PaddlePaddle

license

Apache-2.0(OSI)

source

public(PaddlePaddle/Paddle)

core features withheld

no — no enterprise or ee path in the repository root and no paid tier described in the README

Paddle is released under a stock Apache-2.0 license, with a PaddlePaddle Authors copyright line prepended above the standard text, and the full source is public in this repository. The README describes the whole framework with no enterprise or ee path and no paid tier, so the core is ungated.

• https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/LICENSE recorded 2026-10-06

  LICENSE body, read in full: a prepended 'Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved' line above the stock Apache License 2.0 text. The remainder is byte-identical to the canonical template through END OF TERMS AND CONDITIONS, followed only by the standard appendix. No appended condition.

• https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/README.md recorded 2026-10-06

  README, read for the distribution and gating question: it documents installing the framework from this repository or from the paddlepaddle package and names no enterprise, paid or licence-gated build.

Adoption is measured as PyPI downloads of the paddlepaddle package. GitHub stars are also recorded, but only as corroboration, since a star is not an install.

• https://api.github.com/repos/PaddlePaddle/Paddle recorded 2026-08-31

  Repo metadata for PaddlePaddle/Paddle: stargazers_count 24064, archived false, pushed_at 2026-08-31.

• https://pypistats.org/api/packages/paddlepaddle/recent recorded 2026-10-07

  922,480 downloads in the trailing 30 days for paddlepaddle (pypistats last_month)

Paddle is a foundational framework in the same sense as PyTorch and TensorFlow: it owns its own graph engine, distributed execution and deployment path rather than building on somebody else's. Its automatic-parallelism and integrated training-inference design are the distinguishing claims.

• https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/README.md recorded 2026-10-07

  The 'PaddlePaddle New Generation Framework 3.2' section documents unified dynamic/static graphs with automatic parallelism from single-card tensor partitioning annotations, integrated training and inference for large models within one framework, and high-order differentiation for scientific computing.