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We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.
Here are a few more things to know:
Here is a simple guideline to get you started with your first contribution
Use issues to discuss the suggested changes. Create an issue describing changes if necessary and add labels to ease orientation.
Fork super-gradients so you can make local changes and test them.
Create a new branch for the issue. The branch naming convention is enforced by the CI/CD so please make sure you are using your_username/your_branch_name convention otherwise it will fail.
Create relevant tests for the issue, please make sure you are covering unit, integration and e2e tests where required.
Make code changes.
Ensure all the tests pass and code formatting is up to standards, and follows PEP8.
We use pre-commit package to run our pre-commit hooks. Black formatter and flake8 linter will be ran on each commit. In order to set up pre-commit on your machine, follow the steps here, please note that you only need to run these steps the first time you use pre-commit for this project.
$ pip install pre-commit
$ pre-commit install
$ git commit -m "message"
Each time you commit, git will run the pre-commit hooks (black and flake8 for now) on any python files that are getting committed and are part of the git index. If black modifies/formats the file, or if flake8 finds any linting errors, the commit will not succeed. You will need to stage the file again if black changed the file, or fix the issues identified by flake8 and and stage it again.
To run pre-commit on all files just run
$ pre-commit run --all-files
Create a pull request against master branch.
Pulling updates from remote might cause merge conflicts with jupyter notebooks. The tool nbdime might solve this.
pip install ndime
nbdiff notebook_1.ipynb notebook_2.ipynb
We are working hard to make sure all the code in this repository is readable, maintainable and testable. We follow the Google docstring guidelines outlined on this styleguide page. For example:
def python_function(first_argument: int, second_argument: int) -> bool:
"""
This function will do something with the two arguments.
Args:
first_argument (int): the first argument to the function
second_argument (int): the second argument to the function
Raises:
Exception: arguments should be ints not floats
Returns:
bool: whether or not the calculation was correct
"""
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Browsing data directories saved to S3 is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!
super-gradients is now integrated with AWS S3!
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Browsing data directories saved to Google Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!
super-gradients is now integrated with Google Cloud Storage!
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Browsing data directories saved to Azure Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!
super-gradients is now integrated with Azure Cloud Storage!
Are you sure you want to delete this access key?
Browsing data directories saved to S3 compatible storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!
super-gradients is now integrated with your S3 compatible storage!
Are you sure you want to delete this access key?