Deep Learning Documentation - Last updated September 5, 2017 - 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 Caffe, CNTK, TensorFlow, Theano, and Torch.

Training


Training with Mixed-Precision User Guide
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 v7.0.3.
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 v7.0.3 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 2.0.5.
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 2.0.5.
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. 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.
TensorRT User Guide
This TensorRT User 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. 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.