Deep Learning SDK Documentation - Last updated May 17, 2018 - Send Feedback -

NVIDIA Deep Learning SDK


Introduction
Deep learning algorithms use large amounts of data and the computational power of the GPU to learn information directly from data such as images, signals, and text. NVIDIA DIGITS offers an interactive workflow-based solution for image classification. Deep learning frameworks offer more flexibility with designing and training custom deep neural networks and provide interfaces to common programming language. The NVIDIA Deep Learning SDK offers powerful tools and libraries for the development of deep learning frameworks such as NVCaffe, Microsoft Cognitive Toolkit, TensorFlow, Theano, Torch and many more.

Training


Training with Mixed Precision
The Training with Mixed Precision User Guide introduces NVIDIA's latest architecture called Volta. This guide summarizes the ways that a framework can be fine-tuned to gain additional speedups by leveraging the Volta architectural features.
cuDNN Release Notes
This document describes the key features, software enhancements and improvements, and known issues for cuDNN v7.1.4.
cuDNN SLA
This document is the Software License Agreement (SLA) for NVIDIA cuDNN. The following contains specific license terms and conditions for NVIDIA cuDNN. By accepting this agreement, you agree to comply with all the terms and conditions applicable to the specific product(s) included herein.
cuDNN Installation Guide
This guide provides step-by-step instructions on how to install and check for correct operation of NVIDIA cuDNN v7.1.4 on Linux, Mac OS X, and Microsoft Windows systems.
cuDNN Developer Guide
This NVIDIA CUDA Deep Neural Network (cuDNN) Developer Guide provides an overview about cuDNN and details about the types, enums, and routines within the cuDNN library API.
NCCL Release Notes
This document describes the key features, software enhancements and improvements, and known issues for NCCL 2.2.12.
NCCL SLA
This document is the Software License Agreement (SLA) for NVIDIA NCCL. The following contains specific license terms and conditions for NVIDIA NCCL. By accepting this agreement, you agree to comply with all the terms and conditions applicable to the specific product(s) included herein.
NCCL Installation Guide
This NVIDIA Collective Communication Library (NCCL) Installation Guide provides a step-by-step instructions for downloading and installing NCCL 2.2.12.
NCCL Developer Guide
This NVIDIA Collective Communication Library (NCCL) Developer Guide provides a detailed discussion of the NCCL programming model, creating collective communications and working with operations.
NCCL API
This is the API documentation for the NVIDIA Collective Communications Library (NCCL). It provides information on individual functions, classes and methods.
Additional Resources
The Additional Resources topic provides you with important related links that are outside of this product documentation.

Inference


TensorRT Release Notes
This document describes the key features, software enhancements and improvements, and known issues for TensorRT 4.0 Release Candidate (RC).
TensorRT SLA
This document is the Software License Agreement (SLA) for NVIDIA TensorRT. The following contains specific license terms and conditions for NVIDIA TensorRT. By accepting this agreement, you agree to comply with all the terms and conditions applicable to the specific product(s) included herein.
TensorRT Installation Guide
This TensorRT Installation Guide provides step-by-step instructions for installing TensorRT 4.0 Release Candidate (RC).
TensorRT Developer Guide
This TensorRT 4.0 Release Candidate (RC) Developer Guide provides a deeper understanding of TensorRT and provides examples that show you how to optimize a network definition by merging tensors and layers, transforming weights, choosing efficient intermediate data formats, and selecting from a large kernel catalog based on layer parameters and measured performance.
TensorRT API
This is the API documentation for the NVIDIA TensorRT library. The TensorRT API allows developers to import, calibrate, generate and deploy optimized networks. Networks can be imported directly from NVCaffe, or from other frameworks via the UFF format. They may also be created programmatically by instantiating individual layers and setting parameters and weights directly.
Additional Resources
The Additional Resources topic provides you with important related links that are outside of this product documentation.

Archives


cuDNN Archives
This Archives document provides access to previously released cuDNN documentation versions.
NCCL Archives
This Archives document provides access to previously released NCCL documentation versions.
TensorRT Archives
This Archives document provides access to previously released TensorRT documentation versions.