Contributions are welcome, and greatly appreciated! Every little bit helps, and credit will always be given. We recommend you to check our Developer Tools Guide to make the development process easier and standard.
Notably, you can follow the tag of call for contributors in the issues. Those issues are designed for the external contributors to solve. The pull requests solving these issues are most likely to be merged.
There are many ways to contribute to Auto-Keras, including submit feedback, fix bugs, implement features, and write documentation. The guide for each type of contribution is as follows.
The feedback should be submitted by creating an issue at GitHub issues. Select the related template (bug report, feature request, or custom) and add the corresponding labels.
You may look through the GitHub issues for bugs. Anything tagged with "bug report" is open to whoever wants to implement it. Please follow the Pull Request Guide to submit your pull request. Please also read Code Style Guide, and Documentation Guide to ensure your merge request meet our requirements.
You may look through the GitHub issues for feature requests. Anything tagged with "feature request" is open to whoever wants to implement it. Please follow the Pull Request Guide to submit your pull request. Please also read Code Style Guide, Documentation Guide, and Testing Guide to ensure your merge request meet our requirements.
The documentation of Auto-Keras is either directly written into the Markdown files in mkdocs directory, or automatically extracted from the docstrings by executing the autogen.py. In the first situation, you only need to change the markdown file. In the second situation, you need to change the docstrings and execute autogen.py to update the Markdown files. Please follow the Pull Request Guide to submit your pull request. Please also read Documentation Guide to ensure your merge request meet our requirements.
Before you submit a pull request, check that it meets these guidelines:
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Fork the repository. Create a new branch from the master branch. Give your new branch a meaningful name.
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Pull request from your new branch to the master branch of the original autokeras repo. Give your pull request a meaningful name.
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Include "resolves #issue_number" in the description of the pull request and briefly describe your contribution.
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Submit the pull request from the first day of your development (after your first commit) and prefix the title of the pull request with [WIP].
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When the contribution is complete, make sure the pull request passed the CI tests. Change the [WIP] to [MRG]. Set the reviewer to @jhfjhfj1.
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For the case of bug fixes, add new test cases which would fail before your bug fix.
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If you are a collaborator of the autokeras repo, you don't need to fork the repository. Just create a new branch directly. You also need to change the assignee to the reviewer when request for code review. The reviewer will change the assignee back to you when finished the review. The assignee always means who should push the progress of the pull request now.
This project tries to closely follow the official Python Style Guide detailed in PEP8. The docstrings follow the Google Python Style Guide. Please follow these style guide closely, especially for the docstrings, which would be extracted automatically to generate the documentation.
The documentation should be provided in two ways, docstring, tutorial, and readme file. We prefer the documentation to be as complete as possible.
All the methods and classes may directly be called by the user need to be documented with docstrings. The docstrings should contain all the fields required by the Google Python Style Guide.
You only need to add tutorials to your code if you are contributing or updating a new task module,
e.g. TextClassifier, VideoClassifier,
or a new function could be directly called by the user.
You can modify mkdocs/docs/start.md to add your tutorial.
The code example of your new task module should be added to the examples directory.
You only need to add tutorials to your code if you are contributing or updating a new task module,
e.g. TextClassifier, VideoClassifier.
The readme file should be named as README.md.
It should be written in Markdown.
The content should contain your name, affiliation, and any reference to the method you use.
Pytest is used to write the unit tests of Auto-Keras.
You should test your code by writing unit testing code in tests directory.
The testing file name should be the .py file with a prefix of test_ in the corresponding directory,
e.g., the name should be test_layers.py if the code of which is to test layer.py.
The tests should be run in the root directory of the project by executing the cov.sh file.
It would output the coverage information into a directory named htmlcov.
Please make sure the code coverage percentage does not decrease after your contribution,
otherwise, the code will not be merged.
We highly recommend you to use Pycharm and virtualenvwrapper.
Pycharm is the best IDE for large project development in Python. We recommend you inspect the code before you pull request to fix any error and warning suggested by the inspection.
Virtualenvwrapper is a tool to build separated Python environment for each project. In this way, you can install a different version of Tensorflow, Pytorch, or any other package for each project. We recommend you to create a virtualenv for autokeras development with virtualenvwrapper, and only install the packages required by autokeras with the corresponding version. The virtualenv should be created based on Python 3.6 interpreter. Use pycharm to select the virtualenv as interpreter.
You may checkout this code review video to get familiar with the code structure. Other than the base classes you have to extend, there are some other classes you can extend.
autokeras.model_trainer.ModelTrainer is a class for training Pytorch models.
If needed a new metric or loss function other than the ones we have, you can add your own to loss_function.py and metric.py.
You can follow its documentation and this example to use it.
Make sure your loss function, metric, Pytorch model, and Dataloader are compatible with each other.
We really appreciate all the contributions. To show our appreciation to those who contributed most, we would like to maintain a list of main contributors. To be in the list, you need to meet the following requirments.
- Be on campus of Texas A&M University.
- Constantly present in our meetings.
- Constantly contribute code to our repository.
- Keep the above for over 6 months.