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@@ -0,0 +1 @@ +/fastai-2.7.12.tar.gz diff --git a/python-fastai.spec b/python-fastai.spec new file mode 100644 index 0000000..5c97ab3 --- /dev/null +++ b/python-fastai.spec @@ -0,0 +1,505 @@ +%global _empty_manifest_terminate_build 0 +Name: python-fastai +Version: 2.7.12 +Release: 1 +Summary: fastai simplifies training fast and accurate neural nets using modern best practices +License: Apache Software License 2.0 +URL: https://github.com/fastai/fastai +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a8/8e/46a85646fd441d5beebf7db5d3c6086c1744f3511fe99ba00c5af91e5ec3/fastai-2.7.12.tar.gz +BuildArch: noarch + +Requires: python3-pip +Requires: python3-packaging +Requires: python3-fastdownload +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-ipywidgets +Requires: python3-pytorch-lightning +Requires: python3-pytorch-ignite +Requires: python3-transformers +Requires: python3-sentencepiece +Requires: python3-tensorboard +Requires: python3-pydicom +Requires: python3-catalyst +Requires: python3-flask-compress +Requires: python3-captum +Requires: python3-flask +Requires: python3-wandb +Requires: python3-kornia +Requires: python3-scikit-image +Requires: python3-neptune-client +Requires: python3-comet-ml +Requires: python3-albumentations +Requires: python3-opencv-python +Requires: python3-pyarrow +Requires: python3-ninja +Requires: python3-timm +Requires: python3-accelerate + +%description +<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! --> + +[](https://pypi.org/project/fastai/#description) +[](https://anaconda.org/fastai/fastai) +[](https://github.com/fastai/docker-containers) + +## Installing +You can use fastai without any installation by using [Google +Colab](https://colab.research.google.com/). In fact, every page of this +documentation is also available as an interactive notebook - click “Open +in colab” at the top of any page to open it (be sure to change the Colab +runtime to “GPU” to have it run fast!) See the fast.ai documentation on +[Using Colab](https://course.fast.ai/start_colab) for more information. +You can install fastai on your own machines with conda (highly +recommended), as long as you’re running Linux or Windows (NB: Mac is not +supported). For Windows, please see the “Running on Windows” for +important notes. +If you’re using +[miniconda](https://docs.conda.io/en/latest/miniconda.html) +(recommended) then run (note that if you replace `conda` with +[mamba](https://github.com/mamba-org/mamba) the install process will be +much faster and more reliable): +``` bash +conda install -c fastchan fastai +``` +…or if you’re using +[Anaconda](https://www.anaconda.com/products/individual) then run: +``` bash +conda install -c fastchan fastai anaconda +``` +To install with pip, use: `pip install fastai`. If you install with pip, +you should install PyTorch first by following the PyTorch [installation +instructions](https://pytorch.org/get-started/locally/). +If you plan to develop fastai yourself, or want to be on the cutting +edge, you can use an editable install (if you do this, you should also +use an editable install of +[fastcore](https://github.com/fastai/fastcore) to go with it.) First +install PyTorch, and then: + git clone https://github.com/fastai/fastai + pip install -e "fastai[dev]" +## Learning fastai +The best way to get started with fastai (and deep learning) is to read +[the +book](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527), +and complete [the free course](https://course.fast.ai). +To see what’s possible with fastai, take a look at the [Quick +Start](https://docs.fast.ai/quick_start.html), which shows how to use +around 5 lines of code to build an image classifier, an image +segmentation model, a text sentiment model, a recommendation system, and +a tabular model. For each of the applications, the code is much the +same. +Read through the [Tutorials](https://docs.fast.ai/tutorial.html) to +learn how to train your own models on your own datasets. Use the +navigation sidebar to look through the fastai documentation. Every +class, function, and method is documented here. +To learn about the design and motivation of the library, read the [peer +reviewed paper](https://www.mdpi.com/2078-2489/11/2/108/htm). +## About fastai +fastai is a deep learning library which provides practitioners with +high-level components that can quickly and easily provide +state-of-the-art results in standard deep learning domains, and provides +researchers with low-level components that can be mixed