Torchscript

TorchScript is a tool in the PyTorch ecosystem that enables serialising PyTorch models into a format that can be run independently of Python.

This is particularly useful in production environments where running a Python process might have performance drawbacks or isn't feasible.

TorchScript allows the use of PyTorch for training models with its flexible and friendly Pythonic interface, while also offering a path to use the trained model in an optimized, potentially multi-threaded, lower-level language runtime.

TorchScript — PyTorch 2.3 documentation

Examples of how you could use TorchScript in training

Deploying AI models in a C++ environment

If you have an AI model trained in PyTorch using Python, you can use TorchScript to serialise your model and then load and run it in a C++ environment without needing Python at all.

This is extremely useful in production scenarios where you might want to eliminate Python dependencies for performance reasons or integrate with existing C++ codebases.

Optimising for mobile and embedded devices

TorchScript provides an efficient way to optimise your models for mobile and embedded devices that might not have Python interpreters.

For instance, you can train a PyTorch model on a powerful server with a lot of computational resources and then convert the model to TorchScript to deploy it on a mobile device for on-device inference.

Multi-threading and GPU Acceleration

TorchScript models can be executed in parallel, taking advantage of multi-core CPUs and GPUs.

This is useful for tasks such as image processing, where you might want to process multiple images at the same time.

If you've trained a PyTorch model for image classification, for example, you can use TorchScript to serialise your model and then use it in a multi-threaded application to classify multiple images in parallel.

Remember, TorchScript models are just like regular PyTorch models, and can be trained, fine-tuned, or used for inference. The primary difference is that TorchScript models can run independently from a Python runtime, making them more versatile in different production scenarios.