%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
[](https://github.com/fastai/fastai/actions?query=workflow%3ACI) [](https://pypi.org/project/fastai2/#description) [](https://anaconda.org/fastai/fastai2) [](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).
%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
[](https://github.com/fastai/fastai/actions?query=workflow%3ACI) [](https://pypi.org/project/fastai2/#description) [](https://anaconda.org/fastai/fastai2) [](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).
%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
[](https://github.com/fastai/fastai/actions?query=workflow%3ACI) [](https://pypi.org/project/fastai2/#description) [](https://anaconda.org/fastai/fastai2) [](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).
%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
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.30-1
- Package Spec generated