68 lines
1.8 KiB
Python
68 lines
1.8 KiB
Python
import os
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#测试单个gpu，没有worker的资源脚本
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import gym
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import ray
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from gym.spaces import Discrete, Box
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from ray import tune
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class SimpleCorridor(gym.Env):
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def __init__(self, config):
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self.end_pos = config['corridor_length']
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self.cur_pos = 0
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self.action_space = Discrete(2)
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self.observation_space = Box(0.0, self.end_pos, shape=(1,))
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def reset(self):
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self.cur_pos = 0
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return [self.cur_pos]
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def step(self, action):
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if action == 0 and self.cur_pos > 0:
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self.cur_pos -= 1
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elif action == 1:
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self.cur_pos += 1
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done = self.cur_pos >= self.end_pos
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return [self.cur_pos], 1 if done else 0, done, {}
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if __name__ == '__main__':
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from datetime import datetime
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start_time = datetime.utcnow()
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print('Python start time: {} UTC'.format(start_time))
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if 'CLOUD_PROVIDER' in os.environ and os.environ['CLOUD_PROVIDER'] == 'Agit':
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from agit import ray_init
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ray_init()
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else:
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ray.init()
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print('Ray Cluster Resources: {}'.format(ray.cluster_resources()))
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import tensorflow as tf
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print('TensorFlow CUDA is available: {}'.format(tf.config.list_physical_devices('GPU')))
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import torch
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print('pyTorch CUDA is available: {}'.format(torch.cuda.is_available()))
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tune.run(
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'PPO',
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queue_trials=True, # Don't use this parameter unless you know what you do.
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stop={'training_iteration': 10},
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config={
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'env': SimpleCorridor,
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'env_config': {'corridor_length': 5},
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'num_gpus': 1
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}
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)
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complete_time = datetime.utcnow()
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print('Python complete time: {} UTC'.format(complete_time))
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print('Python resource time: {} UTC'.format(complete_time - start_time)) |