Metadata-Version: 2.1
Name: fastai2
Version: 0.0.26
Summary: Version 2 of the fastai library
Home-page: https://github.com/fastai/fastai2
Author: Jeremy Howard, Sylvain Gugger, and contributors
Author-email: info@fast.ai
License: Apache Software License 2.0
Description: # Welcome to fastai v2
        > NB: This is still in early development. Use v1 unless you want to contribute to the next version of fastai
        
        
        [![CI-Badge](https://github.com/fastai/fastai2/workflows/CI/badge.svg)](https://github.com/fastai/fastai2/actions?query=workflow%3ACI) [![PyPI](https://img.shields.io/pypi/v/fastai2?color=blue&label=pypi%20version)](https://pypi.org/project/fastai2/#description) [![Conda (channel only)](https://img.shields.io/conda/vn/fastai/fastai2?color=seagreen&label=conda%20version)](https://anaconda.org/fastai/fastai2) [![Build fastai2 images](https://github.com/fastai/docker-containers/workflows/Build%20fastai2%20images/badge.svg)](https://github.com/fastai/docker-containers)
        
        To learn more about the library, read our introduction in the [paper](https://arxiv.org/abs/2002.04688) 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. 
        
        <!-- START doctoc generated TOC please keep comment here to allow auto update -->
        
        ## 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:
        
        ```bash
        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](https://github.com/fastai/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:
        
        ```bash
        conda install pyarrow
        pip install pydicom kornia opencv-python scikit-image
        ```
        
        ## Tests
        
        To run the tests in parallel, launch:
        
        ```bash
        nbdev_test_nbs
        ```
        or 
        ```bash
        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](https://github.com/fastai/docker-containers#fastai2).
        
Keywords: fastai,deep learning,machine learning
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
