Frameworks and SDKs
NVIDIA Jarvis 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.
NVIDIA DeepStream SDK
NVIDIA’s DeepStream SDK delivers a complete streaming analytics toolkit for AI-based video and image understanding, as well as multi-sensor processing. DeepStream is an integral part of NVIDIA Metropolis, the platform for building end-to-end services and solutions for transforming pixels and sensor data to actionable insights.
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 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.
NVIDIA Transfer Learning Toolkit
The term “transfer learning” implies that you can extract learned features from an existing neural network and transfer these learned features by transferring weight from an existing neural network. Transfer Learning Toolkit enables you to build high performance IVA based applications such as retail analytics, logistics, smart cities, access control and more.
NVIDIA TensorRT™ is a platform for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications.
Triton Inference Server
The NVIDIA Triton inference server simplifies the deployment of AI models at scale in production. Triton Server is open-source inference server software that lets teams deploy trained AI models from many frameworks, including TensorFlow, TensorRT, PyTorch, and ONNX.
Automatic Mixed Precision
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 offers GPU-accelerated deep learning and HPC containers from NVIDIA GPU Cloud (NGC) that are optimized to deliver maximum performance on NVIDIA GPUs. The NGC container registry includes NVIDIA tuned, tested, certified, and maintained containers for the top deep learning software like TensorFlow, PyTorch, MXNet, TensorRT, and more. NGC also has third-party managed HPC application containers, and NVIDIA HPC visualization containers.
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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 TLT2.0, and show an end-to-end demo using Peoplenet/DS/TLT, for people counting/occupancy analytics, which can be used widely in retail stores or public spaces.
How AI is Reinventing the Retail Industry
Over 100 software partners provide AI capabilities spanning demand forecasting, shrinkage, customer behavior, store analytics, delivery and warehouse logistics. Learn how NVIDIA’s retail software platform can help your data science team quickly build AI applications and your IT team easily manage and update software on thousands of distributed edge devices.
Use NVIDIA’s DeepStream SDK to Deploy Streaming Analytics at Scale
Learn how to build modular and scalable frameworks for multi-stream and how to utilize pre-trained models to update your AI applications.
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
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
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
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
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.
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.
PROGRAMS FOR YOU
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.
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.
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.
NVIDIA Retail News
Bring AI to Market Fast with Pre-Trained Models and Transfer Learning Toolkit 3.0
Today, NVIDIA released several production-ready, pre-trained models and a developer preview of Transfer Learning Toolkit (TLT) 3.0, along with DeepStream SDK 5.1.
Facebook Rolls out a GPU-Accelerated AI Shopping Tool for Marketplace
Facebook recently announced a GPU-accelerated model designed for shopping. The model uses AI to automatically identify consumer goods from images to help make them shoppable. GrokNet, a universal computer vision system, can identify items in categories such as fashion, auto, and home decor.
GTC Digital: Domino’s Takes Delivery to a Whole New Level with NVIDIA DGX-1
In a new video posted to LinkedIn, previewing a talk at GTC Digital, the company reveals the magic behind their AI platform, an NVIDIA DGX-1 system. The AI system is helping Domino’s train their delivery prediction models, and the other proprietary AI models that the company says are part of their secret sauce
New NVIDIA DeepStream SDK 3.0 Removes Boundaries of Video Analytics
NVIDIA today announced the software release of DeepStream SDK 3.0 for Tesla GPUs. Developers can now focus on building core deep learning networks rather than designing end-to-end applications from scratch given its modular framework and hardware-accelerated building blocks.
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