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|
%global _empty_manifest_terminate_build 0
Name: python-ai-traineree
Version: 0.5.2
Release: 1
Summary: Yet another zoo of (Deep) Reinforcment Learning methods in Python using PyTorch
License: Apache Software License
URL: https://github.com/laszukdawid/ai-traineree
Source0: https://mirrors.aliyun.com/pypi/web/packages/22/7c/19594210c0374278e9b1b5e5f0628cffe80505bdde448f3d19f957daac59/ai-traineree-0.5.2.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-torch
Requires: python3-jsons
Requires: python3-gym
Requires: python3-black
Requires: python3-isort
Requires: python3-pycodestyle
Requires: python3-sphinx
Requires: python3-sphinx-rtd-theme
Requires: python3-matplotlib
Requires: python3-tensorboard
Requires: python3-gym
Requires: python3-gym[all]
Requires: python3-mlagents
Requires: python3-neptune-client
Requires: python3-psutil
Requires: python3-pettingzoo[other]
Requires: python3-matplotlib
Requires: python3-tensorboard
Requires: python3-mock
Requires: python3-pytest
Requires: python3-pytest-cov
Requires: python3-pytest-mock
Requires: python3-flake8
Requires: python3-gym[box2d]
Requires: python3-pettingzoo[other,sisl]
%description
# ai-traineree
[](https://app.travis-ci.com/laszukdawid/ai-traineree)
[](https://ai-traineree.readthedocs.io/)
[](https://codecov.io/gh/laszukdawid/ai-traineree)
[](https://www.codacy.com/gh/laszukdawid/ai-traineree/dashboard?utm_source=github.com&utm_medium=referral&utm_content=laszukdawid/ai-traineree&utm_campaign=Badge_Grade)
[](https://discord.gg/n4bN7ZHn)
[](https://zenodo.org/badge/latestdoi/267484000)
The intention is to have a zoo of Deep Reinforcment Learning methods and showcasing their application on some environments.
Read more in the doc: [ReadTheDocs AI-Traineree](https://ai-traineree.readthedocs.io/).
## Why another?
The main reason is the implemention philosophy.
We strongly believe that agents should be emerged in the environment and not the other way round.
Majority of the popular implementations pass environment instance to the agent as if the agent was the focus point.
This might ease implementation of some algorithms but it isn't representative of the world;
agents want to control the environment but that doesn't mean they can/should.
That, and using PyTorch instead of Tensorflow or JAX.
## Quick start
To get started with training your RL agent you need three things: an agent, an environment and a runner. Let's say you want to train a DQN agent on OpenAI CartPole-v1:
```python
from ai_traineree.agents.dqn import DQNAgent
from ai_traineree.runners.env_runner import EnvRunner
from ai_traineree.tasks import GymTask
task = GymTask('CartPole-v1')
agent = DQNAgent(task.obs_space, task.action_space)
env_runner = EnvRunner(task, agent)
scores = env_runner.run()
```
or execute one of provided examples
> \$ python -m examples.cart_dqn
That's it.
## Installation
### PyPi (recommended)
The quickest way to install package is through `pip`.
> \$ pip install ai-traineree
### Git repository clone
As usual with Python, the expectation is to have own virtual environment and then pip install requirements. For example,
```bash
> python -m venv .venv
> git clone git@github.com:laszukdawid/ai-traineree.git
> source .venv/bin/activate
> python setup.py install
```
## Current state
### Playing gym
One way to improve learning speed is to simply show them how to play or, more researchy/creepy, provide a proper seed.
This isn't a general rule, since some algorithms train better without any human interaction, but since you're on GitHub... that's unlikely your case.
Currently there's a script [`interact.py`](scripts/interact.py) which uses OpenAI Gym's play API to record moves and AI Traineree to store them
in a buffer. Such buffers can be loaded by agents on initiation.
This is just a beginning and there will be more work on these interactions.
