From e020d3fdf9e822170ea24763466df30ebe27cbd1 Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Tue, 11 Apr 2023 12:38:46 +0000 Subject: automatic import of python-tensorpack --- python-tensorpack.spec | 369 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 369 insertions(+) create mode 100644 python-tensorpack.spec (limited to 'python-tensorpack.spec') diff --git a/python-tensorpack.spec b/python-tensorpack.spec new file mode 100644 index 0000000..202a5d7 --- /dev/null +++ b/python-tensorpack.spec @@ -0,0 +1,369 @@ +%global _empty_manifest_terminate_build 0 +Name: python-tensorpack +Version: 0.11 +Release: 1 +Summary: A Neural Network Training Interface on TensorFlow +License: Apache +URL: https://github.com/tensorpack/tensorpack +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d2/f0/edfda47ca6cc9ece30a893362c336b9121b691529e4cdf3b8732565be790/tensorpack-0.11.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-six +Requires: python3-termcolor +Requires: python3-tabulate +Requires: python3-tqdm +Requires: python3-msgpack +Requires: python3-msgpack-numpy +Requires: python3-pyzmq +Requires: python3-psutil +Requires: python3-scipy +Requires: python3-h5py +Requires: python3-lmdb +Requires: python3-matplotlib +Requires: python3-scikit-learn +Requires: python3-prctl + +%description +![Tensorpack](https://github.com/tensorpack/tensorpack/raw/master/.github/tensorpack.png) + +Tensorpack is a neural network training interface based on TensorFlow. + +[![ReadTheDoc](https://readthedocs.org/projects/tensorpack/badge/?version=latest)](http://tensorpack.readthedocs.io) +[![Gitter chat](https://img.shields.io/badge/chat-on%20gitter-46bc99.svg)](https://gitter.im/tensorpack/users) +[![model-zoo](https://img.shields.io/badge/model-zoo-brightgreen.svg)](http://models.tensorpack.com) +## Features: + +It's Yet Another TF high-level API, with __speed__, and __flexibility__ built together. + +1. Focus on __training speed__. + + Speed comes for free with Tensorpack -- it uses TensorFlow in the __efficient way__ with no extra overhead. + On common CNNs, it runs training [1.2~5x faster](https://github.com/tensorpack/benchmarks/tree/master/other-wrappers) than the equivalent Keras code. + Your training can probably gets faster if written with Tensorpack. + + + Data-parallel multi-GPU/distributed training strategy is off-the-shelf to use. + It scales as well as Google's [official benchmark](https://www.tensorflow.org/performance/benchmarks). + + + See [tensorpack/benchmarks](https://github.com/tensorpack/benchmarks) for + some benchmark scripts. + +2. Focus on __large datasets__. + + [You don't usually need `tf.data`](https://tensorpack.readthedocs.io/tutorial/philosophy/dataflow.html#alternative-data-loading-solutions). + Symbolic programming often makes data processing harder. + Tensorpack helps you efficiently process large datasets (e.g. ImageNet) in __pure Python__ with autoparallelization. + +3. It's not a model wrapper. + + There are too many symbolic function wrappers in the world. Tensorpack includes only a few common models. + But you can use any symbolic function library inside Tensorpack, including tf.layers/Keras/slim/tflearn/tensorlayer/.... + +See [tutorials and documentations](http://tensorpack.readthedocs.io/tutorial/index.html#user-tutorials) to know more about these features. + +## Examples: + +We refuse toy examples. +Instead of showing tiny CNNs trained on MNIST/Cifar10, +we provide training scripts that reproduce well-known papers. + +We refuse low-quality implementations. +Unlike most open source repos which only __implement__ papers, +[Tensorpack examples](examples) faithfully __reproduce__ papers, +demonstrating its __flexibility__ for actual research. + +### Vision: ++ [Train ResNet](examples/ResNet) and [other models](examples/ImageNetModels) on ImageNet ++ [Train