

You wrote a CUDA layer, but it took a week to write, debug, optimize for speed. You wished you could do this in an hour.


You want to fuse multiple layers like Conv-ReLU-BatchNorm or Linear-ReLU-Linear-ReLU in your network for speed, but it was quite difficult to comprehend


Your research involves weird Tensor shapes that CuDNN and MKL are not optimized for. For example, you do convolutions of 13 x 24 with an input image of 143 x 55. You tried running it with CuDNN and it was slower than you wished.


Your code is slowed-down by transposing Tensors constantly to fit a particular memory layout. You wish it was easy to write custom code that operates efficiently on your input layout.


Tensor Comprehensions are seamless to use in PyTorch, interoperating with PyTorch Tensors and nn Variables.
Let us run through using TC with PyTorch.
1. Install the package
conda install -c pytorch -c tensorcomp tensor_comprehensions