*Requirement*: Install `pygame`.
### Agents
| Short | Progress | Link | Full name | Doc |
| ------- | --------------------------------------------- | -------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------- | ------------------------------------------------------------------------ |
| DQN | [Implemented](ai_traineree/agents/dqn.py) | [DeepMind](https://deepmind.com/research/publications/human-level-control-through-deep-reinforcement-learning) | Deep Q-learning Network | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#dqn) |
| DDPG | [Implemented](ai_traineree/agents/ddpg.py) | [arXiv](https://arxiv.org/abs/1509.02971) | Deep Deterministic Policy Gradient | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#ddpg) |
| D4PG | [Implemented](ai_traineree/agents/d4pg.py) | [arXiv](https://arxiv.org/abs/1804.08617) | Distributed Distributional Deterministic Policy Gradients | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#d4pg) |
| TD3 | [Implemented](ai_traineree/agents/td3.py) | [arXiv](https://arxiv.org/abs/1802.09477) | Twine Delayed Deep Deterministic policy gradient | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#td3) |
| PPO | [Implemented](ai_traineree/agents/ppo.py) | [arXiv](https://arxiv.org/abs/1707.06347) | Proximal Policy Optimization | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#ppo) |
| SAC | [Implemented](ai_traineree/agents/sac.py) | [arXiv](https://arxiv.org/abs/1801.01290) | Soft Actor Critic | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#sac) |
| TRPO | | [arXiv](https://arxiv.org/abs/1502.05477) | Trust Region Policy Optimization |
| RAINBOW | [Implemented](ai_traineree/agents/rainbow.py) | [arXiv](https://arxiv.org/abs/1710.02298) | DQN with a few improvements | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#rainbow) |
### Multi agents
We provide both Multi Agents agents entities and means to execute them against supported (below) environements.
However, that doesn't mean one can be used without the other.
| Short | Progress | Link | Full name | Doc |
| ------ | ------------------------------------------------- | ----------------------------------------- | ---------------------- | ---------------------------------------------------------------------------- |
| IQL | [Implemented](ai_traineree/multi_agent/iql.py) | | Independent Q-Learners | [Doc](https://ai-traineree.readthedocs.io/en/latest/multi_agent.html#iql) |
| MADDPG | [Implemented](ai_traineree/multi_agent/maddpg.py) | [arXiv](https://arxiv.org/abs/1706.02275) | Multi agent DDPG | [Doc](https://ai-traineree.readthedocs.io/en/latest/multi_agent.html#maddpg) |
### Loggers
Supports using Tensorboard (via PyTorch's [SummaryWriter](https://pytorch.org/docs/stable/tensorboard.html)) and [Neptune](https://neptune.ai) to display metrics. Wrappers are provided as `TensorboardLogger` and `NeptuneLogger`.
*Note*: In order to use Neptune one needs to install [neptune-client](https://pypi.org/project/neptune-client/) (`pip install neptune-client`).
### Environments
| Name | Progress | Link |
| -------------------- | ------------------ | -------------------------------------------------------------------------------------------- |
| OpenAI Gym - Classic | Done |
| OpenAI Gym - Atari | Done |
| OpenAI Gym - MuJoCo | Not interested. |
| PettingZoo | Initial support | [Page](https://www.pettingzoo.ml/) / [GitHub](https://github.com/PettingZoo-Team/PettingZoo) |
| Unity ML | Somehow supported. | [Page](https://unity3d.com/machine-learning) |
| MAME Linux emulator | Interested. | [Official page](https://www.mamedev.org/) |
### Development
We are open to any contributions. If you want to contribute but don't know what then feel free to reach out (see Contact below).
The best way to start is through updating documentation and adding tutorials.
In addition there are many other things that we know of which need improvement but also plenty that we don't know of.
Setting up development environment requires installing `dev` and `test` extra packages.
The `dev` extras are for mainly for linting and formatting, and the `test` is for running tests.
