C++ Implementation of PyTorch Tutorials for Everyone
OS (Compiler)\LibTorch
1.10.1
macOS (clang 11.0, 12.0)
Linux (gcc 8, 9, 10, 11)
Windows (msvc 2017, 2019)
Table of Contents
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
Note: Interactive Tutorials are currently running on LibTorch Nightly Version.
So there are some tutorials which can break when working with nightly version.
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=(10.2|11.1|11.3|none)
none
Download LibTorch for a CUDA version (none = download CPU version).
-D LIBTORCH_DOWNLOAD_BUILD_TYPE=(Release|Debug)
Release
Determines which libtorch build type version to download (only relevant on Windows).
-D DOWNLOAD_DATASETS=(OFF|ON)
ON
Download required datasets during build (only if they do not already exist in pytorch-cpp/data).
-D CREATE_SCRIPTMODULES=(OFF|ON)
OFF
Create all required scriptmodule files for prelearned models / weights during build. Requires installed python3 with pytorch and torchvision.
Automatically download LibTorch for CUDA 11.3 (Release version) and all necessary datasets.
Do not create scriptmodule files.
Command
cmake -B build \
-A x64 \
-D CUDA_V=11.3
Build
Note for Windows (Visual Studio) users:
The CMake script downloads the Release version of LibTorch, so --config Release has to be appended to the build command.
How dataset download and scriptmodule creation work:
If DOWNLOAD_DATASETS is ON, the datasets required by the tutorials you choose to build will be downloaded to pytorch-cpp/data (if they do not already exist there).
If CREATE_SCRIPTMODULES is ON, the scriptmodule files for the prelearned models / weights required by the tutorials you choose to build will be created in the model folder of the respective tutorial's source folder (if they do not already exist).
All tutorials
To build all tutorials use
cmake --build build
All tutorials in a category
You can choose to only build tutorials in one of the categories basics, intermediate, advanced or popular. For example, if you are only interested in the basics tutorials:
(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|popular/blitz}/{tutorial_name}
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
Using Docker
Find the latest and previous version images on Docker Hub.
You can build and run the tutorials (on CPU) in a Docker container using the provided Dockerfile and docker-compose.yml files:
From the root directory of the cloned repo build the image:
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.
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 starts bash in interactive mode in the build/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.
License
This repository is licensed under MIT as given in LICENSE.
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