NB: This is still in early development. Use v1 unless you want to contribute to the next version of fastai

CI-Badge PyPI Conda (channel only) Build fastai2 images

To learn more about the library, read our introduction in the paper presenting it.

Note that the docs are in a submodule, so to clone with docs included, you should use:

 git clone --recurse-submodules https://github.com/fastai/fastai2

If you're using a fork of fastai2, you'll need to fork the fastai-docs repo as well.

Installing

You can get all the necessary dependencies by simply installing fastai v1: conda install -c fastai -c pytorch fastai. Or alternatively you can automatically install the dependencies into a new environment:

conda env create -f environment.yml
source activate fastai2

Then, you can install fastai v2 with pip: pip install fastai2.

Or you can use an editable install (which is probably the best approach at the moment, since fastai v2 is under heavy development):

git clone --recurse-submodules https://github.com/fastai/fastai2
cd fastai2
pip install -e ".[dev]"

You should also use an editable install of fastcore to go with it.

If you want to browse the notebooks and build the library from them you will need nbdev, which you can install with conda or pip.

To use fastai2.medical.imaging you'll also need to:

conda install pyarrow
pip install pydicom kornia opencv-python scikit-image

Tests

To run the tests in parallel, launch:

nbdev_test_nbs

or

make test

For all the tests to pass, you'll need to install the following optional dependencies:

pip install "sentencepiece<0.1.90" wandb tensorboard albumentations pydicom opencv-python scikit-image pyarrow kornia

Contributing

After you clone this repository, please run nbdev_install_git_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.

Before submitting a PR, check that the local library and notebooks match. The script nbdev_diff_nbs can let you know if there is a difference between the local library and the notebooks.

  • If you made a change to the notebooks in one of the exported cells, you can export it to the library with nbdev_build_lib or make fastai2.
  • If you made a change to the library, you can export it back to the notebooks with nbdev_update_lib.

Docker Containers

For those interested in offical docker containers for this project, they can be found here.