[WIP] NNCpp - Neural Networks in C++ with CUDA ops
Warning: This is my playground project to implement tensor ops, neural networks, gradient computations etc. Code structure and assumptions made may be restrictive. CUDA Kernel implementations are not optimized for performances and may not work in all cases.
- NNCpp library
- Tensor (CPU/CUDA) on Unified Memory
- Layers: activations, convolution on CUDA only
- Model: list of layers
- Criterion: CrossEntropy, MSE
- Example applications
- Tests
Requirements
- g++
- cuda 10.1, nvcc
- gtests:
apt-get install -y libgtest-dev
cd /usr/src/gtest && cmake . && make && mv libg* /usr/lib/
Installation
mkdir build && cd $_
cmake .. && make
NNCpp API
-
Tensor : "tensor.hpp"
- All tensors are 4D.
- Data allocated in "Unified Memory" for all devices: CPU/CUDA.
#include <iostream>
#include "tensor.hpp"
auto t_cpu = nncpp::Tensor::zeros(n, c, h, w, Device::CPU);
auto t_cuda = nncpp::Tensor::ones(n, c, h, w, Device::CUDA);
std::cout << t_cpu << std::endl;
std::cout << t_cuda << std::endl;
t_cpu.at(0, 0, 0, 0) = 1.0f;
t_cuda.at(0, 0, 0, 0) = 2.0f;- NN operations: "activations.hpp",
Run applications
Extend applications
Tests
cd build
cmake .. & make && ./test-nncpp
Profiling
cd build
nvprof ./test-nncpp