TorchSharp.PyBridge 1.4.3

TorchSharp.PyBridge

👁 NuGet

TorchSharp.PyBridge is an extension library for TorchSharp, providing seamless interoperability between .NET and Python for model serialization. It simplifies the process of saving and loading PyTorch models in a .NET environment, enabling developers to easily develop models in both .NET and Python and transfer models easily.

Features

• module.load_py(...), optim.load_py(...): Extension method for modules and optimizers for easily loading PyTorch models saved in the standard Python format (using torch.save) directly into TorchSharp.

  This only works for when the state_dict was saved and not the whole model, see example below.

• module.save_py(...), optim.save_py(...): Extension method for modules and optimizers for easily saving TorchSharp models in a format that can be directly loaded in PyTorch (using torch.load), offering cross-platform model compatibility.

• module.load_safetensors(...), module.save_safetensors(...): Extension methods for modules for easily saving and loading model weights using the safetensors format.

• module.load_checkpoint(...): Extension method for loading in a checkpoint (both safetensors and regular pytorch, including sharded models) from a directory saved using HuggingFace's PreTrainedModel.save_pretrained() method.

Getting Started

Installation

TorchSharp.PyBridge is available on NuGet. You can install it using the following command:

.NET CLI

dotnet add package TorchSharp.PyBridge

NuGet Package Manager

Install-Package TorchSharp.PyBridge

Prerequisites

• .NET SDK
• TorchSharp library

Usage

Loading a PyTorch Model in .NET

Saving the model in Python:

import torch 

model = ...
torch.save(model.state_dict(), 'path_to_your_model.pth')

Loading it in C#:

using TorchSharp.PyBridge;

var model = ...;
model.load_py("path_to_your_model.pth");

Saving a TorchSharp Model for PyTorch

To save a model in a format compatible with PyTorch:

using TorchSharp.PyBridge;

var model = ...;
model.save_py("path_to_save_model.pth");

And loading it in in Python:

import torch

model = ...
model.load_state_dict(torch.load('path_to_save_model.pth'))

Contributing

Contributions to TorchSharp.PyBridge are welcome.

Acknowledgments

This project makes use of the pickle library, a Java and .NET implementation of Python's pickle serialization protocol, developed by Irmen de Jong. The pickle library plays a vital role in enabling the serialization features within TorchSharp.PyBridge. We extend our thanks to the developer for their significant contributions to the open-source community. For more details about the pickle library, please visit their GitHub repository.

Support and Contact

For support, questions, or feedback, please open an issue in the GitHub repository.