We recommend using `pip` so to install everything requires for development run
```bash
$ pip install -e .[dev,test]
```
Once installed, please configure your IDE to use `black` as formatter, `pycodestyle` as linter,
and `isort` for sorting imports. All these are included in the `dev` extra packages.
### Contact
Should we focus on something specificallly? Let us know by opening a feature request [GitHub issue](https://github.com/laszukdawid/ai-traineree/issues) or contacting through [ai-traineree@dawid.lasz.uk](mailto:ai-traineree@dawid.lasz.uk).
## Citing project
```latex
@misc{ai-traineree,
author = {Laszuk, Dawid},
title = {AI Traineree: Reinforcement learning toolset},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/laszukdawid/ai-traineree}},
}
```
%package -n python3-ai-traineree
Summary: Yet another zoo of (Deep) Reinforcment Learning methods in Python using PyTorch
Provides: python-ai-traineree
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-ai-traineree
# ai-traineree
[](https://app.travis-ci.com/laszukdawid/ai-traineree)
[](https://ai-traineree.readthedocs.io/)
[](https://codecov.io/gh/laszukdawid/ai-traineree)
[](https://www.codacy.com/gh/laszukdawid/ai-traineree/dashboard?utm_source=github.com&utm_medium=referral&utm_content=laszukdawid/ai-traineree&utm_campaign=Badge_Grade)
[](https://discord.gg/n4bN7ZHn)
[](https://zenodo.org/badge/latestdoi/267484000)
The intention is to have a zoo of Deep Reinforcment Learning methods and showcasing their application on some environments.
Read more in the doc: [ReadTheDocs AI-Traineree](https://ai-traineree.readthedocs.io/).
## Why another?
The main reason is the implemention philosophy.
We strongly believe that agents should be emerged in the environment and not the other way round.
Majority of the popular implementations pass environment instance to the agent as if the agent was the focus point.
This might ease implementation of some algorithms but it isn't representative of the world;
agents want to control the environment but that doesn't mean they can/should.
That, and using PyTorch instead of Tensorflow or JAX.
## Quick start
To get started with training your RL agent you need three things: an agent, an environment and a runner. Let's say you want to train a DQN agent on OpenAI CartPole-v1:
```python
from ai_traineree.agents.dqn import DQNAgent
from ai_traineree.runners.env_runner import EnvRunner
from ai_traineree.tasks import GymTask
task = GymTask('CartPole-v1')
agent = DQNAgent(task.obs_space, task.action_space)
env_runner = EnvRunner(task, agent)
scores = env_runner.run()
```
or execute one of provided examples
> \$ python -m examples.cart_dqn
That's it.
## Installation
### PyPi (recommended)
The quickest way to install package is through `pip`.
> \$ pip install ai-traineree
### Git repository clone
As usual with Python, the expectation is to have own virtual environment and then pip install requirements. For example,
```bash
> python -m venv .venv
> git clone git@github.com:laszukdawid/ai-traineree.git
> source .venv/bin/activate
> python setup.py install
```
## Current state
### Playing gym
One way to improve learning speed is to simply show them how to play or, more researchy/creepy, provide a proper seed.
This isn't a general rule, since some algorithms train better without any human interaction, but since you're on GitHub... that's unlikely your case.
Currently there's a script [`interact.py`](scripts/interact.py) which uses OpenAI Gym's play API to record moves and AI Traineree to store them
in a buffer. Such buffers can be loaded by agents on initiation.
This is just a beginning and there will be more work on these interactions.
*Requirement*: Install `pygame`.
