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[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf) | ||
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# Python Package Template | ||
A easy, reliable, fluid template for python packages complete with docs, testing suites, readme's, github workflows, linting and much much more | ||
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## Installation | ||
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You can install the package using pip | ||
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```bash | ||
pip install -e . | ||
``` | ||
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# Usage | ||
```python | ||
print("hello world") | ||
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``` | ||
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### Code Quality 🧹 | ||
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We provide two handy commands inside the `Makefile`, namely: | ||
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- `make style` to format the code | ||
- `make check_code_quality` to check code quality (PEP8 basically) | ||
- `black .` | ||
- `ruff . --fix` | ||
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### Tests 🧪 | ||
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[`pytests`](https://docs.pytest.org/en/7.1.x/) is used to run our tests. | ||
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### Publish on PyPi 🚀 | ||
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**Important**: Before publishing, edit `__version__` in [src/__init__](/src/__init__.py) to match the wanted new version. | ||
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``` | ||
poetry build | ||
poetry publish | ||
``` | ||
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### CI/CD 🤖 | ||
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We use [GitHub actions](https://github.com/features/actions) to automatically run tests and check code quality when a new PR is done on `main`. | ||
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On any pull request, we will check the code quality and tests. | ||
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When a new release is created, we will try to push the new code to PyPi. We use [`twine`](https://twine.readthedocs.io/en/stable/) to make our life easier. | ||
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The **correct steps** to create a new realease are the following: | ||
- edit `__version__` in [src/__init__](/src/__init__.py) to match the wanted new version. | ||
- create a new [`tag`](https://git-scm.com/docs/git-tag) with the release name, e.g. `git tag v0.0.1 && git push origin v0.0.1` or from the GitHub UI. | ||
- create a new release from GitHub UI | ||
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The CI will run when you create the new release. | ||
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# Docs | ||
We use MK docs. This repo comes with the zeta docs. All the docs configurations are already here along with the readthedocs configs. | ||
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# Tests | ||
`pytest` | ||
# Vision Datasets | ||
Scripts and logic to create high quality pre-training and finetuning datasets for multi-modal models! | ||
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# License | ||
MIT |