NVIDIA Documentation Hub

NVIDIA’s program that enables enterprises to confidently deploy hardware solutions that optimally run accelerated workloads—from desktop to data center to edge.

NVIDIA cuOpt™ is a GPU-accelerated solver that uses heuristics and metaheuristics to solve complex vehicle routing problem variants with a wide range of constraints.

The NVIDIA Data Loading Library (DALI) is a collection of highly optimized building blocks, and an execution engine, for accelerating the pre-processing of input data for deep learning applications. DALI provides both the performance and the flexibility for accelerating different data pipelines as a single library. This single library can then be easily integrated into different deep learning training and inference applications.

Deep Graph Library (DGL) is a framework-neutral, easy-to-use, and scalable Python library used for implementing and training Graph Neural Networks (GNN). Being framework-neutral, DGL is easily integrated into an existing PyTorch, TensorFlow, or an Apache MXNet workflow.

The NVIDIA Deep Learning GPU Training System (DIGITS) can be used to rapidly train highly accurate deep neural networks (DNNs) for image classification, segmentation, and object-detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real time with advanced visualizations, and selecting the best-performing model from the results browser for deployment.

NVIDIA NGC is the hub for GPU-optimized software for deep learning, machine learning, and HPC that provides containers, models, model scripts, and industry solutions so data scientists, developers and researchers can focus on building solutions and gathering insights faster.

The RAPIDS data science framework is a collection of libraries for running end-to-end data science pipelines completely on the GPU. The interaction is designed to have a familiar look and feel to working in Python, but utilizes optimized NVIDIA CUDA primitives and high-bandwidth GPU memory under the hood.

The RAPIDS Accelerator for Apache Spark leverages GPUs to accelerate processing by combining the power of the RAPIDS cuDF library and the scale of the Spark distributed computing framework. You can run your existing Apache Spark applications on GPUs with no code change by launching Spark with the RAPIDS Accelerator for Apache Spark plugin jar and enabling a single configuration setting.

GPU-accelerated enhancements to gradient boosting library XGBoost to provide fast and accurate ways to solve large-scale AI and data science problems.

NVIDIA’s program that enables enterprises to confidently deploy hardware solutions that optimally run accelerated workloads—from desktop to data center to edge.

The RAPIDS Accelerator for Apache Spark leverages GPUs to accelerate processing by combining the power of the RAPIDS cuDF library and the scale of the Spark distributed computing framework. You can run your existing Apache Spark applications on GPUs with no code change by launching Spark with the RAPIDS Accelerator for Apache Spark plugin jar and enabling a single configuration setting.

The RAPIDS data science framework is a collection of libraries for running end-to-end data science pipelines completely on the GPU. The interaction is designed to have a familiar look and feel to working in Python, but utilizes optimized NVIDIA CUDA primitives and high-bandwidth GPU memory under the hood.

The NVIDIA Deep Learning GPU Training System (DIGITS) can be used to rapidly train highly accurate deep neural networks (DNNs) for image classification, segmentation, and object-detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real time with advanced visualizations, and selecting the best-performing model from the results browser for deployment.

GPU-accelerated enhancements to gradient boosting library XGBoost to provide fast and accurate ways to solve large-scale AI and data science problems.

NVIDIA NGC is the hub for GPU-optimized software for deep learning, machine learning, and HPC that provides containers, models, model scripts, and industry solutions so data scientists, developers and researchers can focus on building solutions and gathering insights faster.

Deep Graph Library (DGL) is a framework-neutral, easy-to-use, and scalable Python library used for implementing and training Graph Neural Networks (GNN). Being framework-neutral, DGL is easily integrated into an existing PyTorch, TensorFlow, or an Apache MXNet workflow.

NVIDIA cuOpt™ is a GPU-accelerated solver that uses heuristics and metaheuristics to solve complex vehicle routing problem variants with a wide range of constraints.

The NVIDIA Data Loading Library (DALI) is a collection of highly optimized building blocks, and an execution engine, for accelerating the pre-processing of input data for deep learning applications. DALI provides both the performance and the flexibility for accelerating different data pipelines as a single library. This single library can then be easily integrated into different deep learning training and inference applications.