%global _empty_manifest_terminate_build 0 Name: python-livelossplot Version: 0.5.5 Release: 1 Summary: Live training loss plot in Jupyter Notebook for Keras, PyTorch and others. License: MIT URL: https://github.com/stared/livelossplot Source0: https://mirrors.nju.edu.cn/pypi/web/packages/13/e9/7a1bdea97b97a321844f38d707fe3e786d598215e6dc06e5a024f8494fe0/livelossplot-0.5.5.tar.gz BuildArch: noarch Requires: python3-matplotlib Requires: python3-bokeh Requires: python3-ipython Requires: python3-numpy %description # livelossplot [![livelossplot version - PyPI](https://img.shields.io/pypi/v/livelossplot)](https://pypi.org/project/livelossplot/) ![PyPI status](https://img.shields.io/pypi/status/livelossplot.svg) ![MIT license - PyPI](https://img.shields.io/pypi/l/livelossplot.svg) ![Python version - PyPI](https://img.shields.io/pypi/pyversions/livelossplot.svg) [![GitHub Workflow Status](https://img.shields.io/github/workflow/status/stared/livelossplot/Python%20package)](https://github.com/stared/livelossplot/actions) [![Downloads](http://pepy.tech/badge/livelossplot)](https://pepy.tech/project/livelossplot) [![Twitter @pmigdal](https://img.shields.io/twitter/follow/pmigdal)](https://twitter.com/pmigdal) Don't train deep learning models blindfolded! Be impatient and look at each epoch of your training! ([RECENT CHANGES](CHANGELOG.md), [EXAMPLES IN COLAB](https://colab.research.google.com/github/stared/livelossplot), [API LOOKUP](http://p.migdal.pl/livelossplot/), [CODE](https://github.com/stared/livelossplot)) A live training loss plot in [Jupyter Notebook](http://jupyter.org/) for [Keras](https://keras.io/), [PyTorch](http://pytorch.org/) and other frameworks. An open-source Python package by [Piotr Migdał](https://p.migdal.pl/), [Bartłomiej Olechno](https://github.com/Bartolo1024/) and [others](https://github.com/stared/livelossplot/graphs/contributors). **Open for collaboration!** (Some tasks are as simple as writing code docstrings, so - no excuses! :)) ```python from livelossplot import PlotLossesKeras model.fit(X_train, Y_train, epochs=10, validation_data=(X_test, Y_test), callbacks=[PlotLossesKeras()], verbose=0) ``` ![Animated fig for livelossplot tracking log-loss and accuracy](https://raw.githubusercontent.com/stared/livelossplot/master/livelossplot.gif) - (The most FA)Q: Why not TensorBoard? - A: Jupyter Notebook compatibility (for exploration and teaching). The simplicity of use. ## Installation To install [this version from PyPI](https://pypi.org/project/livelossplot/), type: ```bash pip install livelossplot ``` To get the newest one from this repo (note that we are in the alpha stage, so there may be frequent updates), type: ```bash pip install git+git://github.com/stared/livelossplot.git ``` ## Examples Look at notebook files with full working [examples](https://github.com/stared/livelossplot/blob/master/examples/): - [keras.ipynb](https://github.com/stared/livelossplot/blob/master/examples/keras.ipynb) - a Keras callback - [minimal.ipynb](https://github.com/stared/livelossplot/blob/master/examples/minimal.ipynb) - a bare API, to use anywhere - [bokeh.ipynb](https://github.com/stared/livelossplot/blob/master/examples/bokeh.ipynb) - a bare API, plots with Bokeh ([open it in Colab to see the plots](https://colab.research.google.com/github/stared/livelossplot/blob/master/examples/bokeh.ipynb)) - [pytorch.ipynb](https://github.com/stared/livelossplot/blob/master/examples/pytorch.ipynb) - a bare API, as applied to PyTorch - [2d_prediction_maps.ipynb](https://github.com/stared/livelossplot/blob/master/examples/2d_prediction_maps.ipynb) - example of custom plots - 2d prediction maps (0.4.1+) - [poutyne.ipynb](https://github.com/stared/livelossplot/blob/master/examples/poutyne.ipynb) - a Poutyne callback ([Poutyne](https://poutyne.org/) is a Keras-like