and matched to +build new approaches. It aims to do both things without substantial +compromises in ease of use, flexibility, or performance. This is +possible thanks to a carefully layered architecture, which expresses +common underlying patterns of many deep learning and data processing +techniques in terms of decoupled abstractions. These abstractions can be +expressed concisely and clearly by leveraging the dynamism of the +underlying Python language and the flexibility of the PyTorch library. +fastai includes: +- A new type dispatch system for Python along with a semantic type + hierarchy for tensors +- A GPU-optimized computer vision library which can be extended in pure + Python +- An optimizer which refactors out the common functionality of modern + optimizers into two basic pieces, allowing optimization algorithms to + be implemented in 4–5 lines of code +- A novel 2-way callback system that can access any part of the data, + model, or optimizer and change it at any point during training +- A new data block API +- And much more… +fastai is organized around two main design goals: to be approachable and +rapidly productive, while also being deeply hackable and configurable. +It is built on top of a hierarchy of lower-level APIs which provide +composable building blocks. This way, a user wanting to rewrite part of +the high-level API or add particular behavior to suit their needs does +not have to learn how to use the lowest level. +<img alt="Layered API" src="https://raw.githubusercontent.com/fastai/fastai/master/images/layered.png" width="345"> +## Migrating from other libraries +It’s very easy to migrate from plain PyTorch, Ignite, or any other +PyTorch-based library, or even to use fastai in conjunction with other +libraries. Generally, you’ll be able to use all your existing data +processing code, but will be able to reduce the amount of code you +require for training, and more easily take advantage of modern best +practices. Here are migration guides from some popular libraries to help +you on your way: +- [Plain PyTorch](https://docs.fast.ai/examples/migrating_pytorch.html) +- [Ignite](https://docs.fast.ai/examples/migrating_ignite.html) +- [Lightning](https://docs.fast.ai/examples/migrating_lightning.html) +- [Catalyst](https://docs.fast.ai/examples/migrating_catalyst.html) +## Windows Support +When installing with `mamba` or `conda` replace `-c fastchan` in the +installation with `-c pytorch -c nvidia -c fastai`, since fastchan is +not currently supported on Windows. +Due to python multiprocessing issues on Jupyter and Windows, +`num_workers` of `Dataloader` is reset to 0 automatically to avoid +Jupyter hanging. This makes tasks such as computer vision in Jupyter on +Windows many times slower than on Linux. This limitation doesn’t exist +if you use fastai from a script. +See [this +example](https://github.com/fastai/fastai/blob/master/nbs/examples/dataloader_spawn.py) +to fully leverage the fastai API on Windows. +## Tests +To run the tests in parallel, launch: +`nbdev_test` +For all the tests to pass, you’ll need to install the dependencies +specified as part of dev_requirements in settings.ini +`pip install -e .[dev]` +Tests are written using `nbdev`, for example see the documentation for +`test_eq`. +## Contributing +After you clone this repository, make sure you have run +`nbdev_install_hooks` in your terminal. This install Jupyter and git +hooks to automatically clean, trust, and fix merge conflicts in +notebooks. +After making changes in the repo, you should run `nbdev_prepare` and +make additional and necessary changes in order to pass all the tests. +## Docker Containers +For those interested in official docker containers for this project, +they can be found +[here](https://github.com/fastai/docker-containers#fastai). + +%package -n python3-fastai +Summary: fastai simplifies training fast and accurate neural nets using modern best practices +Provides: python-fastai +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-fastai +<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! --> + +[](https://pypi.org/project/fastai/#description) +[](https://anaconda.org/fastai/fastai) +[](https://github.com/fastai/docker-containers) + +## Installing +You can use fastai without any installation by using [Google +Colab](https://colab.research.google.com/). In fact, every page of this +documentation is also available as an interactive notebook - click “Open +in colab” at the top of any page to open it (be sure to change the Colab +runtime to “GPU” to have it