There are 8 repositories under cudnn topic.
Introduction to Deep Neural Networks with Keras and Tensorflow
1st place solution
Deep learning in Rust, with shape checked tensors and neural networks
A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
GPU-accelerated Deep Learning on Windows 10 native
Minimal runtime core of Caffe, Forward only, GPU support and Memory efficiency.
MLSpace: Hassle-free machine learning & deep learning development
The purpose of this tutorial is to learn how to install and prepare TensorFlow framework to train your own convolutional neural network object detection classifier for multiple objects, starting from scratch
VGG-19 deep learning model trained using ISCX 2012 IDS Dataset
TensorFlow wheels built for latest CUDA/CuDNN and enabled performance flags: SSE, AVX, FMA; XLA
OpenCV installation script with CUDA and cuDNN support
Tutorial for using Singularity containers
Script to remotely check GPU servers for free GPUs
Lightweight turnkey solution for AI
Ubuntu 18.04 How to install Nvidia driver + CUDA + CUDNN + build tensorflow for gpu step by step command line
Hooked CUDA-related dynamic libraries by using automated code generation tools.
PyTorch installation wheels for Jetson Nano
Archlinux PKGBUILDs for Data Science, Machine Learning, Deep Learning, NLP and Computer Vision
Allstate Kaggle Competition ML Capstone Project
🔥🔥🔥 A collection of some awesome public NVIDIA CUDA, cuBLAS, cuDNN, TensorRT, AMD ROCm and FPGA projects.
Tutorial on how to setup your system with a NVIDIA GPU and to install Deep Learning Frameworks like TensorFlow, Darknet for YOLO, Theano, and Keras; OpenCV; and NVIDIA drivers, CUDA, and cuDNN libraries on Ubuntu 16.04, 17.10 and 18.04.
A simple starting point for doing deep learning in Racket
darknet + ROS2 Humble + OpenCV4 + CUDA 11(cuDNN, Jetson Orin)
Tutorial to install NVIDIA Drivers, CUDA 11.4 and cuDNN for deep learning programming on Ubuntu 20.04.
Tests and benchmarks for cudnn (and in the future, other nvidia libraries)
Guide to installing Tensorflow with NVIDIA GPU and Deep learning enviroment - Nvidia Drivers/cuda/cuDNN/tensorflow-gpu/中文文档
Set up CI in DL/ cuda/ cudnn/ TensorRT/ onnx2trt/ onnxruntime/ onnxsim/ Pytorch/ Triton-Inference-Server/ Bazel/ Tesseract/ PaddleOCR/ NVIDIA-docker/ minIO/ Supervisord on AGX or PC from scratch.
Run YOLOv4 directly with OpenCV using the CUDA enabled DNN module.
This fork of the deep learning guide has been adapted to work with a variety of different inputs USB camera, GigEVision and RTP on the TX1 SoM. This is a a quick demonstrator and example for users of the Abaco Systems rugged Small Form Factor (SFF) TX1 boxed solutions. Please visit out website for more details.