NVIDIA Documentation Hub

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  • Documentation Center
    02/06/23
    NVIDIA’s program that enables enterprises to confidently deploy hardware solutions that optimally run accelerated workloads—from desktop to data center to edge.
  • Product
    07/14/23
    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.
  • Product
    03/22/23
    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.
  • Documentation Center
    08/07/23
    Simplify your data science development environment and maximize productivity with NVIDIA® Data Science Workbench. Workbench puts key assets, like containers from the NVIDIA NGC catalog, automatic software updates, and dockerized GitHub content, within a click’s reach. Finally, users have fast and convenient access to a plethora of data science tools. Easily reproduce content, access containerized solutions, utilize multiple CLIs, get news and events and more.
  • Documentation Center
    01/23/23
    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.
  • Documentation Center
    02/03/23
    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.
  • Product
    04/26/23
    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.
  • Documentation Center
    01/23/23
    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.
  • Product
    09/14/23
    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.
  • Documentation Center
    01/23/23
    GPU-accelerated enhancements to gradient boosting library XGBoost to provide fast and accurate ways to solve large-scale AI and data science problems.
  • Documentation Center
    02/06/23
    NVIDIA’s program that enables enterprises to confidently deploy hardware solutions that optimally run accelerated workloads—from desktop to data center to edge.
  • Product
    09/14/23
    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.
  • Documentation Center
    08/07/23
    Simplify your data science development environment and maximize productivity with NVIDIA® Data Science Workbench. Workbench puts key assets, like containers from the NVIDIA NGC catalog, automatic software updates, and dockerized GitHub content, within a click’s reach. Finally, users have fast and convenient access to a plethora of data science tools. Easily reproduce content, access containerized solutions, utilize multiple CLIs, get news and events and more.
  • Documentation Center
    01/23/23
    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.
  • Documentation Center
    02/03/23
    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.
  • Documentation Center
    01/23/23
    GPU-accelerated enhancements to gradient boosting library XGBoost to provide fast and accurate ways to solve large-scale AI and data science problems.
  • Product
    04/26/23
    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.
  • Documentation Center
    01/23/23
    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.
  • Product
    07/14/23
    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.
  • Product
    03/22/23
    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.