run fast!) See the fast.ai documentation on +[Using Colab](https://course.fast.ai/start_colab) for more information. +You can install fastai on your own machines with conda (highly +recommended), as long as you’re running Linux or Windows (NB: Mac is not +supported). For Windows, please see the “Running on Windows” for +important notes. +If you’re using +[miniconda](https://docs.conda.io/en/latest/miniconda.html) +(recommended) then run (note that if you replace `conda` with +[mamba](https://github.com/mamba-org/mamba) the install process will be +much faster and more reliable): +``` bash +conda install -c fastchan fastai +``` +…or if you’re using +[Anaconda](https://www.anaconda.com/products/individual) then run: +``` bash +conda install -c fastchan fastai anaconda +``` +To install with pip, use: `pip install fastai`. If you install with pip, +you should install PyTorch first by following the PyTorch [installation +instructions](https://pytorch.org/get-started/locally/). +If you plan to develop fastai yourself, or want to be on the cutting +edge, you can use an editable install (if you do this, you should also +use an editable install of +[fastcore](https://github.com/fastai/fastcore) to go with it.) First +install PyTorch, and then: + git clone https://github.com/fastai/fastai + pip install -e "fastai[dev]" +## Learning fastai +The best way to get started with fastai (and deep learning) is to read +[the +book](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527), +and complete [the free course](https://course.fast.ai). +To see what’s possible with fastai, take a look at the [Quick +Start](https://docs.fast.ai/quick_start.html), which shows how to use +around 5 lines of code to build an image classifier, an image +segmentation model, a text sentiment model, a recommendation system, and +a tabular model. For each of the applications, the code is much the +same. +Read through the [Tutorials](https://docs.fast.ai/tutorial.html) to +learn how to train your own models on your own datasets. Use the +navigation sidebar to look through the fastai documentation. Every +class, function, and method is documented here. +To learn about the design and motivation of the library, read the [peer +reviewed paper](https://www.mdpi.com/2078-2489/11/2/108/htm). +## About fastai +fastai is a deep learning library which provides practitioners with +high-level components that can quickly and easily provide +state-of-the-art results in standard deep learning domains, and provides +researchers with low-level components that can be mixed and matched to +build new approaches. It aims to do both things without substantial +compromises in ease of use, flexibility, or performance. This is +possible thanks to a carefully layered architecture, which expresses +common underlying patterns of many deep learning and data processing +techniques in terms of decoupled abstractions. These abstractions can be +expressed concisely and clearly by leveraging the dynamism of the +underlying Python language and the flexibility of the PyTorch library. +fastai includes: +- A new type dispatch system for Python along with a semantic type + hierarchy for tensors +- A GPU-optimized computer vision library which can be extended in pure + Python +- An optimizer which refactors out the common functionality of modern + optimizers into two basic pieces, allowing optimization algorithms to + be implemented in 4–5 lines of code +- A novel 2-way callback system that can access any part of the data, + model, or optimizer and change it at any point during training +- A new data block API +- And much more… +fastai is organized around two main design goals: to be approachable and +rapidly productive, while also being deeply hackable and configurable. +It is built on top of a hierarchy of lower-level APIs which provide +composable building blocks. This way, a user wanting to rewrite part of +the high-level API or add particular behavior to suit their needs does +not have to learn how to use the lowest level. +<img alt="Layered API" src="https://raw.githubusercontent.com/fastai/fastai/master/images/layered.png" width="345"> +## Migrating from other libraries +It’s very easy to migrate from plain PyTorch, Ignite, or any other +PyTorch-based library, or even to use fastai in conjunction with other +libraries. Generally, you’ll be able to use all your existing data +processing code, but will be able to reduce the amount of code you +require for training, and more easily take advantage of modern best +practices. Here are migration