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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.aliyun.com/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
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 2.4.2-1
- Package Spec generated
|
