PaddlePaddle Releasing Process

PaddlePaddle manages its branches using "git-flow branching model", and Semantic Versioning as it's version number semantics.

Each time we release a new PaddlePaddle version, we should follow the below steps:

  1. Fork a new branch from develop named release/[version], e.g. release/0.10.0.
  2. Push a new tag on the release branch, the tag name should be like [version]rc.patch. The first tag should be 0.10.0rc1, and the second should be 0.10.0.rc2 and so on.
  3. After that, we should do:
  4. Run all regression test on the Regression Test List (see PaddlePaddle TeamCity CI), to confirm that this release has no major bugs. * If regression test fails, we must fix those bugs and create a new release/[version] branch from previous release branch.
    • Modify python/, change the version number and change ISTAGED to True.
    • Publish PaddlePaddle release wheel packages to pypi (see below instructions for detail).
    • Update the Docker images (see below instructions for detail).
  5. After above step, merge release/[version] branch to master and push a tag on the master commit, then merge master to develop.
  6. Update the Release Note.


  • Do NOT merge commits from develop branch to release branches to keep the release branch contain features only for current release, so that we can test on that version.
  • If we want to fix bugs on release branches, we must merge the fix to master, develop and release branch.

Publish Wheel Packages to pypi

  1. Use our CI tool to build all wheel packages needed to publish. As shown in the following picture, choose a build version, click "..." button on the right side of "Run" button, and switch to the second tab in the pop-up box, choose the current release branch and click "Run Build" button. You may repeat this step to start different versions of builds.
  2. After the build succeeds, download the outputs under "Artifacts" including capi, cp27m and cp27mu.
  3. Since follows PEP 513, before we upload the package using twine, we need to rename the package from linux_x86_64 to manylinux1_x86_64.
  4. Start the upload:

    cd build/python
    pip install twine
    twine upload dist/[package to upload]
  5. NOTE: We use a special Docker image to build our releases to support more Linux distributions, you can download it from, or build it using scripts under tools/manylinux1.

  6. pypi does not allow overwrite the already uploaded version of wheel package, even if you delete the old version. you must change the version number before upload a new one.

Publish Docker Images

Our CI tool will push latest images to DockerHub, so we only need to push a version tag like:

docker pull [image]:latest
docker tag [image]:latest [image]:[version]
docker push [image]:[version]

Tags that need to be updated are: [version]: CPU only version image [version]-openblas: openblas version image [version]-gpu: GPU version(using CUDA 8.0 cudnn 5) [version]-gpu-[cudaver]-[cudnnver]: tag for different cuda, cudnn versions

You can then checkout the latest pushed tags at

Branching Model

We use git-flow as our branching model, with some modifications:

  • master branch is the stable branch. Each version on the master branch is tested and guaranteed.
  • develop branch is for development. Each commit on develop branch has passed CI unit test, but no regression tests are run.
  • release/[version] branch is used to publish each release. Latest release version branches have bugfix only for that version, but no feature updates.
  • Developer forks are not required to follow git-flow branching model, all forks is like a feature branch.
    • Advise: developer fork's develop branch is used to sync up with main repo's develop branch.
    • Advise: developer use it's fork's develop branch to for new branch to start developing.
  • Use that branch on developer's fork to create pull requests and start reviews.
    • developer can push new commits to that branch when the pull request is open.
  • Bug fixes are also started from developers forked repo. And, bug fixes branch can merge to master, develop and releases.

PaddlePaddle Regression Test List

All Chapters of PaddlePaddle Book

We need to guarantee that all the chapters of PaddlePaddle Book can run correctly. Including V1 (paddle_trainer training) and V2 training and Fluid training.

Linear Regression Recognize Digits Image Classification Word2Vec Personalized Recommendation Sentiment Analysis Semantic Role Labeling Machine Translation
API.V2 + Docker + GPU
API.V2 + Docker + CPU
`paddle_trainer` + Docker + GPU
`paddle_trainer` + Docker + CPU
API.V2 + Ubuntu + GPU
API.V2 + Ubuntu + CPU
`paddle_trainer` + Ubuntu + GPU
`paddle_trainer` + Ubuntu + CPU