NVIDIA Deep Learning SDK Documentation - Last updated May 18, 2020 - Send Feedback -

NVIDIA Deep Learning SDK

Introduction To Deep Learning SDK
The two major operations from which deep learning produces insight are training and inference. While similar, there are significant differences. Training, feeds examples of objects to be detected/recognized like animals, traffic signs, etc., allowing it to make predictions, as to what these objects are. The training process reinforces correct predictions and corrects the wrong ones. Once trained, a production neural network can achieve upwards of 90-98% correct results. "Inference", is the deployment of a trained network to evaluate new objects, and make predictions with similar predictive accuracy. Inference comes after training, therefore, you must obtain a trained neural network before you can perform inference.

Training Library

cuDNN Support Matrix
These support matrices provide a look into the supported versions of the OS, CUDA, the CUDA driver, and the NVIDIA hardware for cuDNN 7.6.5 and earlier releases.
cuDNN Release Notes
NVIDIA CUDA Deep Neural Network (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. It provides highly tuned implementations of routines arising frequently in DNN applications. These release notes describe the key features, software enhancements and improvements, and known issues for cuDNN 7.6.5 and earlier releases.
cuDNN Installation Guide
This cuDNN 7.6.5 Installation Guide provides step-by-step instructions on how to install and check for correct operation of cuDNN on Linux, Mac OS X, and Microsoft Windows systems.
cuDNN Developer Guide
This cuDNN 7.6.5 Developer Guide provides an overview of cuDNN features such as customizable data layouts, supporting flexible dimension ordering, striding, and subregions for the 4D tensors used as inputs and outputs to all of its routines. This flexibility allows easy integration into any neural network implementation.
Best Practices For cuDNN
This Best Practices guide covers various 3D convolution and deconvolution guidelines. This document also provides guidelines for setting the cuDNN library parameters to enhance the performance for 3D convolutions in cuDNN 7.6.5. Specifically, these guidelines are focused on settings such as filter sizes, padding and dilation settings.
This is the API documentation for the cuDNN library. This API Guide consists of the cuDNN datatype reference chapter which describes the types of enums and the cuDNN API reference chapter which describes all routines in the cuDNN library API. The cuDNN API is a context-based API that allows for easy multithreading and (optional) interoperability with CUDA streams.
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 Archives
This Archives document provides access to previously released cuDNN documentation versions.