guides from some popular libraries to help +you on your way: +- [Plain PyTorch](https://docs.fast.ai/examples/migrating_pytorch.html) +- [Ignite](https://docs.fast.ai/examples/migrating_ignite.html) +- [Lightning](https://docs.fast.ai/examples/migrating_lightning.html) +- [Catalyst](https://docs.fast.ai/examples/migrating_catalyst.html) +## Windows Support +When installing with `mamba` or `conda` replace `-c fastchan` in the +installation with `-c pytorch -c nvidia -c fastai`, since fastchan is +not currently supported on Windows. +Due to python multiprocessing issues on Jupyter and Windows, +`num_workers` of `Dataloader` is reset to 0 automatically to avoid +Jupyter hanging. This makes tasks such as computer vision in Jupyter on +Windows many times slower than on Linux. This limitation doesn’t exist +if you use fastai from a script. +See [this +example](https://github.com/fastai/fastai/blob/master/nbs/examples/dataloader_spawn.py) +to fully leverage the fastai API on Windows. +## Tests +To run the tests in parallel, launch: +`nbdev_test` +For all the tests to pass, you’ll need to install the dependencies +specified as part of dev_requirements in settings.ini +`pip install -e .[dev]` +Tests are written using `nbdev`, for example see the documentation for +`test_eq`. +## Contributing +After you clone this repository, make sure you have run +`nbdev_install_hooks` in your terminal. This install Jupyter and git +hooks to automatically clean, trust, and fix merge conflicts in +notebooks. +After making changes in the repo, you should run `nbdev_prepare` and +make additional and necessary changes in order to pass all the tests. +## Docker Containers +For those interested in official docker containers for this project, +they can be found +[here](https://github.com/fastai/docker-containers#fastai). + +%package help +Summary: Development documents and examples for fastai +Provides: python3-fastai-doc +%description help +<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! --> + +[](https://pypi.org/project/fastai/#description) +[](https://anaconda.org/fastai/fastai) +[](https://github.com/fastai/docker-containers) + +## Installing +You can use fastai without any installation by using [Google +Colab](https://colab.research.google.com/). In fact, every page of this +documentation is also available as an interactive notebook - click “Open +in colab” at the top of any page to open it (be sure to change the Colab +runtime to “GPU” to have it run fast!) See the fast.ai documentation on +[Using Colab](https://course.fast.ai/start_colab) for more information. +You can install fastai on your own machines with conda (highly +recommended), as long as you’re running Linux or Windows (NB: Mac is not +supported). For Windows, please see the “Running on Windows” for +important notes. +If you’re using +[miniconda](https://docs.conda.io/en/latest/miniconda.html) +(recommended) then run (note that if you replace `conda` with +[mamba](https://github.com/mamba-org/mamba) the install process will be +much faster and more reliable): +``` bash +conda install -c fastchan fastai +``` +…or if you’re using +[Anaconda](https://www.anaconda.com/products/individual) then run: +``` bash +conda install -c fastchan fastai anaconda +``` +To install with pip, use: `pip install fastai`. If you install with pip, +you should install PyTorch first by following the PyTorch [installation +instructions](https://pytorch.org/get-started/locally/). +If you plan to develop fastai yourself, or want to be on the cutting +edge, you can use an editable install (if you do this, you should also +use an editable install of +[fastcore](https://github.com/fastai/fastcore) to go with it.) First +install PyTorch, and then: + git clone https://github.com/fastai/fastai + pip install -e "fastai[dev]" +## Learning fastai +The best way to get started with fastai (and deep learning) is to read +[the +book](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527), +and complete [the free course](https://course.fast.ai). +To see what’s possible with fastai, take a look at the [Quick +Start](https://docs.fast.ai/quick_start.html), which shows how to use +around 5 lines of code to build an image classifier, an image +segmentation model, a text sentiment model, a recommendation system, and +a tabular model. For each of the applications, the code is much the +same. +Read through the [Tutorials](https://docs.fast.ai/tutorial.html) to +learn how to train your own models on your own datasets. Use the +navigation sidebar to look through the fastai