%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