%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 - 2.4.2-1 - Package Spec generated