All our development here in this repository is done using Git and Github. If you're not very much familiar with Git and Github, start by reviewing this guide. https://guides.github.com/activities/hello-world/
On https://github.com/vrook-co/oto-helper/issues you can find all open Issues. You can find a detailed explanation on how to work on issues below under Issue Allocation.
To contribute to oto-helper you'll need to fork this repository(oto-helper) into your github account. Then you can work risk-free on your fork without affecting oto-helper repository.
To work on an issue in your local computer write git clone https://github.com/<your-username>/oto-helper.git command into terminal. Which will copy the whole code to your computer.
You'll just need to fork once. After that you can call git fetch upstream and git pull 'branch-name' before you do your local changes to get the remote changes and be up-to-date.
You can install it with the command pip install pre-commit
Then you just need to call pre-commit install
All this can also be done by running command make install_hooks
To sync your fork with this repository please see this Guide on how to sync your fork.
If you want to work on an open issue, please post a comment telling that you'll work on that issue, we will assign that issue to you as the assignee then.
Caution: We will try our best to keep the assignee up-to-date, but as we are all humans with our own schedule delays are possible, so make sure to check the comments once before you start working on an issue even when no one is assigned to it.
It's important to write test cases, and test the code before submitting a Pull Request (PR).
Oto-helper is using pytest to execute the test cases.
Sometimes you want to test functions that hold multiple arguments, which again can have multiple values. To test this, please parametrize your tests.
Example:
@pytest.mark.parametrize(
"compress, compressScheme", [(True, "lz4"), (False, "lz4")]
)
def test_hooked_tensor(self, compress, compressScheme):
TorchHook(torch)
t = Tensor(numpy.random.random((100, 100)))
t_serialized = serialize(t, compress=compress, compressScheme=compressScheme)
t_serialized_deserialized = deserialize(
t_serialized, compressed=compress, compressScheme=compressScheme
)
assert (t == t_serialized_deserialized).all()To ensure code quality and make sure other people can understand your changes, make proper documentation for your code. For documentation we are using the Google Python Style Rules which can be found here. A well-written example can be viewed here.
You documentation should explain what's the intention behind the code and how you tried to realize your intention.
You should also document non self-explanatory code fragments e.g. complicated for-loops. Again please don't just describe about each line, but explain the idea behind the code fragment and why you've decided to use that exact solution.
The codebase contains static type hints for code clarity and catching errors prior to runtime. If you're adding type hints, please run the static type checker to ensure the type annotations you added are correct via:
mypy filenameAs with any software project, it's important to keep the amount of code to a minimum, so keep code duplication to a minimum!
At any point in time you can create a pull request, so others can see your changes and give you feedback.
Please create all pull requests to the master branch.
We will only merge PRs that pass the tests.
If your check fails, don't worry, you will still be able to make changes and make your code pass the checks.
Thank you all who have contributed to this repository(oto-helper). I hope that this repository will be beneficial to all who need help in this repository. But please use this repository wisely.