Comments (3)
I recommend using the CMakeLists.txt
because it will automatically do the right thing when PyTorch libraries get updated. If you want to see the full command that CMake ends up running, you can use make VERBOSE=1
when compiling, then you'll see the full compiler invocation including the linker flags and such, and you can copy paste those into your terminal if you want to.
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Thanks @goldsborough this was very helpful. As I already mentioned I am learning the internals of pytorch for better understanding and also it will be helpful in my future research projects. I get really stuck on some portions is there a way to reach you (if possible) to ask these questions. Since I don't feel raising an issue everytime is such a good idea, I know about pytorch forums but I think questions get lost there due to the volume of the questions.
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You can reach us on the PyTorch slack. See https://pytorch.org/resources
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Related Issues (20)
- www.facebook.com/rozi.khatun.961
- This repo can not compile using Pytorch 1.6.0 HOT 1
- This repo can not compile using Pytorch 1.6.0 HOT 1
- A question about backward propagation speed in cpp extension in tutorial
- Error when building cpp extension HOT 1
- AttributeError: module 'depthwise' has no attribute 'forward'
- cuda does not install HOT 17
- How does the layer of C++ extensions translate to TorchScript or onnx? HOT 1
- Cannot Unpack, Too Many Items
- How to debug in cuda-pytorch env?
- Writing a custom C++ extension with variants for both CPU and GPU? HOT 2
- Custom CUDA operator only work well on cuda:0 HOT 3
- How can I access data in cuda kernel like in pytorch?
- error LNK2001 undefined reference to `__cudaRegisterLinkedBinary
- Relation between at::Half and __half
- Deprecation warning
- Scikit-build-core / scikit-build support for binding
- `TORCH_LIBRARY` and `m.def` Not Working as Documented
- [feature request] Instruction on how to setup compile-env for Windows
- JIT-compiling the extension results in non-functional Python module.
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