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https://github.com/gryf/coach.git
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5fadb9c18e
Adding mxnet components to rl_coach architectures. - Supports PPO and DQN - Tested with CartPole_PPO and CarPole_DQN - Normalizing filters don't work right now (see #49) and are disabled in CartPole_PPO preset - Checkpointing is disabled for MXNet
107 lines
3.7 KiB
Python
107 lines
3.7 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import sys
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from codecs import open
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from os import path
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from setuptools import setup, find_packages
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import subprocess
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# Creating the pip package involves the following steps:
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# - Define the pip package related files - setup.py (this file) and MANIFEST.in by:
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# 1. Make sure all the requirements in install_requires are defined correctly and that their version is the correct one
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# 2. Add all the non .py files to the package_data and to the MANIFEST.in file
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# 3. Make sure that all the python directories have an __init__.py file
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# - Check that everything works fine by:
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# 1. Create a new virtual environment using `virtualenv coach_env -p python3`
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# 2. Run `pip install -e .`
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# 3. Run `coach -p CartPole_DQN` and make sure it works
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# 4. Run `dashboard` and make sure it works
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# - If everything works fine, build and upload the package to PyPi:
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# 1. Update the version of Coach in the call to setup()
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# 2. Remove the directories build, dist and rl_coach.egg-info if they exist
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# 3. Run `python setup.py sdist`
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# 4. Run `twine upload dist/*`
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here = path.abspath(path.dirname(__file__))
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# Get the long description from the README file
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with open(path.join(here, 'README.md'), encoding='utf-8') as f:
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long_description = f.read()
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install_requires = list()
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with open(path.join(here, 'requirements.txt'), 'r') as f:
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for line in f:
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install_requires.append(line.strip())
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# check if system has CUDA enabled GPU
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p = subprocess.Popen(['command -v nvidia-smi'], stdout=subprocess.PIPE, shell=True)
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out = p.communicate()[0].decode('UTF-8')
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using_GPU = out != ''
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if not using_GPU:
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# For linux wth no GPU, we install the Intel optimized version of TensorFlow
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if sys.platform == "linux" or sys.platform == "linux2":
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subprocess.check_call(['pip install '
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'https://anaconda.org/intel/tensorflow/1.6.0/download/tensorflow-1.6.0-cp35-cp35m-linux_x86_64.whl'],
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shell=True)
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install_requires.append('tensorflow==1.6.0')
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else:
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install_requires.append('tensorflow-gpu==1.9.0')
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# Framework-specific dependencies.
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extras = {
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'mxnet': ['mxnet-cu90mkl>=1.3.0']
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}
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all_deps = []
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for group_name in extras:
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all_deps += extras[group_name]
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extras['all'] = all_deps
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setup(
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name='rl-coach',
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version='0.10.0',
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description='Reinforcement Learning Coach enables easy experimentation with state of the art Reinforcement Learning algorithms.',
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url='https://github.com/NervanaSystems/coach',
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author='Intel AI Lab',
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author_email='coach@intel.com',
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packages=find_packages(),
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python_requires=">=3.5.*",
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install_requires=install_requires,
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extras_require=extras,
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package_data={'rl_coach': ['dashboard_components/*.css',
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'environments/doom/*.cfg',
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'environments/doom/*.wad',
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'environments/mujoco/common/*.xml',
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'environments/mujoco/*.xml',
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'environments/*.ini',
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'tests/*.ini']},
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entry_points={
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'console_scripts': [
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'coach=rl_coach.coach:main',
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'dashboard=rl_coach.dashboard:main'
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],
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}
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)
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