framework for PyTorch) - [torchbearer.ipynb](https://github.com/stared/livelossplot/blob/master/examples/torchbearer.ipynb) - an example using the built in functionality from torchbearer ([torchbearer](https://github.com/ecs-vlc/torchbearer) is a model fitting library for PyTorch) - [neptune.py](https://github.com/stared/livelossplot/blob/master/examples/neptune.py) and [neptune.ipynb](https://github.com/stared/livelossplot/blob/master/examples/neptune.ipynb) - a [Neptune.AI](https://neptune.ai/) - [matplotlib.ipynb](https://github.com/stared/livelossplot/blob/master/examples/matplotlib.ipynb) - a Matplotlib output example - [various_options.ipynb](https://github.com/stared/livelossplot/blob/master/examples/various_options.ipynb) - an extended API for metrics grouping and custom outputs You [run examples in Colab](https://colab.research.google.com/github/stared/livelossplot). ## Overview Text logs are easy, but it's easy to miss the most crucial information: is it learning, doing nothing or overfitting? Visual feedback allows us to keep track of the training process. Now there is one for Jupyter. If you want to get serious - use [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard), . But what if you just want to train a small model in Jupyter Notebook? Here is a way to do so, using `livelossplot` as a plug&play component ### from livelossplot import ... `PlotLosses` for a generic API. ```{python} plotlosses = PlotLosses() plotlosses.update({'acc': 0.7, 'val_acc': 0.4, 'loss': 0.9, 'val_loss': 1.1}) plot.send() # draw, update logs, etc ``` There are callbacks for common libraries and frameworks: `PlotLossesKeras`, `PlotLossesKerasTF`, `PlotLossesPoutyne`, `PlotLossesIgnite`. Feel invited to write, and contribute, your adapter. If you want to use a bare logger, there is `MainLogger`. ### from livelossplot.outputs import ... Plots: `MatplotlibPlot`, `BokehPlot`. Loggers: `ExtremaPrinter` (to standard output), `TensorboardLogger`, `TensorboardTFLogger`, `NeptuneLogger`. To use them, initialize PlotLosses with some outputs: ```{python} plotlosses = PlotLosses(outputs=[MatplotlibPlot(), TensorboardLogger()]) ``` There are custom `matplotlib` plots in `livelossplot.outputs.matplotlib_subplots` you can pass in `MatplotlibPlot` arguments. If you like to plot with [Bokeh](https://docs.bokeh.org/en/latest/) instead of [matplotlib](https://matplotlib.org/), use ```{python} plotlosses = PlotLosses(outputs=[BokehPlot()]) ``` ## Sponsors This project supported by [Jacek Migdał](http://jacek.migdal.pl/), [Marek Cichy](https://medium.com/@marekkcichy/), [Casper da Costa-Luis](https://cdcl.ml/), and [Piotr Zientara](https://twitter.com/piotr_zientara). [Join the sponsors - show your ❤️ and support, and appear on the list](https://github.com/sponsors/stared)! It will give me time and energy to work on this project. This project is also supported by a European program *Program Operacyjny Inteligentny Rozwój* for [GearShift - building the engine of behavior of wheeled motor vehicles and map’s generation based on artificial intelligence algorithms implemented on the Unreal Engine platform](https://mapadotacji.gov.pl/projekty/874596/?lang=en) lead by ECC Games (NCBR grant GameINN). ## Trivia It started as [this gist](https://gist.github.com/stared/dfb4dfaf6d9a8501cd1cc8b8cb806d2e). Since it went popular, I decided to rewrite it as a package. Oh, and I am in general interested in data vis, see [Simple diagrams of convoluted neural networks](https://medium.com/inbrowserai/simple-diagrams-of-convoluted-neural-networks-39c097d2925b) (and overview of deep learning architecture diagrams): > A good diagram is worth a thousand equations — let’s create more of these! ...or [my other data vis projects](https://p.migdal.pl/projects/). ## Todo If you want more functionality - open an [Issue](https://github.com/stared/livelossplot/issues) or even better - prepare a [Pull Request](https://github.com/stared/livelossplot/pulls). %package -n python3-livelossplot Summary: Live training loss plot in Jupyter Notebook for Keras, PyTorch and others. Provides: python-livelossplot BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-livelossplot # livelossplot [![livelossplot version - PyPI](https://img.shields.io/pypi/v/livelossplot)](https://pypi.org/project/livelossplot/) ![PyPI status](https://img.shields.io/pypi/status/livelossplot.svg) ![MIT license - PyPI](https://img.shields.io/pypi/l/livelossplot.svg) ![Python version - PyPI](https://img.shields.io/pypi/pyversions/livelossplot.svg) [![GitHub Workflow Status](https://img.shields.io/github/workflow/status/stared/livelossplot/Python%20package)](https://github.com/stared/livelossplot/actions) [![Downloads](http://pepy.tech/badge/livelossplot)](https://pepy.tech/project/livelossplot) [![Twitter @pmigdal](https://img.shields.io/twitter/follow/pmigdal)](https://twitter.com/pmigdal) Don't train deep learning models blindfolded! Be impatient and look at each epoch of your training! ([RECENT CHANGES](CHANGELOG.md), [EXAMPLES IN COLAB](https://colab.research.google.com/github/stared/livelossplot), [API LOOKUP](http://p.migdal.pl/livelossplot/), [CODE](https://github.com/stared/livelossplot)) A live training loss plot in [Jupyter Notebook](http://jupyter.org/) for [Keras](https://keras.io/), [PyTorch](http://pytorch.org/) and other frameworks. An open-source Python package by [Piotr Migdał](https://p.migdal.pl/), [Bartłomiej Olechno](https://github.com/Bartolo1024/) and [others](https://github.com/stared/livelossplot/graphs/contributors). **Open for collaboration!** (Some tasks are as simple as writing code docstrings, so - no excuses! :)) ```python from livelossplot import PlotLossesKeras model.fit(X_train, Y_train, epochs=10, validation_data=(X_test, Y_test), callbacks=[PlotLossesKeras()], verbose=0) ``` ![Animated fig for livelossplot tracking log-loss and accuracy](https://raw.githubusercontent.com/stared/livelossplot/master/livelossplot.gif) - (The most FA)Q: Why not TensorBoard? - A: Jupyter Notebook compatibility (for exploration and teaching). The simplicity of use. ## Installation To install [this version from PyPI](https://pypi.org/project/livelossplot/), type: ```bash pip install livelossplot ``` To get the newest one from this repo (note that we are in the alpha stage, so there may be frequent updates), type: ```bash pip install git+git://github.com/stared/livelossplot.git ``` ## Examples Look at notebook files with full working [examples](https://github.com/stared/livelossplot/blob/master/examples/): - [keras.ipynb](https://github.com/stared/livelossplot/blob/master/examples/keras.ipynb) - a Keras callback - [minimal.ipynb](https://github.com/stared/livelossplot/blob/master/examples/minimal.ipynb) - a bare API, to use anywhere - [bokeh.ipynb](https://github.com/stared/livelossplot/blob/master/examples/bokeh.ipynb) - a bare API, plots with Bokeh ([open it in Colab to see the plots](https://colab.research.google.com/github/stared/livelossplot/blob/master/examples/bokeh.ipynb)) - [pytorch.ipynb](https://github.com/stared/livelossplot/blob/master/examples/pytorch.ipynb) - a bare API, as applied to PyTorch - [2d_prediction_maps.ipynb](https://github.com/stared/livelossplot/blob/master/examples/2d_prediction_maps.ipynb) - example of custom plots - 2d prediction maps (0.4.1+) - [poutyne.ipynb](https://github.com/stared/livelossplot/blob/master/examples/poutyne.ipynb) - a Poutyne callback ([Poutyne](https://poutyne.org/) is a Keras-like framework for PyTorch) - [torchbearer.ipynb](https://github.com/stared/livelossplot/blob/master/examples/torchbearer.ipynb) - an example using