Python is a powerful, dynamic language. Rather than bake everything into
the language, it lets the programmer customize it to make it work for
them. fastcore uses this flexibility to add to Python features
inspired by other languages we’ve loved, like multiple dispatch from
Julia, mixins from Ruby, and currying, binding, and more from Haskell.
It also adds some “missing features” and clean up some rough edges in
the Python standard library, such as simplifying parallel processing,
and bringing ideas from NumPy over to Python’s list type.
To install fastcore run: conda install fastcore -c fastai (if you use
Anaconda, which we recommend) or pip install fastcore. For an
editable
install,
clone this repo and run: pip install -e ".[dev]". fastcore is tested
to work on Ubuntu, macOS and Windows (versions tested are those shown
with the -latest suffix
here).
fastcore contains many features, including:
fastcore.test: Simple testing functionsfastcore.foundation: Mixins, delegation, composition, and morefastcore.xtras: Utility functions to help with functional-style programming, parallel processing, and morefastcore.dispatch: Multiple dispatch methodsfastcore.transform: Pipelines of composed partially reversible transformations
To get started, we recommend you read through the fastcore tour.
After you clone this repository, please run nbdev_install_hooks in
your terminal. This sets up git hooks, which clean up the notebooks to
remove the extraneous stuff stored in the notebooks (e.g. which cells
you ran) which causes unnecessary merge conflicts.
To run the tests in parallel, launch nbdev_test.
Before submitting a PR, check that the local library and notebooks match.
- If you made a change to the notebooks in one of the exported cells,
you can export it to the library with
nbdev_prepare. - If you made a change to the library, you can export it back to the
notebooks with
nbdev_update.