Serving PyTorch Models in C++

• This repository contains various examples to perform inference using PyTorch C++ API.
• Run git clone https://github.com/Wizaron/pytorch-cpp-inference in order to clone this repository.

Environment

1. Dockerfile can be found at docker directory. In order to build docker image, you should go to docker directory and run docker build -t <docker-image-name> ..
2. After creation of the docker image, you should create a docker container via docker run -v <directory-that-this-repository-resides>:<target-directory-in-docker-container> -p 8181:8181 -it <docker-image-name> (We will use 8181 to serve our PyTorch C++ model).
3. Inside docker container, go to the directory that this repository resides.
4. Download libtorch from PyTorch Website or using wget https://download.pytorch.org/libtorch/cpu/libtorch-shared-with-deps-latest.zip.
5. Unzip it via unzip libtorch-shared-with-deps-latest.zip. This will create libtorch directory that contains torch shared libraries and headers.

Code Structure

• models directory stores PyTorch models.
• libtorch directory stores C++ torch headers and shared libraries to link the model against PyTorch.
• utils directory stores various utility function to perform inference in C++.
• inference-cpp directory stores codes to perform inference.

Exporting PyTorch ScriptModule

• In order to export torch.jit.ScriptModule of ResNet18 to perform C++ inference, go to models/resnet directory and run python resnet.py. It will download pretrained ResNet18 model on ImageNet and create models/resnet_model.pth which we will use in C++ inference.

Serving the C++ Model

• We can either serve the model as a single executable or as a web server.

Single Executable

• In order to build a single executable for inference:
  1. Go to inference-cpp/cnn-classification directory.
  2. Run ./build.sh in order to build executable, named as predict.
  3. Run the executable via ./predict <path-to-image> <path-to-exported-script-module> <path-to-labels-file>.
  4. Example: ./predict image.jpeg ../../models/resnet/resnet_model.pth ../../models/resnet/labels.txt

Web Server

• In order to build a web server for production:
  1. Go to inference-cpp/cnn-classification/server directory.
  2. Run ./build.sh in order to build web server, named as predict.
  3. Run the binary via ./predict <path-to-exported-script-module> <path-to-labels-file> (It will serve the model on http://localhost:8181/predict).
  4. In order to make a request, open a new tab and run python test_api.py (It will make a request to localhost:8181/predict).

Acknowledgement

1. pytorch
2. crow
3. tensorflow_cpp_object_detection_web_server