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| author | Peter Goldsborough <peter@goldsborough.me> | 2018-06-21 06:48:04 -0700 |
|---|---|---|
| committer | Edward Z. Yang <ezyang@mit.edu> | 2018-06-21 09:48:04 -0400 |
| commit | 98a7d84a5ab300040ad95437fe9caccf12c13a8c (patch) | |
| tree | 92857472eea349d2395bc33810f5a88ce684720f /README.md | |
| parent | c0dfe2370340a215d3bdf441b42c1a2d275df6d1 (diff) | |
| download | pytorch-98a7d84a5ab300040ad95437fe9caccf12c13a8c.tar.gz pytorch-98a7d84a5ab300040ad95437fe9caccf12c13a8c.tar.bz2 pytorch-98a7d84a5ab300040ad95437fe9caccf12c13a8c.zip | |
Link to C++ extensions in README.md (#8737)
Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 5 |
1 files changed, 2 insertions, 3 deletions
@@ -141,9 +141,8 @@ and with minimal abstractions. You can write new neural network layers in Python using the torch API [or your favorite NumPy-based libraries such as SciPy](http://pytorch.org/tutorials/advanced/numpy_extensions_tutorial.html). -If you want to write your layers in C/C++, we provide an extension API based on -[cffi](http://cffi.readthedocs.io/en/latest/) that is efficient and with minimal boilerplate. -There is no wrapper code that needs to be written. You can see [a tutorial here](http://pytorch.org/tutorials/advanced/c_extension.html) and [an example here](https://github.com/pytorch/extension-ffi). +If you want to write your layers in C/C++, we provide a convenient extension API that is efficient and with minimal boilerplate. +There is no wrapper code that needs to be written. You can see [a tutorial here](http://pytorch.org/tutorials/advanced/cpp_extension.html) and [an example here](https://github.com/pytorch/extension-cpp). ## Installation |