hivemind/CONTRIBUTING.md

Contributing to hivemind

This document covers the technical details of making your contributions to the code of hivemind. For other ways to contribute, read the contributing guide in our documentation.

Before you begin, file a new issue on GitHub or announce that you are going to work on an existing one to avoid duplicate effort. After you finish, submit a pull request and wait for it to be reviewed by the library maintainers (and possibly other community members).

Environment setup

First, install hivemind in the development mode, preferably with Python 3.8 on Linux.

git clone https://github.com/learning-at-home/hivemind
cd hivemind
pip install -e .

Pull Request checklist

To make sure that the reviewers will request minimal changes in your PR, you can check that your contribution complies with the following rules:

• All code changes are consistent with the repository code style.
• The title and the description of your pull request adhere to the formatting guidelines both for pull requests and for commit messages.
• New modules or functions are sufficiently documented and covered with tests.
• The CI pipelines both for the documentation and for tests pass successfully.
• If you make performance-sensitive changes, their impact is measured with benchmarks (the more, the better).

Code style

• The code must follow PEP8 unless absolutely necessary. Also, each line cannot be longer than 120 characters.
• We highly encourage the use of typing where applicable.
• Use get_logger from hivemind.utils.logging to log any information instead of printing directly to standard output/error streams.
• Comments should be used sparingly and never describe the obvious; usually it's best to clean up the code logic instead of describing it, as it might lead to redundant (or worse, stale or incorrect).
• In general, strive for code readability instead of compactness. In particular, prefer to create a new variable instead of a long one-liner and to break up a long method into several meaningful parts. This rule can be overridden in case of major performance considerations, but only if verified by benchmarks.
• Each user-facing function must have a correct docstring that describes the intended usage, the input arguments and the return value. Both in comments and docstrings, please try to follow the capitalization rules for all terms and objects and to use proper grammar.

Contribution formatting guidelines

To make sure that each change to hivemind is consistent across the entire project history and is easy to review by any community member, follow these guidelines when submitting your pull request and writing commit messages. The library maintainers use the same rules when merging your commits into the master branch after the PR approval.

Commit messages

To ensure a consistent format across the entire repository history, please follow the following rules when formatting your commits (especially for PR merge commits):

• Keep the subject line short, preferably under 50 characters.
• Capitalize the subject line and do not end it with a period.
• If possible, write a conceptual description of your commit in the body (why?) instead

It is not required to use this format while you are still working on your pull request. However, each merged PR commit message has to adhere to these guidelines, and it will be easier for the maintainers to accept the PR if you have already done most of the necessary formatting work.

For further reading on the commit message format, see this guide on good Git commit messages, as well as this repository.

Pull requests

All commits from a pull request are squashed before merging to ensure a clean commit history in the master branch. The merge commit title is the name of the pull request along with the PR number reference; the merge commit body is either the pull request description (if it adheres to the format) or a cleaned up compilation of PR branch commit messages.

• As such, the name and the description of your PR should follow the same guidelines as commit messages.
• Try to make your pull requests more narrow in scope and split significant changes to the code base in separate pieces. This will ensure faster and better feedback from the reviewers.
• In particular, try to separate functional and non-functional code changes, as well as independent functional changes if they make the pull request too large to review in a short period of time.
• In general, when naming a pull request instead of a commit, it's best to highlight the major change in its title instead of listing all modifications. Also, if a pull request makes significant changes to the library, it's best to give a high-level description in the title instead of a technical one: compare Implement decentralized parameter averaging with Add hivemind.client.averaging.

For more on the philosophy of easy-to-review pull requests, read these guides: 1 2 . If the changelist is not very large (more than a hundred lines) already, we encourage making small improvements to the codebase in the files already changed by the PR; however, they should not dilute its major purpose.

Running tests

Hivemind uses pytest for testing the behavior of the library modules. If you implement a new part of the library, you are expected to write a test for the correctness of its implementation. If you discovered a bug in the existing code base and intend to fix it, it's also best if you add the steps to reproduce it as a new test to make sure it's not reintroduced by future changes.

To run tests, you need to install hivemind in development mode with additional dependencies: pip install -e .[dev]. You can run all tests with pytest tests/ or choose a specific subset, e.g., pytest tests/test_dht.py.

Building documentation

Any function exposed to a user must have a docstring compatible with Sphinx, which is used for building the online documentation.

To build the docs locally,

1. pip install -e .[docs]
2. make sure you ran setup.py (see above)
3. cd ./docs && make html

The documentation root will be available in ./docs/_build/html/index.html

Running benchmarks

Currently, hivemind has three benchmark scripts for evaluating the impact of code changes on the most performance-sensitive parts of the library. If you make a change that might introduce a regression, you may be asked by the maintainers to provide the benchmarking results for your branch and a comparison with the master branch.

• tests/benchmark_averaging.py measures the performance of decentralized parameter averaging across the DHT.
• tests/benchmark_dht.py measures the performance of core DHT operations.
• tests/benchmark_throughput.py measures the performance of a server hosting several expert layers under heavy load from multiple clients.

Example benchmark runs are available in the benchmarking page of the documentation.

See also

For more details on overall contributions, visit the contributing guide at:

https://learning-at-home.readthedocs.io/en/latest/user/contributing.html

This guide was inspired by several influential Python open source projects listed below:

• PyTorch
• Scikit-learn
• transformers