Shizhi Tang (roastduck)

license

Apache-2.0(OSI)

source

public

core features withheld

no

Apache-2.0 license body confirmed. The repository is public and unarchived and builds the whole product, and the README describes no paid tier, enterprise edition or license-gated build beside it, so source is public and the core ungated.

• https://api.github.com/repos/roastduck/FreeTensor recorded 2026-09-02

  Repo metadata for freetensor's canonical repository - private false, archived false, fork false.

• https://github.com/roastduck/FreeTensor/blob/master/LICENSE recorded 2026-10-07

  LICENSE body carries the verbatim Apache License 2.0 text.

• https://github.com/roastduck/FreeTensor/blob/master/README.md recorded 2026-10-07

  README describes FreeTensor as compiling loop-based tensor programs written in Python to native code, with dynamic shapes, parallelization, explicit optimization transformations and automatic differentiation, and names no paid tier, enterprise edition or license-gated build beside the published source.

FreeTensor has no PyPI or other registry package and is built from source, so GitHub stars are the only adoption signal available. A star is not a use.

• https://api.github.com/repos/roastduck/FreeTensor recorded 2026-09-02

  Repo metadata - stargazers_count = 152 - for roastduck/FreeTensor.

FreeTensor is a compiler for loop-based tensor programs - parsing, transformation, parallelization, code generation and automatic differentiation - targeting OpenMP and CUDA. That narrows it to one class of programs, one step below Apache TVM's retargetable, multi-vendor scope.

• https://github.com/roastduck/FreeTensor/blob/master/README.md recorded 2026-10-07

  README describes FreeTensor as compiling loop-based tensor programs written in Python to native code, with dynamic shapes, parallelization, explicit optimization transformations and automatic differentiation, and names no paid tier, enterprise edition or license-gated build beside the published source.