In this tutorial, we will cover how to use torch::deploy to run eager PyTorch models in a production environment. torch::deploy is a C++ library developed by the PyTorch team to simplify the deployment of PyTorch models. It provides a flexible and efficient way to run PyTorch models in a production setting without the need for a Python runtime.
Step 1: Setting up torch::deploy
The first step is to set up torch::deploy in your development environment. You can download the library from the PyTorch website or build it from source using the instructions provided in the PyTorch GitHub repository.
Once you have torch::deploy installed, you can start using it in your C++ code by including the necessary headers and linking to the library. Make sure to set up your build system to include the appropriate flags and paths.
Step 2: Loading a PyTorch model
To run a PyTorch model with torch::deploy, you first need to load the model into memory. You can do this by creating a torch::deploy::Model object and loading the model from a file using the torch::deploy::Model::load() function. Here’s an example:
torch::deploy::Model model;
model.load("path/to/model.pth");Step 3: Preparing input data
Before running inference with your PyTorch model, you need to prepare input data in the correct format. Input data should be in a torch::Tensor object, which you can create from a C++ array or vector. Here’s an example of creating input data:
std::vector<float> input_data = {1.0, 2.0, 3.0};
torch::Tensor input_tensor = torch::from_blob(input_data.data(), {1, 3});Step 4: Running inference
Once you have loaded your PyTorch model and prepared input data, you can run inference using the torch::deploy::Model::forward() function. This function takes a torch::Tensor as input and returns a torch::Tensor with the output of the model. Here’s an example:
torch::Tensor output = model.forward({input_tensor});Step 5: Post-processing output
After running inference, you may need to post-process the output of the model before using it for further analysis or tasks. This could include converting the output tensor to a different data format or performing additional computations. Here’s an example of post-processing the output tensor:
std::vector<float> output_data(output.data_ptr<float>(), output.data_ptr<float>() + output.numel());Step 6: Deploying the model
Finally, you can deploy your PyTorch model in a production environment using torch::deploy. You can create a torch::deploy::Predictor object to serve the model and handle incoming requests. Here’s an example of deploying a model and running inference:
torch::deploy::Predictor predictor(model);
torch::deploy::IValue result = predictor.predict({input_tensor});These are the basic steps to run eager PyTorch models in a production environment using torch::deploy. Make sure to check the official documentation and examples provided by the PyTorch team for more advanced usage and features.
