MNIST Classifier with CUDA and C++

This project is a MNIST classifier using CUDA and C++ to code an MLP from scratch. In its tests it uses the torch C++ API to assure correct implementation. It achieves ~97% on MNIST dataset.

Attention: This not yet in a clean version, but it is working. It is not optimized at all. Tested with NVIDIA RTX 4090.

Right now, running torch_speedtest vs cuda_speedtest the runtime is 16.01 sec vs 8.561 sec on my hardware. https://twitter.com/janusch_patas/status/1686016066537840641?s=20 The naive CUDA implementation is already twice as fast.

Dependencies

• CMake (version >= 3.22)
• CUDA Toolkit (version >= 12.0)
• PyTorch (libtorch)
• Google Test (release-1.10.0)
• Cutlass (version >= 3.1)

It might also work with a lower version of CUDA, but that is the only one I have tested. CMake version might be also considerable lower. Just test it.

Installation

Open a terminal and navigate to the directory where you want the project to clone.

git clone https://github.com/MrNeRF/MNIST_CUDA
cd MNIST_CUDA

libtorch

Download the libtorch library using the following command:

wget https://download.pytorch.org/libtorch/test/cu118/libtorch-cxx11-abi-shared-with-deps-latest.zip  

This will download a zip file named libtorch-shared-with-deps-latest.zip. To extract this zip file, use the command:

unzip libtorch-cxx11-abi-shared-with-deps-latest.zip -d external/
rm libtorch-cxx11-abi-shared-with-deps-latest.zip

This will create a folder named libtorch in the external directory of your project.

MNIST Data

The MNIST data can be downloaded using the following command:

cd data
wget http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
wget http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
wget http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
wget http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz

These commands will download four gzip files. To extract these gzip files, use the command:

gunzip train-images-idx3-ubyte.gz
gunzip train-labels-idx1-ubyte.gz
gunzip t10k-images-idx3-ubyte.gz
gunzip t10k-labels-idx1-ubyte.gz
cd ..

Building the Project

To build the project, follow these steps:

1. Create a new directory named build and navigate into it:

   mkdir build && cd build

2. Run the CMake configuration:

   cmake -DCMAKE_BUILD_TYPE=Release ..

   If you have problems to configure the build, you might look up the graphics card architecture you are using. Then replace CUDA_ARCHITECTURE 89 with the number of your architecture.

3. Finally, compile the project:

   make -j$(nproc)
   cd ..

This will create an executable named mnist_cuda in the build directory.

Run it:

./build/mnist_cuda data

Running the Tests

After building the project, you can run the tests with the following command:

./build/cuda_kernel_tests