%global _empty_manifest_terminate_build 0 Name: python-fastai2 Version: 0.0.30 Release: 1 Summary: Version 2 of the fastai library License: Apache Software License 2.0 URL: https://github.com/fastai/fastai2 Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d0/6a/c7bd066839796bb19e10f481dd0c7f7c33ee75eb86a6f87279aeebcb4b9d/fastai2-0.0.30.tar.gz BuildArch: noarch Requires: python3-pip Requires: python3-packaging Requires: python3-fastcore Requires: python3-torchvision Requires: python3-matplotlib Requires: python3-pandas Requires: python3-requests Requires: python3-pyyaml Requires: python3-fastprogress Requires: python3-pillow Requires: python3-scikit-learn Requires: python3-scipy Requires: python3-spacy Requires: python3-torch Requires: python3-nbdev %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/fastai/workflows/CI/badge.svg)](https://github.com/fastai/fastai/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. ## 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). %package -n python3-fastai2 Summary: Version 2 of the fastai library Provides: python-fastai2 BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-fastai2 # 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/fastai/workflows/CI/badge.svg)](https://github.com/fastai/fastai/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. ## 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). %package help Summary: Development documents and examples for fastai2 Provides: python3-fastai2-doc %description help # 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/fastai/workflows/CI/badge.svg)](https://github.com/fastai/fastai/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. ## 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). %prep %autosetup -n fastai2-0.0.30 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-fastai2 -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Wed May 10 2023 Python_Bot - 0.0.30-1 - Package Spec generated