Contributing to GNES¶
🙇 Thanks for your interest in contributing! GNES always welcome the contribution from the open-source community, individual committers and other partners. Without you, GNES can’t be successful.
❤️ Making Your First Commit¶
The beginning is always the hardest. But fear not, even if you find a typo, a missing docstring or unit test, you can simply correct them by making a commit to GNES. Here are the steps:
- Create a new branch, say
fix-gnes-typo-1
- Fix/improve the codebase
- Commit the changes. Note the commit message must follow the naming style, say
fix(readme): improve the readability and move sections
- Make a pull request. Note the commit message must follow the naming style. It can simply be one of your commit messages, just copy paste it, e.g.
fix(readme): improve the readability and move sections
- Submit your pull request and wait for all checks passed (usually 10 minutes)
- Coding style
- Commit and PR styles check
- All unit tests
- Request reviews from one of the developers from our core team.
- Get a LGTM 👍 and PR gets merged.
Well done! Once a PR gets merged, here are the things happened next:
- all Docker images tagged with
-latest
will be automatically updated in an hour. You may check the its building status at here - on every Friday when a new release is published, PyPi packages and all Docker images tagged with
-stable
will be updated accordindly. - your contribution and commits will be included in our weekly release note. 🍻
Table of Content¶
Commit Message Naming¶
To help everyone with understanding the commit history of GNES, we employ commitlint
in the CI pipeline to enforce the commit styles. Specifically, our convention is:
type(scope?): subject
where type
is one of the following:
- build
- ci
- chore
- docs
- feat
- fix
- perf
- refactor
- revert
- style
- test
scope
is optional, represents the module your commit working on.
subject
explains the commit.
As an example, a commit that implements a new encoder should be phrased as:
feat(encoder): add new inceptionV3 as image encoder
Merging Process¶
A pull request has to meet the following conditions to be merged into master:
- Coding style check (PEP8, via Codacy)
- Commit style check (in CI pipeline via Drone.io)
- Unit tests (via Drone.io)
- Review and approval from a GNES team member.
After the merging is triggered, the build will be delivered to the followings:
- Docker Hub:
gnes:latest
will be updated. - Tencent Container Service:
gnes:latest
will be updated. - ReadTheDoc:
latest
will be updated. - Benchmark: speed test will be updated.
Note that merging into master does not mean an official releasing. For the releasing process, please refer to the next section.
Release Process¶
A new release is scheduled on every Friday (triggered and approved by Han Xiao) summarizing all new commits since the last release. The release will increment the third (revision) part of the version number, i.e. from 0.0.24
to 0.0.25
.
After a release is triggered, the build will be delivered to the followings:
- Docker Hub: a new image with the release version tag will be created,
gnes:latest
will be updated. - Tencent Container Service: a new image with the release version tag will be created,
gnes:latest
will be updated. - PyPi Package: a new version of Python package is uploaded to Pypi, allowing one to
pip install -U gnes
- ReadTheDoc: a new version of the document will be built,
latest
will be updated and the old version will be achieved - Benchmark: speed test will be updated.
Meanwhile, a new pull request containing the updated CHANGELOG and the new version number will be made automatically, pending for review and merge.
Major and minor version increments¶
- MAJOR version when GNES make incompatible API changes;
- MINOR version when GNES add functionality in a backwards-compatible manner.
The decision of incrementing major and minor version, i.e. from 0.0.0
to 0.1.0
or from 1.0.0
to 2.0.0
, is made by the GNES team.
Testing Locally¶
The best way to test GNES is using a Docker container, in which you don’t have to worry about the dependencies.
We provide a public Docker image gnes/ci-base
, which contains the required dependencies and some pretrained models used in our continuous integration pipeline.
You can find the image at here or pull the image via:
docker pull gnes/ci-base
To test GNES inside this image, you may run
docker run --network=host --rm --entrypoint "/bin/bash" -it gnes/ci-base
# now you are inside the 'gnes/ci-base' container
# first sync your local modification, then
pip install -e .[all]
python -m unittest tests/*.py
Interesting Points¶
Currently there are three major directions of contribution:
- Porting state-of-the-art models to GNES. This includes new preprocessing algorithms, new DNN networks for encoding, and new high-performance index. Believe me, it is super easy to wrap an algorithm and use it in GNES. Checkout this example.
- Adding tutorial and learning experience. What is good and what can be improved? If you apply GNES in your domain, whether it’s about NLP or CV, whether it’s a blog post or a Reddit/Twitter thread, we are always eager to hear your thoughts.
- Completing the user experience of other programming languages. GNES offers a generic interface with gRPC and protobuf, therefore it is easy to add an interface for other languages, e.g. Java, C, Go.