documentation. Every +class, function, and method is documented here. +To learn about the design and motivation of the library, read the [peer +reviewed paper](https://www.mdpi.com/2078-2489/11/2/108/htm). +## About fastai +fastai is a deep learning library which provides practitioners with +high-level components that can quickly and easily provide +state-of-the-art results in standard deep learning domains, and provides +researchers with low-level components that can be mixed and matched to +build new approaches. It aims to do both things without substantial +compromises in ease of use, flexibility, or performance. This is +possible thanks to a carefully layered architecture, which expresses +common underlying patterns of many deep learning and data processing +techniques in terms of decoupled abstractions. These abstractions can be +expressed concisely and clearly by leveraging the dynamism of the +underlying Python language and the flexibility of the PyTorch library. +fastai includes: +- A new type dispatch system for Python along with a semantic type + hierarchy for tensors +- A GPU-optimized computer vision library which can be extended in pure + Python +- An optimizer which refactors out the common functionality of modern + optimizers into two basic pieces, allowing optimization algorithms to + be implemented in 4–5 lines of code +- A novel 2-way callback system that can access any part of the data, + model, or optimizer and change it at any point during training +- A new data block API +- And much more… +fastai is organized around two main design goals: to be approachable and +rapidly productive, while also being deeply hackable and configurable. +It is built on top of a hierarchy of lower-level APIs which provide +composable building blocks. This way, a user wanting to rewrite part of +the high-level API or add particular behavior to suit their needs does +not have to learn how to use the lowest level. +<img alt="Layered API" src="https://raw.githubusercontent.com/fastai/fastai/master/images/layered.png" width="345"> +## Migrating from other libraries +It’s very easy to migrate from plain PyTorch, Ignite, or any other +PyTorch-based library, or even to use fastai in conjunction with other +libraries. Generally, you’ll be able to use all your existing data +processing code, but will be able to reduce the amount of code you +require for training, and more easily take advantage of modern best +practices. Here are migration guides from some popular libraries to help +you on your way: +- [Plain PyTorch](https://docs.fast.ai/examples/migrating_pytorch.html) +- [Ignite](https://docs.fast.ai/examples/migrating_ignite.html) +- [Lightning](https://docs.fast.ai/examples/migrating_lightning.html) +- [Catalyst](https://docs.fast.ai/examples/migrating_catalyst.html) +## Windows Support +When installing with `mamba` or `conda` replace `-c fastchan` in the +installation with `-c pytorch -c nvidia -c fastai`, since fastchan is +not currently supported on Windows. +Due to python multiprocessing issues on Jupyter and Windows, +`num_workers` of `Dataloader` is reset to 0 automatically to avoid +Jupyter hanging. This makes tasks such as computer vision in Jupyter on +Windows many times slower than on Linux. This limitation doesn’t exist +if you use fastai from a script. +See [this +example](https://github.com/fastai/fastai/blob/master/nbs/examples/dataloader_spawn.py) +to fully leverage the fastai API on Windows. +## Tests +To run the tests in parallel, launch: +`nbdev_test` +For all the tests to pass, you’ll need to install the dependencies +specified as part of dev_requirements in settings.ini +`pip install -e .[dev]` +Tests are written using `nbdev`, for example see the documentation for +`test_eq`. +## Contributing +After you clone this repository, make sure you have run +`nbdev_install_hooks` in your terminal. This install Jupyter and git +hooks to automatically clean, trust, and fix merge conflicts in +notebooks. +After making changes in the repo, you should run `nbdev_prepare` and +make additional and necessary changes in order to pass all the tests. +## Docker Containers +For those interested in official docker containers for this project, +they can be found +[here](https://github.com/fastai/docker-containers#fastai). + +%prep +%autosetup -n fastai-2.7.12 + +%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-fastai -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 2.7.12-1 +- Package Spec generated @@ -0,0 +1 @@ +3d74eea8f24c8589ffed53a82631f963 fastai-2.7.12.tar.gz |