Mask/Faster R-CNN on COCO object detection](examples/FasterRCNN) ++ [Unsupervised learning with Momentum Contrast](https://github.com/ppwwyyxx/moco.tensorflow) (MoCo) ++ [Generative Adversarial Network(GAN) variants](examples/GAN), including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN ++ [DoReFa-Net: train binary / low-bitwidth CNN on ImageNet](examples/DoReFa-Net) ++ [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](examples/HED) ++ [Spatial Transformer Networks on MNIST addition](examples/SpatialTransformer) ++ [Visualize CNN saliency maps](examples/Saliency) ++ [Similarity learning on MNIST](examples/SimilarityLearning) + +### Reinforcement Learning: ++ [Deep Q-Network(DQN) variants on Atari games](examples/DeepQNetwork), including DQN, DoubleDQN, DuelingDQN. ++ [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](examples/A3C-Gym) + +### Speech / NLP: ++ [LSTM-CTC for speech recognition](examples/CTC-TIMIT) ++ [char-rnn for fun](examples/Char-RNN) ++ [LSTM language model on PennTreebank](examples/PennTreebank) + +## Install: + +Dependencies: + ++ Python 3.3+. ++ Python bindings for OpenCV. (Optional, but required by a lot of features) ++ TensorFlow ≥ 1.5, < 2 + * TF is not not required if you only want to use `tensorpack.dataflow` alone as a data processing library + * TF2 is supported if used in graph mode (and use `tf.compat.v1` when needed) +``` +pip install --upgrade git+https://github.com/tensorpack/tensorpack.git +# or add `--user` to install to user's local directories +``` + +Please note that tensorpack is not yet stable. +If you use tensorpack in your code, remember to mark the exact version of tensorpack you use as your dependencies. + +## Citing Tensorpack: + +If you use Tensorpack in your research or wish to refer to the examples, please cite with: +``` +@misc{wu2016tensorpack, + title={Tensorpack}, + author={Wu, Yuxin and others}, + howpublished={\url{https://github.com/tensorpack/}}, + year={2016} +} +``` + + + + +%package -n python3-tensorpack +Summary: A Neural Network Training Interface on TensorFlow +Provides: python-tensorpack +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-tensorpack +![Tensorpack](https://github.com/tensorpack/tensorpack/raw/master/.github/tensorpack.png) + +Tensorpack is a neural network training interface based on TensorFlow. + +[![ReadTheDoc](https://readthedocs.org/projects/tensorpack/badge/?version=latest)](http://tensorpack.readthedocs.io) +[![Gitter chat](https://img.shields.io/badge/chat-on%20gitter-46bc99.svg)](https://gitter.im/tensorpack/users) +[![model-zoo](https://img.shields.io/badge/model-zoo-brightgreen.svg)](http://models.tensorpack.com) +## Features: + +It's Yet Another TF high-level API, with __speed__, and __flexibility__ built together. + +1. Focus on __training speed__. + + Speed comes for free with Tensorpack -- it uses TensorFlow in the __efficient way__ with no extra overhead. + On common CNNs, it runs training [1.2~5x faster](https://github.com/tensorpack/benchmarks/tree/master/other-wrappers) than the equivalent Keras code. + Your training can probably gets faster if written with Tensorpack. + + + Data-parallel multi-GPU/distributed training strategy is off-the-shelf to use. + It scales as well as Google's [official benchmark](https://www.tensorflow.org/performance/benchmarks). + + + See [tensorpack/benchmarks](https://github.com/tensorpack/benchmarks) for + some benchmark scripts. + +2. Focus on __large datasets__. + + [You don't usually need `tf.data`](https://tensorpack.readthedocs.io/tutorial/philosophy/dataflow.html#alternative-data-loading-solutions). + Symbolic programming often makes data processing harder. + Tensorpack helps you efficiently process large datasets (e.g. ImageNet) in __pure Python__ with autoparallelization. + +3. It's not a model wrapper. + + There are too many