the built in functionality from torchbearer ([torchbearer](https://github.com/ecs-vlc/torchbearer) is a model fitting library for PyTorch) - [neptune.py](https://github.com/stared/livelossplot/blob/master/examples/neptune.py) and [neptune.ipynb](https://github.com/stared/livelossplot/blob/master/examples/neptune.ipynb) - a [Neptune.AI](https://neptune.ai/) - [matplotlib.ipynb](https://github.com/stared/livelossplot/blob/master/examples/matplotlib.ipynb) - a Matplotlib output example - [various_options.ipynb](https://github.com/stared/livelossplot/blob/master/examples/various_options.ipynb) - an extended API for metrics grouping and custom outputs You [run examples in Colab](https://colab.research.google.com/github/stared/livelossplot). ## Overview Text logs are easy, but it's easy to miss the most crucial information: is it learning, doing nothing or overfitting? Visual feedback allows us to keep track of the training process. Now there is one for Jupyter. If you want to get serious - use [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard), . But what if you just want to train a small model in Jupyter Notebook? Here is a way to do so, using `livelossplot` as a plug&play component ### from livelossplot import ... `PlotLosses` for a generic API. ```{python} plotlosses = PlotLosses() plotlosses.update({'acc': 0.7, 'val_acc': 0.4, 'loss': 0.9, 'val_loss': 1.1}) plot.send() # draw, update logs, etc ``` There are callbacks for common libraries and frameworks: `PlotLossesKeras`, `PlotLossesKerasTF`, `PlotLossesPoutyne`, `PlotLossesIgnite`. Feel invited to write, and contribute, your adapter. If you want to use a bare logger, there is `MainLogger`. ### from livelossplot.outputs import ... Plots: `MatplotlibPlot`, `BokehPlot`. Loggers: `ExtremaPrinter` (to standard output), `TensorboardLogger`, `TensorboardTFLogger`, `NeptuneLogger`. To use them, initialize PlotLosses with some outputs: ```{python} plotlosses = PlotLosses(outputs=[MatplotlibPlot(), TensorboardLogger()]) ``` There are custom `matplotlib` plots in `livelossplot.outputs.matplotlib_subplots` you can pass in `MatplotlibPlot` arguments. If you like to plot with [Bokeh](https://docs.bokeh.org/en/latest/) instead of [matplotlib](https://matplotlib.org/), use ```{python} plotlosses = PlotLosses(outputs=[BokehPlot()]) ``` ## Sponsors This project supported by [Jacek Migdał](http://jacek.migdal.pl/), [Marek Cichy](https://medium.com/@marekkcichy/), [Casper da Costa-Luis](https://cdcl.ml/), and [Piotr Zientara](https://twitter.com/piotr_zientara). [Join the sponsors - show your ❤️ and support, and appear on the list](https://github.com/sponsors/stared)! It will give me time and energy to work on this project. This project is also supported by a European program *Program Operacyjny Inteligentny Rozwój* for [GearShift - building the engine of behavior of wheeled motor vehicles and map’s generation based on artificial intelligence algorithms implemented on the Unreal Engine platform](https://mapadotacji.gov.pl/projekty/874596/?lang=en) lead by ECC Games (NCBR grant GameINN). ## Trivia It started as [this gist](https://gist.github.com/stared/dfb4dfaf6d9a8501cd1cc8b8cb806d2e). Since it went popular, I decided to rewrite it as a package. Oh, and I am in general interested in data vis, see [Simple diagrams of convoluted neural networks](https://medium.com/inbrowserai/simple-diagrams-of-convoluted-neural-networks-39c097d2925b) (and overview of deep learning architecture diagrams): > A good diagram is worth a thousand equations — let’s create more of these! ...or [my other data vis projects](https://p.migdal.pl/projects/). ## Todo If you want more functionality - open an [Issue](https://github.com/stared/livelossplot/issues) or even better - prepare a [Pull Request](https://github.com/stared/livelossplot/pulls). %package