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C++ Implementation of PyTorch Tutorials for Everyone

OS (Compiler)\LibTorch	2.6.0
macOS (clang 15, 16)
Linux (gcc 13, 14)
Windows (msvc 2022, 2025)

This repository provides tutorial code in C++ for deep learning researchers to learn PyTorch(i.e. Section 1 to 3)
Python Tutorial:https://github.com/yunjey/pytorch-tutorial

• PyTorch Basics
• Linear Regression
• Logistic Regression
• Feedforward Neural Network

• Convolutional Neural Network
• Deep Residual Network
• Recurrent Neural Network
• Bidirectional Recurrent Neural Network
• Language Model (RNN-LM)

• Generative Adversarial Networks
• Variational Auto-Encoder
• Neural Style Transfer
• Image Captioning (CNN-AttentionRNN)

• Tensor Slicing

• Deep Learning with PyTorch: A 60 Minute Blitz

1. C++-17 compatible compiler
2. CMake (minimum version 3.28.6)
3. LibTorch version >= 1.12.0 and <= 2.6.0
4. Conda

Note: Interactive Tutorials are currently running onLibTorch Nightly Version.
So there are some tutorials which can break when working withnightly version.

conda create --name pytorch-cppconda activate pytorch-cppconda install xeus-cling notebook -c conda-forge

git clone https://github.com/prabhuomkar/pytorch-cpp.gitcd pytorch-cpp

cmake -B build#<options>

Note for Windows users:
Libtorch only supports 64bit Windows and an x64 generator needs to be specified. For Visual Studio this can be done by appending-A x64 to the above command.

Some useful options:

cmake -B build \-D CMAKE_BUILD_TYPE=Release \-D CMAKE_PREFIX_PATH=/path/to/libtorch/share/cmake/Torch \-D CREATE_SCRIPTMODULES=ON

cmake -B build \-A x64 \-D CUDA_V=11.8

Note for Windows (Visual Studio) users:
The CMake script downloads theRelease version of LibTorch, so--config Release has to be appended to the build command.

How dataset download and scriptmodule creation work:

• IfDOWNLOAD_DATASETS isON, the datasets required by the tutorials you choose to build will be downloaded topytorch-cpp/data (if they do not already exist there).
• IfCREATE_SCRIPTMODULES isON, the scriptmodule files for the prelearned models / weights required by the tutorials you choose to build will be created in themodel folder of the respective tutorial's source folder (if they do not already exist).

To build all tutorials use

cmake --build build

You can choose to only build tutorials in one of the categoriesbasics,intermediate,advanced orpopular. For example, if you are only interested in thebasics tutorials:

cmake --build build --target basics# In general: cmake --build build --target {category}

You can also choose to only build a single tutorial. For example to build the language model tutorial only:

cmake --build build --target language-model# In general: cmake --build build --target {tutorial-name}

Note:
The target argument is the tutorial's foldername with all underscores replaced by hyphens.

Tip for users of CMake version >= 3.15:
You can specify several targets separated by spaces, for example:

cmake --build build --target language-model image-captioning

1. (IMPORTANT!) First change into the tutorial's directory withinbuild/tutorials. For example, assuming you are in thepytorch-cpp directory and want to change to the pytorch basics tutorial folder:

   cd build/tutorials/basics/pytorch_basics# In general: cd build/tutorials/{basics|intermediate|advanced|popular/blitz}/{tutorial_name}

2. Run the executable. Note that the executable's name is the tutorial's foldername with all underscores replaced with hyphens (e.g. for tutorial folder:pytorch_basics -> executable name:pytorch-basics (orpytorch-basics.exe on Windows)). For example, to run the pytorch basics tutorial:
   
   Linux/Mac

   ./pytorch-basics# In general: ./{tutorial-name}

   Windows

   .\pytorch-basics.exe# In general: .\{tutorial-name}.exe

Find the latest and previous version images onDocker Hub.

You can build and run the tutorials (on CPU) in a Docker container using the providedDockerfile anddocker-compose.yml files:

1. From the root directory of the cloned repo build the image:

   docker-compose build --build-arg USER_ID=$(id -u) --build-arg GROUP_ID=$(id -g)

   Note:
   When you run the Docker container, the host repo directory is mounted as a volume in the Docker container in order to cache build and downloaded dependency files so that it is not necessary to rebuild or redownload everything when a container is restarted. In order to have correct file permissions it is necessary to provide your user and group ids as build arguments when building the image on Linux.

2. Now start the container and build the tutorials using:

   docker-compose run --rm pytorch-cpp

   This fetches all necessary dependencies and builds all tutorials.After the build is done, by default the container startsbash in interactive mode in thebuild/tutorials folder.
   As with the local build, you can choose to only build tutorials of a category (basics,intermediate,advanced,popular):

   docker-compose run --rm pytorch-cpp {category}

   In this case the container is started in the chosen category's base build directory.
   Alternatively, you can also directly run a tutorial by instead invoking the run command with a tutorial name as additional argument, for example:

   docker-compose run --rm pytorch-cpp pytorch-basics# In general: docker-compose run --rm pytorch-cpp {tutorial-name}

   This will - if necessary - build the pytorch-basics tutorial and then start the executable in a container.

This repository is licensed under MIT as given inLICENSE.