NVIDIA CUDA Deep Neural Network (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN is built on top of the CUDA framework which is how you use NVIDIA GPUs for general purpose computing tasks. High performance GPU acceleration is helpful for machine learning tasks because it it allows computers to speed up processes, saving you time.

You can think of this as NVIDIA's framework for deep learning primitives – like pooling and convolution – which act as the lego blocks you need to build a machine learning framework. For example, Pytorch and Tensorflow call out to these primitives to access the GPUs and build the abstraction layers on top of cuDNN.

Video explanation of CuDNN

Download cuDNN for Free

In order to download CuDNN, you have to register to become a member of the NVIDIA Developer Program (which is free).

What is the difference between cuDNN and CUDA?

The cuDNN library is a library optimized for CUDA containing GPU implementations. Think of cuDNN as a library for Deep Learning using CUDA and CUDA as a way to talk to the GPU.

Tips for using cuDNN

When using cuDNN with machine learning models, make sure to pay close attention to the specific versions of cuDNN, CUDA, or any libraries or versions of python required. Failing to use the proper version could cause errors in your project. One way to avoid something like this is using a tool like Roboflow Deploy which helps manage the versioning for you and gives you an API or Docker container to deploy your model. We also have a breakdown on accessing your machines GPU within a Docker container.