help Summary: Development documents and examples for livelossplot Provides: python3-livelossplot-doc %description help # livelossplot [![livelossplot version - PyPI](https://img.shields.io/pypi/v/livelossplot)](https://pypi.org/project/livelossplot/) ![PyPI status](https://img.shields.io/pypi/status/livelossplot.svg) ![MIT license - PyPI](https://img.shields.io/pypi/l/livelossplot.svg) ![Python version - PyPI](https://img.shields.io/pypi/pyversions/livelossplot.svg) [![GitHub Workflow Status](https://img.shields.io/github/workflow/status/stared/livelossplot/Python%20package)](https://github.com/stared/livelossplot/actions) [![Downloads](http://pepy.tech/badge/livelossplot)](https://pepy.tech/project/livelossplot) [![Twitter @pmigdal](https://img.shields.io/twitter/follow/pmigdal)](https://twitter.com/pmigdal) Don't train deep learning models blindfolded! Be impatient and look at each epoch of your training! ([RECENT CHANGES](CHANGELOG.md), [EXAMPLES IN COLAB](https://colab.research.google.com/github/stared/livelossplot), [API LOOKUP](http://p.migdal.pl/livelossplot/), [CODE](https://github.com/stared/livelossplot)) A live training loss plot in [Jupyter Notebook](http://jupyter.org/) for [Keras](https://keras.io/), [PyTorch](http://pytorch.org/) and other frameworks. An open-source Python package by [Piotr Migdał](https://p.migdal.pl/), [Bartłomiej Olechno](https://github.com/Bartolo1024/) and [others](https://github.com/stared/livelossplot/graphs/contributors). **Open for collaboration!** (Some tasks are as simple as writing code docstrings, so - no excuses! :)) ```python from livelossplot import PlotLossesKeras model.fit(X_train, Y_train, epochs=10, validation_data=(X_test, Y_test), callbacks=[PlotLossesKeras()], verbose=0) ``` ![Animated fig for livelossplot tracking log-loss and accuracy](https://raw.githubusercontent.com/stared/livelossplot/master/livelossplot.gif) - (The most FA)Q: Why not TensorBoard? - A: Jupyter Notebook compatibility (for exploration and teaching). The simplicity of use. ## Installation To install [this version from PyPI](https://pypi.org/project/livelossplot/), type: ```bash pip install livelossplot ``` To get the newest one from this repo (note that we are in the alpha stage, so there may be frequent updates), type: ```bash pip install git+git://github.com/stared/livelossplot.git ``` ## Examples Look at notebook files with full working [examples](https://github.com/stared/livelossplot/blob/master/examples/): - [keras.ipynb](https://github.com/stared/livelossplot/blob/master/examples/keras.ipynb) - a Keras callback - [minimal.ipynb](https://github.com/stared/livelossplot/blob/master/examples/minimal.ipynb) - a bare API, to use anywhere - [bokeh.ipynb](https://github.com/stared/livelossplot/blob/master/examples/bokeh.ipynb) - a bare API, plots with Bokeh ([open it in Colab to see the plots](https://colab.research.google.com/github/stared/livelossplot/blob/master/examples/bokeh.ipynb)) - [pytorch.ipynb](https://github.com/stared/livelossplot/blob/master/examples/pytorch.ipynb) - a bare API, as applied to PyTorch - [2d_prediction_maps.ipynb](https://github.com/stared/livelossplot/blob/master/examples/2d_prediction_maps.ipynb) - example of custom plots - 2d prediction maps (0.4.1+) - [poutyne.ipynb](https://github.com/stared/livelossplot/blob/master/examples/poutyne.ipynb) - a Poutyne callback ([Poutyne](https://poutyne.org/) is a Keras-like framework for PyTorch) - [torchbearer.ipynb](https://github.com/stared/livelossplot/blob/master/examples/torchbearer.ipynb) - an example using the built in functionality from torchbearer ([torchbearer](https://github.com/ecs-vlc/torchbearer) is a model fitting library for PyTorch) - [neptune.py](https://github.com/stared/livelossplot/blob/master/examples/neptune.py) and [neptune.ipynb](https://github.com/stared/livelossplot/blob/master/examples/neptune.ipynb) - a [Neptune.AI](https://neptune.ai/) - [matplotlib.ipynb](https://github.com/stared/livelossplot/blob/master/examples/matplotlib.ipynb) - a Matplotlib output example - [various_options.ipynb](https://github.com/stared/livelossplot/blob/master/examples/various_options.ipynb) - an extended API for metrics grouping and custom outputs You [run examples in Colab](https://colab.research.google.com/github/stared/livelossplot). ## Overview Text logs are easy, but it's easy to miss the most crucial information: is it learning, doing nothing or overfitting? Visual feedback allows us to keep track of the training process. Now there is one for Jupyter. If you want to get serious - use [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard), . But what if you just want to train a small model in Jupyter Notebook? Here is a way to do so, using `livelossplot` as a plug&play component ### from livelossplot import ... `PlotLosses` for a generic API. ```{python} plotlosses = PlotLosses() plotlosses.update({'acc': 0.7, 'val_acc': 0.4, 'loss': 0.9, 'val_loss': 1.1}) plot.send() # draw, update logs, etc ``` There are callbacks for common libraries and frameworks: `PlotLossesKeras`, `PlotLossesKerasTF`, `PlotLossesPoutyne`, `PlotLossesIgnite`. Feel invited to write, and contribute, your adapter. If you want to use a bare logger, there is `MainLogger`. ### from livelossplot.outputs import ... Plots: `MatplotlibPlot`, `BokehPlot`. Loggers: `ExtremaPrinter` (to standard output), `TensorboardLogger`, `TensorboardTFLogger`, `NeptuneLogger`. To use them, initialize PlotLosses with some outputs: ```{python} plotlosses = PlotLosses(outputs=[MatplotlibPlot(), TensorboardLogger()]) ``` There are custom `matplotlib` plots in `livelossplot.outputs.matplotlib_subplots` you can pass in `MatplotlibPlot` arguments. If you like to plot with [Bokeh](https://docs.bokeh.org/en/latest/) instead of [matplotlib](https://matplotlib.org/), use ```{python} plotlosses = PlotLosses(outputs=[BokehPlot()]) ``` ## Sponsors This project supported by [Jacek Migdał](http://jacek.migdal.pl/), [Marek Cichy](https://medium.com/@marekkcichy/), [Casper da Costa-Luis](https://cdcl.ml/), and [Piotr Zientara](https://twitter.com/piotr_zientara). [Join the sponsors - show your ❤️ and support, and appear on the list](https://github.com/sponsors/stared)! It will give me time and energy to work on this project. This project is also supported by a European program *Program Operacyjny Inteligentny Rozwój* for [GearShift - building the engine of behavior of wheeled motor vehicles and map’s generation based on artificial intelligence algorithms implemented on the Unreal Engine platform](https://mapadotacji.gov.pl/projekty/874596/?lang=en) lead by ECC Games (NCBR grant GameINN). ## Trivia It started as [this gist](https://gist.github.com/stared/dfb4dfaf6d9a8501cd1cc8b8cb806d2e). Since it went popular, I decided to rewrite it as a package. Oh, and I am in general interested in data vis, see [Simple diagrams of convoluted neural networks](https://medium.com/inbrowserai/simple-diagrams-of-convoluted-neural-networks-39c097d2925b) (and overview of deep learning architecture diagrams): > A good diagram is worth a thousand equations — let’s create more of these! ...or [my other data vis projects](https://p.migdal.pl/projects/). ## Todo If you want more functionality - open an [Issue](https://github.com/stared/livelossplot/issues) or even better - prepare a [Pull Request](https://github.com/stared/livelossplot/pulls). %prep %autosetup -n livelossplot-0.5.5 %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-livelossplot -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Sun Apr 23 2023 Python_Bot - 0.5.5-1 - Package Spec generated