MNIST Example with the PyTorch C++ Frontend

This folder contains an example of training a computer vision model to recognize digits in images from the MNIST dataset, using the PyTorch C++ frontend.

The entire training code is contained in mnist.cpp.

To build the code, run the following commands from your terminal:

$ cd mnist
$ mkdir build
$ cd build
$ cmake -DCMAKE_PREFIX_PATH=/path/to/libtorch ..
$ make

where /path/to/libtorch should be the path to the unzipped LibTorch distribution, which you can get from the PyTorch homepage.

Execute the compiled binary to train the model:

$ ./mnist
Train Epoch: 1 [59584/60000] Loss: 0.4232
Test set: Average loss: 0.1989 | Accuracy: 0.940
Train Epoch: 2 [59584/60000] Loss: 0.1926
Test set: Average loss: 0.1338 | Accuracy: 0.959
Train Epoch: 3 [59584/60000] Loss: 0.1390
Test set: Average loss: 0.0997 | Accuracy: 0.969
Train Epoch: 4 [59584/60000] Loss: 0.1239
Test set: Average loss: 0.0875 | Accuracy: 0.972
...

For running with CUDA Graphs add --use-train-graph and/or --use-test-graph for training and testing passes respectively.