### Agents
| Short | Progress | Link | Full name | Doc |
| ------- | --------------------------------------------- | -------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------- | ------------------------------------------------------------------------ |
| DQN | [Implemented](ai_traineree/agents/dqn.py) | [DeepMind](https://deepmind.com/research/publications/human-level-control-through-deep-reinforcement-learning) | Deep Q-learning Network | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#dqn) |
| DDPG | [Implemented](ai_traineree/agents/ddpg.py) | [arXiv](https://arxiv.org/abs/1509.02971) | Deep Deterministic Policy Gradient | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#ddpg) |
| D4PG | [Implemented](ai_traineree/agents/d4pg.py) | [arXiv](https://arxiv.org/abs/1804.08617) | Distributed Distributional Deterministic Policy Gradients | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#d4pg) |
| TD3 | [Implemented](ai_traineree/agents/td3.py) | [arXiv](https://arxiv.org/abs/1802.09477) | Twine Delayed Deep Deterministic policy gradient | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#td3) |
| PPO | [Implemented](ai_traineree/agents/ppo.py) | [arXiv](https://arxiv.org/abs/1707.06347) | Proximal Policy Optimization | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#ppo) |
| SAC | [Implemented](ai_traineree/agents/sac.py) | [arXiv](https://arxiv.org/abs/1801.01290) | Soft Actor Critic | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#sac) |
| TRPO | | [arXiv](https://arxiv.org/abs/1502.05477) | Trust Region Policy Optimization |
| RAINBOW | [Implemented](ai_traineree/agents/rainbow.py) | [arXiv](https://arxiv.org/abs/1710.02298) | DQN with a few improvements | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#rainbow) |
### Multi agents
We provide both Multi Agents agents entities and means to execute them against supported (below) environements.
However, that doesn't mean one can be used without the other.
| Short | Progress | Link | Full name | Doc |
| ------ | ------------------------------------------------- | ----------------------------------------- | ---------------------- | ---------------------------------------------------------------------------- |
| IQL | [Implemented](ai_traineree/multi_agent/iql.py) | | Independent Q-Learners | [Doc](https://ai-traineree.readthedocs.io/en/latest/multi_agent.html#iql) |
| MADDPG | [Implemented](ai_traineree/multi_agent/maddpg.py) | [arXiv](https://arxiv.org/abs/1706.02275) | Multi agent DDPG | [Doc](https://ai-traineree.readthedocs.io/en/latest/multi_agent.html#maddpg) |
### Loggers
Supports using Tensorboard (via PyTorch's [SummaryWriter](https://pytorch.org/docs/stable/tensorboard.html)) and [Neptune](https://neptune.ai) to display metrics. Wrappers are provided as `TensorboardLogger` and `NeptuneLogger`.
*Note*: In order to use Neptune one needs to install [neptune-client](https://pypi.org/project/neptune-client/) (`pip install neptune-client`).
### Environments
| Name | Progress | Link |
| -------------------- | ------------------ | -------------------------------------------------------------------------------------------- |
| OpenAI Gym - Classic | Done |
| OpenAI Gym - Atari | Done |
| OpenAI Gym - MuJoCo | Not interested. |
| PettingZoo | Initial support | [Page](https://www.pettingzoo.ml/) / [GitHub](https://github.com/PettingZoo-Team/PettingZoo) |
| Unity ML | Somehow supported. | [Page](https://unity3d.com/machine-learning) |
| MAME Linux emulator | Interested. | [Official page](https://www.mamedev.org/) |
### Development
We are open to any contributions. If you want to contribute but don't know what then feel free to reach out (see Contact below).
The best way to start is through updating documentation and adding tutorials.
In addition there are many other things that we know of which need improvement but also plenty that we don't know of.
Setting up development environment requires installing `dev` and `test` extra packages.
The `dev` extras are for mainly for linting and formatting, and the `test` is for running tests.
We recommend using `pip` so to install everything requires for development run
```bash
$ pip install -e .[dev,test]
```
Once installed, please configure your IDE to use `black` as formatter, `pycodestyle` as linter,
and `isort` for sorting imports. All these are included in the `dev` extra packages.
### Contact
Should we focus on something specificallly? Let us know by opening a feature request [GitHub issue](https://github.com/laszukdawid/ai-traineree/issues) or contacting through [ai-traineree@dawid.lasz.uk](mailto:ai-traineree@dawid.lasz.uk).
