Developer Resources For Retail

NVIDIA Jarvis

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.

RAPIDS

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.

TensorRT

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.

GPU-Optimized Containers

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.

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

Supercomputing Platform for the Edge

The edge platform provides a high-performance, cloud-native platform that lets organizations harness rapidly streaming data from factory floors, manufacturing inspection lines and city streets to securely deliver next-generation AI, IoT and 5G-based services at scale, with low latency.

Speedup End-to-End Vision AI Using Transfer Learning Toolkit 2.0 & DeepStream SDK 5.0

NVIDIA announced general availability of production quality pre-trained models, Transfer Learning Toolkit 2.0 and DeepStream SDK 5.0 for building real-time high performance vision AI applications.