symbolic function wrappers in the world. Tensorpack includes only a few common models. + But you can use any symbolic function library inside Tensorpack, including tf.layers/Keras/slim/tflearn/tensorlayer/.... + +See [tutorials and documentations](http://tensorpack.readthedocs.io/tutorial/index.html#user-tutorials) to know more about these features. + +## Examples: + +We refuse toy examples. +Instead of showing tiny CNNs trained on MNIST/Cifar10, +we provide training scripts that reproduce well-known papers. + +We refuse low-quality implementations. +Unlike most open source repos which only __implement__ papers, +[Tensorpack examples](examples) faithfully __reproduce__ papers, +demonstrating its __flexibility__ for actual research. + +### Vision: ++ [Train ResNet](examples/ResNet) and [other models](examples/ImageNetModels) on ImageNet ++ [Train Mask/Faster R-CNN on COCO object detection](examples/FasterRCNN) ++ [Unsupervised learning with Momentum Contrast](https://github.com/ppwwyyxx/moco.tensorflow) (MoCo) ++ [Generative Adversarial Network(GAN) variants](examples/GAN), including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN ++ [DoReFa-Net: train binary / low-bitwidth CNN on ImageNet](examples/DoReFa-Net) ++ [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](examples/HED) ++ [Spatial Transformer Networks on MNIST addition](examples/SpatialTransformer) ++ [Visualize CNN saliency maps](examples/Saliency) ++ [Similarity learning on MNIST](examples/SimilarityLearning) + +### Reinforcement Learning: ++ [Deep Q-Network(DQN) variants on Atari games](examples/DeepQNetwork), including DQN, DoubleDQN, DuelingDQN. ++ [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](examples/A3C-Gym) + +### Speech / NLP: ++ [LSTM-CTC for speech recognition](examples/CTC-TIMIT) ++ [char-rnn for fun](examples/Char-RNN) ++ [LSTM language model on PennTreebank](examples/PennTreebank) + +## Install: + +Dependencies: + ++ Python 3.3+. ++ Python bindings for OpenCV. (Optional, but required by a lot of features) ++ TensorFlow ≥ 1.5, < 2 + * TF is not not required if you only want to use `tensorpack.dataflow` alone as a data processing library + * TF2 is supported if used in graph mode (and use `tf.compat.v1` when needed) +``` +pip install --upgrade git+https://github.com/tensorpack/tensorpack.git +# or add `--user` to install to user's local directories +``` + +Please note that tensorpack is not yet stable. +If you use tensorpack in your code, remember to mark the exact version of tensorpack you use as your dependencies. + +## Citing Tensorpack: + +If you use Tensorpack in your research or wish to refer to the examples, please cite with: +``` +@misc{wu2016tensorpack, + title={Tensorpack}, + author={Wu, Yuxin and others}, + howpublished={\url{https://github.com/tensorpack/}}, + year={2016} +} +``` + + + + +%package help +Summary: Development documents and examples for tensorpack +Provides: python3-tensorpack-doc +%description help +![Tensorpack](https://github.com/tensorpack/tensorpack/raw/master/.github/tensorpack.png) + +Tensorpack is a neural network training interface based on TensorFlow. + +[![ReadTheDoc](https://readthedocs.org/projects/tensorpack/badge/?version=latest)](http://tensorpack.readthedocs.io) +[![Gitter chat](https://img.shields.io/badge/chat-on%20gitter-46bc99.svg)](https://gitter.im/tensorpack/users) +[![model-zoo](https://img.shields.io/badge/model-zoo-brightgreen.svg)](http://models.tensorpack.com) +## Features: + +It's Yet Another TF high-level API, with __speed__, and __flexibility__ built together. + +1. Focus on __training speed__. + + Speed comes for free with Tensorpack -- it uses TensorFlow in the __efficient way__ with no extra overhead. + On common CNNs, it runs training [1.2~5x faster](https://github.com/tensorpack/benchmarks/tree/master/other-wrappers) than the