TorchSharp
.NET wrapper
A .NET wrapper around the PyTorch library, providing access to its features and functionality.
.NET bindings for the Pytorch engine
17 stars
7 watching
1 forks
Language: C#
last commit: almost 7 years agoRelated projects:
| Repository | Description | Stars |
|---|---|---|
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 amdegroot/pytorch-containers | A collection of PyTorch implementations of Torch Table Layers to simplify the transition from old Torch architectures. | 89 |
 pytorch-labs/torchfix | A tool to analyze and fix issues in PyTorch-related Python code, with automated fixes available. | 111 |
 hughperkins/pytorch | Provides Python wrappers for PyTorch and Lua, enabling developers to use PyTorch's deep learning capabilities from both languages. | 432 |
 josipd/torch-two-sample | A PyTorch library for implementing various differentiable two-sample tests | 237 |
 pistony/torch-toolbox | A collection of reusable utility functions and classes to simplify PyTorch development | 416 |
 nearai/torchfold | A PyTorch module for dynamic batching and optimized computation on deep neural networks | 221 |
 metaopt/torchopt | An efficient library for differentiable optimization built on top of PyTorch. | 554 |
 locuslab/pytorch_fft | Provides an efficient wrapper around CUDA FFTs for PyTorch transformations | 315 |
 pytorch/extension-cpp | Enables the creation of custom C++ extensions with CUDA support in PyTorch | 1,031 |
 kaiyangzhou/dassl.pytorch | A PyTorch toolbox for supporting research and development of domain adaptation, generalization, and semi-supervised learning methods in computer vision. | 1,236 |
 clcarwin/convert_torch_to_pytorch | Converts PyTorch models to their Torch equivalents. | 541 |
 vermeille/torchelie | A collection of utility functions and tools for building deep learning models with PyTorch | 111 |
 mattstauffer/torch | A project providing examples and instructions for using Laravel's Illuminate components in standalone, non-Laravel applications | 1,854 |
 pytorchbearer/torchbearer | A PyTorch model fitting library designed to simplify the process of training deep learning models. | 636 |