grady1006 / pytorch-cpp

C++ Implementation of PyTorch Tutorial for Everyone

C++ Implementation of PyTorch Tutorials for Deep Learning Researchers

OS (Compiler)\libtorch	1.5	nightly
macOS (clang 9.1)
macOS (clang 10.0)
macOS (clang 11.0)
Linux (gcc 5)
Linux (gcc 6)
Linux (gcc 7)
Linux (gcc 8)
Windows (msvc 2017)

This repository provides tutorial code in C++ for deep learning researchers to learn PyTorch.
Python Tutorial: https://github.com/yunjey/pytorch-tutorial

Getting Started

Requirements

1. C++
2. CMake
3. LibTorch v1.5.0
4. Conda

For Interactive Tutorials

Note: Interactive Tutorials are currently running on LibTorch Nightly Version.
So there are some tutorials which can break when working with nightly version.

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

Clone, build and run tutorials

In Google Colab

Run pytorch-cpp notebook in Google Colab

On Local Machine

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

Generate build system

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:

Option	Default	Description
-D CUDA_V=(9.2|10.1|10.2|none)	none	Download libtorch for a CUDA version (none = download CPU version).
-D DOWNLOAD_DATASETS=(OFF|ON)	ON	Download all datasets used in the tutorials.
-D CMAKE_PREFIX_PATH=path/to/libtorch/share/cmake/Torch	<empty>	Skip the downloading of libtorch and use your own local version instead.
-D CMAKE_BUILD_TYPE=(Release|Debug)	<empty> (Release when downloading libtorch on Windows)	Set the build type (Release = compile with optimizations)

Build

cmake --build build

Note for Windows users:
The CMake script downloads the Release version of libtorch, so --config Release has to be appended to the build command.

General Note:
By default all tutorials will be built. If you only want to build one specific tutorial, specify the target parameter for the build command. For example to only build the language model tutorial, append --target language-model (target name = tutorial foldername with all underscores replaced with hyphens).

Run Tutorials

1. (IMPORTANT!) First change into the tutorial's directory within build/tutorials. For example, assuming you are in the pytorch-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}/{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 (or pytorch-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

Table of Contents

1. Basics

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

2. Intermediate

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

3. Advanced

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

4. Interactive Tutorials

• Tensor Slicing

License

This repository is licensed under MIT as given in LICENSE.