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In the folder Dockerfile, one can prepare the develop env by following the readme.
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In the folder simple_cuda_ops, three different ways are presented to wrap cuda ops as python functions. All the interface data types are tensors defined in pytorch.
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Besides the cuda ops, if the differentiable pytorch ops are further needed, the folder torch_ops_template gives an extendable template.
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custom_linear_ops gives an runnable example of a linear ops with an extra function to check the top-k gradients of the network parameter.
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