Tensor Comprehensions

Tensor Comprehensions (TC) accelerates development by automatically generating efficient GPU code from high-level mathematical operations. TC is a C++ library and mathematical language that helps bridge the gap between researchers, who communicate in terms of mathematical operations, and engineers who are focused on running large-scale models.

Boost productivity

Tensor Comprehensions (TC) is based on generalized Einstein notation for computing on multi-dimensional arrays. It greatly simplifies the development of new operations by providing a concise and powerful syntax which can be automatically and efficiently translated into high-performance computation CUDA kernels.

Tensor Comprehensions provides a lightweight and seamless integration with PyTorch.

Tensor Comprehensions for 2D average pooling

Get Started


  • Set up orinstall Anaconda if you don't already have it.

    $ export PATH=$HOME/anaconda3/bin:$PATH

  • 2

  • Install Tensor Comprehensions.

    $ conda install -y -c pytorch -c tensorcomp tensor_comprehensions

  • 3

  • Review the tutorial and documentation to familiarize yourself with how to use Tensor Comprehensions.

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    PyTorch is an open source deep learning framework built to be flexible and modular for research, with the stability and support needed for production deployment. It enables fast, flexible experimentation through a tape-based autograd system designed for immediate and python-like execution.

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