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Python supercharged for fastai development

Project description

from nbdev.showdoc import *
from fastcore.all import *
import numpy as np,numbers

Welcome to fastcore

Python goodies to make your coding faster, easier, and more maintainable

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.

Getting started

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 show with the -latest suffix here.

fastcore contains many features, including:

  • fastcore.test: Simple testing functions
  • fastcore.foundation: Mixins, delegation, composition, and more
  • fastcore.xtras: Utility functions to help with functional-style programming, parallel processing, and more
  • fastcore.dispatch: Multiple dispatch methods
  • fastcore.transform: Pipelines of composed partially reversible transformations

To get started, we recommend you read through the fastcore tour.

Contributing

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.

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