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® Clara™ is an open, scalable computing platform that enables developers to build and deploy medical imaging applications into hybrid (embedded, on-premises, or cloud) computing environments to create intelligent instruments and automate healthcare workflows.

The NVIDIA-managed cuOpt service is a high-performance, on-demand routing optimization service fully managed by NVIDIA.

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.

Extract valuable insights from large quantities of video and sensor data with NVIDIA Metropolis for smart cities. Build with a powerful set of software tools, including the DeepStream SDK, NVIDIA TAO Toolkit, pretrained models from the NVIDIA NGC™ catalog, and NVIDIA® TensorRT™. Take advantage of containers to package these applications in a cloud-native format for flexible deployment that can be easily scaled out with the NVIDIA EGX™ platform.

The NVIDIA MONAI framework is the open-source foundation being created by Project MONAI. MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm.

NVIDIA Morpheus is an open AI application framework that provides cybersecurity developers with a highly optimized AI pipeline and pre-trained AI capabilities and allows them to instantaneously inspect all IP traffic across their data center fabric.

NVIDIA NeMo™ Framework is a development platform for building custom generative AI models. The framework supports custom models for language (LLMs), multimodal, computer vision (CV), automatic speech recognition (ASR), natural language processing (NLP), and text to speech (TTS).

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.

Reference documentation, examples, and tutorials for the NVIDIA OptiX ray-tracing engine, the Iray rendering system, and the Material Definition Language (MDL).

NVIDIA® Riva is an SDK for building multimodal conversational systems. Riva is used for building and deploying AI applications that fuse vision, speech, sensors, and services together to achieve conversational AI use cases that are specific to a domain of expertise. It offers a complete workflow to build, train, and deploy AI systems that can use visual cues such as gestures and gaze along with speech in context.

NVIDIA TAO eliminates the time-consuming process of building and fine-tuning DNNs from scratch for IVA applications.

Reference documentation, APIs, and samples for NVIDIA video technology SDKs on Windows and Linux platforms.

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 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.

NVIDIA NeMo™ Framework is a development platform for building custom generative AI models. The framework supports custom models for language (LLMs), multimodal, computer vision (CV), automatic speech recognition (ASR), natural language processing (NLP), and text to speech (TTS).

NVIDIA Morpheus is an open AI application framework that provides cybersecurity developers with a highly optimized AI pipeline and pre-trained AI capabilities and allows them to instantaneously inspect all IP traffic across their data center fabric.

NVIDIA® Riva is an SDK for building multimodal conversational systems. Riva is used for building and deploying AI applications that fuse vision, speech, sensors, and services together to achieve conversational AI use cases that are specific to a domain of expertise. It offers a complete workflow to build, train, and deploy AI systems that can use visual cues such as gestures and gaze along with speech in context.

NVIDIA® Clara™ is an open, scalable computing platform that enables developers to build and deploy medical imaging applications into hybrid (embedded, on-premises, or cloud) computing environments to create intelligent instruments and automate healthcare workflows.

NVIDIA TAO eliminates the time-consuming process of building and fine-tuning DNNs from scratch for IVA applications.

The NVIDIA MONAI framework is the open-source foundation being created by Project MONAI. MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm.

Extract valuable insights from large quantities of video and sensor data with NVIDIA Metropolis for smart cities. Build with a powerful set of software tools, including the DeepStream SDK, NVIDIA TAO Toolkit, pretrained models from the NVIDIA NGC™ catalog, and NVIDIA® TensorRT™. Take advantage of containers to package these applications in a cloud-native format for flexible deployment that can be easily scaled out with the NVIDIA EGX™ platform.

Reference documentation, examples, and tutorials for the NVIDIA OptiX ray-tracing engine, the Iray rendering system, and the Material Definition Language (MDL).

Reference documentation, APIs, and samples for NVIDIA video technology SDKs on Windows and Linux platforms.

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.

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-managed cuOpt service is a high-performance, on-demand routing optimization service fully managed by NVIDIA.

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.