giacomov/fastai2

Temporary home for fastai v2 while it's being developed

Welcome to fastai v2

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

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

Installing

Normal installation

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:

git clone https://github.com/fastai/fastai2
cd fastai2
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 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:

pip install nbdev

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

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

Docker

If you have docker>=19.03 set up (see here for installing it with GPU support) you can use fastai2 without installing anything:

Running jupyter lab

The first time you need to run:

docker run --name fastai2 --gpus all -p 8890:8890 giacomov/fastai2 jupyter lab --ip='*' --port 8890 --no-browser

After a few seconds you will see something like:

The Jupyter Notebook is running at:
http://fe4c6f41277b:8890/?token=6aac41e796bae49489abf941d277f668d384b429210bedbe
 or http://127.0.0.1:8890/?token=6aac41e796bae49489abf941d277f668d384b429210bedbe

Copy paste the latest url (starting with http://127.0.0.1) into your browser and enjoy fastai!

From Jupyter Lab you can also open a terminal in the container, if you need a command line.

Use Ctrl-C in the same terminal where you started the container to stop it. Your file will be kept in the container.

The next time you can simply do:

docker start -i fastai2

to resume where you stopped.

Where are my files

Everything you create in the container stays in the container, until you remove the container with docker rm fastai2. If you want to export your files to the host you can use the command docker cp. You can also mount directories in the container, see the docker documentation for that.

Tests

To run the tests in parallel, launch:

nbdev_test_nbs

or

make test

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.