equivalent Keras code. + Your training can probably gets faster if written with Tensorpack. + + + Data-parallel multi-GPU/distributed training strategy is off-the-shelf to use. + It scales as well as Google's [official benchmark](https://www.tensorflow.org/performance/benchmarks). + + + See [tensorpack/benchmarks](https://github.com/tensorpack/benchmarks) for + some benchmark scripts. + +2. Focus on __large datasets__. + + [You don't usually need `tf.data`](https://tensorpack.readthedocs.io/tutorial/philosophy/dataflow.html#alternative-data-loading-solutions). + Symbolic programming often makes data processing harder. + Tensorpack helps you efficiently process large datasets (e.g. ImageNet) in __pure Python__ with autoparallelization. + +3. It's not a model wrapper. + + There are too many symbolic function wrappers in the world. Tensorpack includes only a few common models. + But you can use any symbolic function library inside Tensorpack, including tf.layers/Keras/slim/tflearn/tensorlayer/.... + +See [tutorials and documentations](http://tensorpack.readthedocs.io/tutorial/index.html#user-tutorials) to know more about these features. + +## Examples: + +We refuse toy examples. +Instead of showing tiny CNNs trained on MNIST/Cifar10, +we provide training scripts that reproduce well-known papers. + +We refuse low-quality implementations. +Unlike most open source repos which only __implement__ papers, +[Tensorpack examples](examples) faithfully __reproduce__ papers, +demonstrating its __flexibility__ for actual research. + +### Vision: ++ [Train ResNet](examples/ResNet) and [other models](examples/ImageNetModels) on ImageNet ++ [Train Mask/Faster R-CNN on COCO object detection](examples/FasterRCNN) ++ [Unsupervised learning with Momentum Contrast](https://github.com/ppwwyyxx/moco.tensorflow) (MoCo) ++ [Generative Adversarial Network(GAN) variants](examples/GAN), including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN ++ [DoReFa-Net: train binary / low-bitwidth CNN on ImageNet](examples/DoReFa-Net) ++ [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](examples/HED) ++ [Spatial Transformer Networks on MNIST addition](examples/SpatialTransformer) ++ [Visualize CNN saliency maps](examples/Saliency) ++ [Similarity learning on MNIST](examples/SimilarityLearning) + +### Reinforcement Learning: ++ [Deep Q-Network(DQN) variants on Atari games](examples/DeepQNetwork), including DQN, DoubleDQN, DuelingDQN. ++ [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](examples/A3C-Gym) + +### Speech / NLP: ++ [LSTM-CTC for speech recognition](examples/CTC-TIMIT) ++ [char-rnn for fun](examples/Char-RNN) ++ [LSTM language model on PennTreebank](examples/PennTreebank) + +## Install: + +Dependencies: + ++ Python 3.3+. ++ Python bindings for OpenCV. (Optional, but required by a lot of features) ++ TensorFlow ≥ 1.5, < 2 + * TF is not not required if you only want to use `tensorpack.dataflow` alone as a data processing library + * TF2 is supported if used in graph mode (and use `tf.compat.v1` when needed) +``` +pip install --upgrade git+https://github.com/tensorpack/tensorpack.git +# or add `--user` to install to user's local directories +``` + +Please note that tensorpack is not yet stable. +If you use tensorpack in your code, remember to mark the exact version of tensorpack you use as your dependencies. + +## Citing Tensorpack: + +If you use Tensorpack in your research or wish to refer to the examples, please cite with: +``` +@misc{wu2016tensorpack, + title={Tensorpack}, + author={Wu, Yuxin and others}, + howpublished={\url{https://github.com/tensorpack/}}, + year={2016} +} +``` + + + + +%prep +%autosetup -n tensorpack-0.11 + +%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-tensorpack -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot - 0.11-1 +- Package Spec generated -- cgit v1.2.3