## Citing project
```latex
@misc{ai-traineree,
author = {Laszuk, Dawid},
title = {AI Traineree: Reinforcement learning toolset},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/laszukdawid/ai-traineree}},
}
```
%package help
Summary: Development documents and examples for ai-traineree
Provides: python3-ai-traineree-doc
%description help
# ai-traineree
[](https://app.travis-ci.com/laszukdawid/ai-traineree)
[](https://ai-traineree.readthedocs.io/)
[](https://codecov.io/gh/laszukdawid/ai-traineree)
[](https://www.codacy.com/gh/laszukdawid/ai-traineree/dashboard?utm_source=github.com&utm_medium=referral&utm_content=laszukdawid/ai-traineree&utm_campaign=Badge_Grade)
[](https://discord.gg/n4bN7ZHn)
[](https://zenodo.org/badge/latestdoi/267484000)
The intention is to have a zoo of Deep Reinforcment Learning methods and showcasing their application on some environments.
Read more in the doc: [ReadTheDocs AI-Traineree](https://ai-traineree.readthedocs.io/).
## Why another?
The main reason is the implemention philosophy.
We strongly believe that agents should be emerged in the environment and not the other way round.
Majority of the popular implementations pass environment instance to the agent as if the agent was the focus point.
This might ease implementation of some algorithms but it isn't representative of the world;
agents want to control the environment but that doesn't mean they can/should.
That, and using PyTorch instead of Tensorflow or JAX.
## Quick start
To get started with training your RL agent you need three things: an agent, an environment and a runner. Let's say you want to train a DQN agent on OpenAI CartPole-v1:
```python
from ai_traineree.agents.dqn import DQNAgent
from ai_traineree.runners.env_runner import EnvRunner
from ai_traineree.tasks import GymTask
task = GymTask('CartPole-v1')
agent = DQNAgent(task.obs_space, task.action_space)
env_runner = EnvRunner(task, agent)
scores = env_runner.run()
```
or execute one of provided examples
> \$ python -m examples.cart_dqn
That's it.
## Installation
### PyPi (recommended)
The quickest way to install package is through `pip`.
> \$ pip install ai-traineree
### Git repository clone
As usual with Python, the expectation is to have own virtual environment and then pip install requirements. For example,
```bash
> python -m venv .venv
> git clone git@github.com:laszukdawid/ai-traineree.git
> source .venv/bin/activate
> python setup.py install
```
## Current state
### Playing gym
One way to improve learning speed is to simply show them how to play or, more researchy/creepy, provide a proper seed.
This isn't a general rule, since some algorithms train better without any human interaction, but since you're on GitHub... that's unlikely your case.
Currently there's a script [`interact.py`](scripts/interact.py) which uses OpenAI Gym's play API to record moves and AI Traineree to store them
in a buffer. Such buffers can be loaded by agents on initiation.
This is just a beginning and there will be more work on these interactions.
*Requirement*: Install `pygame`.
