You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I have searched all issues/PRs to ensure it has not already been reported or fixed.
Criteria
Non-GUI tool
Reasonably well-known and widely used (e.g. if it's a GitHub project, it should have at least 500 stars and/or 150 forks)
English interface (or at least English documentation)
Latest stable version
Full version (i.e. not a trial version)
Fairly standard install (e.g. uses a version-specific download URL, no elaborate pre/post install scripts)
Name
CUDNN
Description
NVIDIA cuDNN The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, attention, matmul, pooling, and normalization.
NVIDIA cuDNN
The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, attention, matmul, pooling, and normalization.
The text was updated successfully, but these errors were encountered:
Prerequisites
Criteria
Name
CUDNN
Description
NVIDIA cuDNN The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, attention, matmul, pooling, and normalization.
Homepage
https://developer.nvidia.com/cudnn
Download Link(s)
https://developer.download.nvidia.com/compute/cudnn/9.5.1/local_installers/cudnn_9.5.1_windows.exe
Some Indication of Popularity/Repute
NVIDIA cuDNN
The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, attention, matmul, pooling, and normalization.
The text was updated successfully, but these errors were encountered: