automatic import of python-mead-baseline

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+/mead-baseline-2.4.2.tar.gz
diff --git a/python-mead-baseline.spec b/python-mead-baseline.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-mead-baseline
+Version:	2.4.2
+Release:	1
+Summary:	Strong Deep-Learning Baseline algorithms for NLP
+License:	Apache 2.0
+URL:		https://www.github.com/dpressel/baseline
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/ef/1e/fd8dd5aaed1874cab96d8073aaff8afbfc563ce76170dd76d89846ddfe32/mead-baseline-2.4.2.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-six
+Requires:	python3-mead-layers
+Requires:	python3-fastBPE
+Requires:	python3-fastBPE
+Requires:	python3-subword-nmt
+Requires:	python3-grpc
+Requires:	python3-onnxruntime
+Requires:	python3-regex
+Requires:	python3-tensorboard
+Requires:	python3-pytest
+Requires:	python3-mock
+Requires:	python3-contextdecorator
+Requires:	python3-pytest-forked
+Requires:	python3-onnxruntime
+Requires:	python3-tensorflow-addons
+Requires:	python3-tfrecord
+Requires:	python3-pyyaml
+
+%description
+# MEAD
+
+MEAD is a library for reproducible deep learning research and fast model
+development for NLP. It provides easily extensible abstractions and
+implementations for data loading, model development, training, experiment tracking and export to production. 
+
+It also provides implementations of high-performance deep learning models for various NLP tasks, against which newly developed models
+can be compared. Deep learning experiments are hard to reproduce, MEAD
+provides functionalities to track them. The goal is to allow a researcher to
+focus on model development, delegating the repetitive tasks to the library.
+
+[Documentation](https://github.com/dpressel/mead-baseline/blob/master/docs/main.md)
+
+[Tutorials using Colab](https://github.com/dpressel/mead-tutorials)
+
+[MEAD Hub](https://github.com/mead-ml/hub)
+
+## Installation
+
+### Pip
+
+Baseline can be installed as a Python package.
+
+`pip install mead-baseline`
+
+You will need to have
+`tensorflow_addons` already installed or have it get installed directly with: 
+
+`pip install mead-baseline[tf2]`
+
+### From the repository
+
+If you have a clone of this repostory and want to install from it:
+
+```
+cd layers
+pip install -e .
+cd ../
+pip install -e .
+```
+
+This first installs `mead-layers` AKA 8 mile, a tiny layers API containing PyTorch and TensorFlow primitives, locally and then `mead-baseline`
+
+### Dockerhub
+
+We use Github CI/CD to automatically release TensorFlow and PyTorch via this project:
+
+https://github.com/mead-ml/mead-gpu
+
+Links to the latest dockerhub images can be found there
+
+## A Note About Versions
+
+Deep Learning Frameworks are evolving quickly and changes are not always
+backwards compatible. We recommend recent versions of whichever framework is being used underneath.  We currently test on TF versions 2.1.0 and 2.4.1.
+The PyTorch backend requires at least version 1.3.0, though we recommend using a more recent version.
+
+## Citing
+
+If you use the library, please cite the following paper:
+
+```
+@InProceedings{W18-2506,
+  author =    "Pressel, Daniel
+               and Ray Choudhury, Sagnik
+               and Lester, Brian
+               and Zhao, Yanjie
+               and Barta, Matt",
+  title =     "Baseline: A Library for Rapid Modeling, Experimentation and
+               Development of Deep Learning Algorithms targeting NLP",
+  booktitle = "Proceedings of Workshop for NLP Open Source Software (NLP-OSS)",
+  year =      "2018",
+  publisher = "Association for Computational Linguistics",
+  pages =     "34--40",
+  location =  "Melbourne, Australia",
+  url =       "http://aclweb.org/anthology/W18-2506"
+}
+```
+
+MEAD was selected for a Spotlight Poster at the NeurIPS MLOSS workshop in 2018.  [OpenReview link](https://openreview.net/forum?id=r1xEb7J15Q)
+
+### Acknowledgements
+
+- Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)
+
+
+
+
+
+%package -n python3-mead-baseline
+Summary:	Strong Deep-Learning Baseline algorithms for NLP
+Provides:	python-mead-baseline
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-mead-baseline
+# MEAD
+
+MEAD is a library for reproducible deep learning research and fast model
+development for NLP. It provides easily extensible abstractions and
+implementations for data loading, model development, training, experiment tracking and export to production. 
+
+It also provides implementations of high-performance deep learning models for various NLP tasks, against which newly developed models
+can be compared. Deep learning experiments are hard to reproduce, MEAD
+provides functionalities to track them. The goal is to allow a researcher to
+focus on model development, delegating the repetitive tasks to the library.
+
+[Documentation](https://github.com/dpressel/mead-baseline/blob/master/docs/main.md)
+
+[Tutorials using Colab](https://github.com/dpressel/mead-tutorials)
+
+[MEAD Hub](https://github.com/mead-ml/hub)
+
+## Installation
+
+### Pip
+
+Baseline can be installed as a Python package.
+
+`pip install mead-baseline`
+
+You will need to have
+`tensorflow_addons` already installed or have it get installed directly with: 
+
+`pip install mead-baseline[tf2]`
+
+### From the repository
+
+If you have a clone of this repostory and want to install from it:
+
+```
+cd layers
+pip install -e .
+cd ../
+pip install -e .
+```
+
+This first installs `mead-layers` AKA 8 mile, a tiny layers API containing PyTorch and TensorFlow primitives, locally and then `mead-baseline`
+
+### Dockerhub
+
+We use Github CI/CD to automatically release TensorFlow and PyTorch via this project:
+
+https://github.com/mead-ml/mead-gpu
+
+Links to the latest dockerhub images can be found there
+
+## A Note About Versions
+
+Deep Learning Frameworks are evolving quickly and changes are not always
+backwards compatible. We recommend recent versions of whichever framework is being used underneath.  We currently test on TF versions 2.1.0 and 2.4.1.
+The PyTorch backend requires at least version 1.3.0, though we recommend using a more recent version.
+
+## Citing
+
+If you use the library, please cite the following paper:
+
+```
+@InProceedings{W18-2506,
+  author =    "Pressel, Daniel
+               and Ray Choudhury, Sagnik
+               and Lester, Brian
+               and Zhao, Yanjie
+               and Barta, Matt",
+  title =     "Baseline: A Library for Rapid Modeling, Experimentation and
+               Development of Deep Learning Algorithms targeting NLP",
+  booktitle = "Proceedings of Workshop for NLP Open Source Software (NLP-OSS)",
+  year =      "2018",
+  publisher = "Association for Computational Linguistics",
+  pages =     "34--40",
+  location =  "Melbourne, Australia",
+  url =       "http://aclweb.org/anthology/W18-2506"
+}
+```
+
+MEAD was selected for a Spotlight Poster at the NeurIPS MLOSS workshop in 2018.  [OpenReview link](https://openreview.net/forum?id=r1xEb7J15Q)
+
+### Acknowledgements
+
+- Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)
+
+
+
+
+
+%package help
+Summary:	Development documents and examples for mead-baseline
+Provides:	python3-mead-baseline-doc
+%description help
+# MEAD
+
+MEAD is a library for reproducible deep learning research and fast model
+development for NLP. It provides easily extensible abstractions and
+implementations for data loading, model development, training, experiment tracking and export to production. 
+
+It also provides implementations of high-performance deep learning models for various NLP tasks, against which newly developed models
+can be compared. Deep learning experiments are hard to reproduce, MEAD
+provides functionalities to track them. The goal is to allow a researcher to
+focus on model development, delegating the repetitive tasks to the library.
+
+[Documentation](https://github.com/dpressel/mead-baseline/blob/master/docs/main.md)
+
+[Tutorials using Colab](https://github.com/dpressel/mead-tutorials)
+
+[MEAD Hub](https://github.com/mead-ml/hub)
+
+## Installation
+
+### Pip
+
+Baseline can be installed as a Python package.
+
+`pip install mead-baseline`
+
+You will need to have
+`tensorflow_addons` already installed or have it get installed directly with: 
+
+`pip install mead-baseline[tf2]`
+
+### From the repository
+
+If you have a clone of this repostory and want to install from it:
+
+```
+cd layers
+pip install -e .
+cd ../
+pip install -e .
+```
+
+This first installs `mead-layers` AKA 8 mile, a tiny layers API containing PyTorch and TensorFlow primitives, locally and then `mead-baseline`
+
+### Dockerhub
+
+We use Github CI/CD to automatically release TensorFlow and PyTorch via this project:
+
+https://github.com/mead-ml/mead-gpu
+
+Links to the latest dockerhub images can be found there
+
+## A Note About Versions
+
+Deep Learning Frameworks are evolving quickly and changes are not always
+backwards compatible. We recommend recent versions of whichever framework is being used underneath.  We currently test on TF versions 2.1.0 and 2.4.1.
+The PyTorch backend requires at least version 1.3.0, though we recommend using a more recent version.
+
+## Citing
+
+If you use the library, please cite the following paper:
+
+```
+@InProceedings{W18-2506,
+  author =    "Pressel, Daniel
+               and Ray Choudhury, Sagnik
+               and Lester, Brian
+               and Zhao, Yanjie
+               and Barta, Matt",
+  title =     "Baseline: A Library for Rapid Modeling, Experimentation and
+               Development of Deep Learning Algorithms targeting NLP",
+  booktitle = "Proceedings of Workshop for NLP Open Source Software (NLP-OSS)",
+  year =      "2018",
+  publisher = "Association for Computational Linguistics",
+  pages =     "34--40",
+  location =  "Melbourne, Australia",
+  url =       "http://aclweb.org/anthology/W18-2506"
+}
+```
+
+MEAD was selected for a Spotlight Poster at the NeurIPS MLOSS workshop in 2018.  [OpenReview link](https://openreview.net/forum?id=r1xEb7J15Q)
+
+### Acknowledgements
+
+- Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)
+
+
+
+
+
+%prep
+%autosetup -n mead-baseline-2.4.2
+
+%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-mead-baseline -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 2.4.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..5ca59d4
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+9a4eb80678e3059165211d342a7d528e  mead-baseline-2.4.2.tar.gz