### Agents
| Short | Progress | Link | Full name | Doc |
| ------- | --------------------------------------------- | -------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------- | ------------------------------------------------------------------------ |
| DQN | [Implemented](ai_traineree/agents/dqn.py) | [DeepMind](https://deepmind.com/research/publications/human-level-control-through-deep-reinforcement-learning) | Deep Q-learning Network | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#dqn) |
| DDPG | [Implemented](ai_traineree/agents/ddpg.py) | [arXiv](https://arxiv.org/abs/1509.02971) | Deep Deterministic Policy Gradient | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#ddpg) |
| D4PG | [Implemented](ai_traineree/agents/d4pg.py) | [arXiv](https://arxiv.org/abs/1804.08617) | Distributed Distributional Deterministic Policy Gradients | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#d4pg) |
| TD3 | [Implemented](ai_traineree/agents/td3.py) | [arXiv](https://arxiv.org/abs/1802.09477) | Twine Delayed Deep Deterministic policy gradient | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#td3) |
| PPO | [Implemented](ai_traineree/agents/ppo.py) | [arXiv](https://arxiv.org/abs/1707.06347) | Proximal Policy Optimization | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#ppo) |
| SAC | [Implemented](ai_traineree/agents/sac.py) | [arXiv](https://arxiv.org/abs/1801.01290) | Soft Actor Critic | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#sac) |
| TRPO | | [arXiv](https://arxiv.org/abs/1502.05477) | Trust Region Policy Optimization |
| RAINBOW | [Implemented](ai_traineree/agents/rainbow.py) | [arXiv](https://arxiv.org/abs/1710.02298) | DQN with a few improvements | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#rainbow) |
### Multi agents
We provide both Multi Agents agents entities and means to execute them against supported (below) environements.
However, that doesn't mean one can be used without the other.
| Short | Progress | Link | Full name | Doc |
| ------ | ------------------------------------------------- | ----------------------------------------- | ---------------------- | ---------------------------------------------------------------------------- |
| IQL | [Implemented](ai_traineree/multi_agent/iql.py) | | Independent Q-Learners | [Doc](https://ai-traineree.readthedocs.io/en/latest/multi_agent.html#iql) |
| MADDPG | [Implemented](ai_traineree/multi_agent/maddpg.py) | [arXiv](https://arxiv.org/abs/1706.02275) | Multi agent DDPG | [Doc](https://ai-traineree.readthedocs.io/en/latest/multi_agent.html#maddpg) |
### Loggers
Supports using Tensorboard (via PyTorch's [SummaryWriter](https://pytorch.org/docs/stable/tensorboard.html)) and [Neptune](https://neptune.ai) to display metrics. Wrappers are provided as `TensorboardLogger` and `NeptuneLogger`.
*Note*: In order to use Neptune one needs to install [neptune-client](https://pypi.org/project/neptune-client/) (`pip install neptune-client`).
### Environments
| Name | Progress | Link |
| -------------------- | ------------------ | -------------------------------------------------------------------------------------------- |
| OpenAI Gym - Classic | Done |
| OpenAI Gym - Atari | Done |
| OpenAI Gym - MuJoCo | Not interested. |
| PettingZoo | Initial support | [Page](https://www.pettingzoo.ml/) / [GitHub](https://github.com/PettingZoo-Team/PettingZoo) |
| Unity ML | Somehow supported. | [Page](https://unity3d.com/machine-learning) |
| MAME Linux emulator | Interested. | [Official page](https://www.mamedev.org/) |
### Development
We are open to any contributions. If you want to contribute but don't know what then feel free to reach out (see Contact below).
The best way to start is through updating documentation and adding tutorials.
In addition there are many other things that we know of which need improvement but also plenty that we don't know of.
Setting up development environment requires installing `dev` and `test` extra packages.
The `dev` extras are for mainly for linting and formatting, and the `test` is for running tests.
We recommend using `pip` so to install everything requires for development run
```bash
$ pip install -e .[dev,test]
```
Once installed, please configure your IDE to use `black` as formatter, `pycodestyle` as linter,
and `isort` for sorting imports. All these are included in the `dev` extra packages.
### Contact
Should we focus on something specificallly? Let us know by opening a feature request [GitHub issue](https://github.com/laszukdawid/ai-traineree/issues) or contacting through [ai-traineree@dawid.lasz.uk](mailto:ai-traineree@dawid.lasz.uk).
## Citing project
```latex
@misc{ai-traineree,
author = {Laszuk, Dawid},
title = {AI Traineree: Reinforcement learning toolset},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/laszukdawid/ai-traineree}},
}
```
%prep
%autosetup -n ai-traineree-0.5.2
%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-ai-traineree -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5.2-1
- Package Spec generated
|
