Table of Contents

• Getting Started
• Clone, Build and Run Tutorials
• Using Docker
• Troubleshooting

Getting Started

This repository provides tutorial code in C++ for deep learning researchers to learn PyTorch. To get started, ensure you have the necessary requirements:

1. C++-17 compatible compiler
2. CMake (minimum version 3.19)
3. LibTorch version 2.3.0
4. Conda

For Interactive Tutorials

Note: Interactive Tutorials are currently running on LibTorch Nightly Version. Some tutorials may not function properly with this version.

Set up your interactive environment using:

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

Clone, Build and Run Tutorials

Follow these steps to clone, build, and run the tutorials:

On Local Machine

First, clone the repository:

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

Next, generate the build system:

bash
cmake -B build

Important Note for Windows Users:

LibTorch only supports 64-bit Windows. For Visual Studio, append -A x64 to the command above.

Run All Tutorials

bash
cmake --build build

Run a Specific Tutorial

The executable name is determined by the folder name of the tutorial, replacing all underscores with hyphens. For example:

Change to the tutorials directory and run:

bash
cd build/tutorials/basics/pytorch_basics
./pytorch-basics

Using Docker

If you prefer using Docker, follow these steps:

Build the Docker image:

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

Then run the container and build the tutorials:

bash
docker-compose run --rm pytorch-cpp

Troubleshooting

If you encounter issues, consider the following troubleshooting steps:

• Make sure all dependencies are correctly installed.
• Double-check your CMake version and compatibility.
• If using Docker, verify that the container has internet access to download dependencies.
• For interactive tutorials, ensure you’re using the correct version of LibTorch.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.