删除 '读取数据集GPU.py'

读取数据集GPU.py

@@ -1,79 +0,0 @@
-import os
-
-import gym
-import ray
-from gym.spaces import Discrete, Box
-from ray import tune
-
-
-class SimpleCorridor(gym.Env):
-    def __init__(self, config):
-        self.end_pos = config['corridor_length']
-        self.cur_pos = 0
-        self.action_space = Discrete(2)
-        self.observation_space = Box(0.0, self.end_pos, shape=(1,))
-
-    def reset(self):
-        self.cur_pos = 0
-        return [self.cur_pos]
-
-    def step(self, action):
-        if action == 0 and self.cur_pos > 0:
-            self.cur_pos -= 1
-        elif action == 1:
-            self.cur_pos += 1
-        done = self.cur_pos >= self.end_pos
-        return [self.cur_pos], 1 if done else 0, done, {}
-
-
-if __name__ == '__main__':
-    from datetime import datetime
-
-    start_time = datetime.utcnow()
-
-    print('Python start time: {} UTC'.format(start_time))
-
-    if 'CLOUD_PROVIDER' in os.environ and os.environ['CLOUD_PROVIDER'] == 'Agit':
-        from agit import ray_init
-
-        ray_init()
-
-        from agit import open
-
-        dataset_path = 'agit://'
-    else:
-        ray.init()
-
-        dataset_path = './'
-
-    print('Ray Cluster Resources: {}'.format(ray.cluster_resources()))
-
-    import tensorflow as tf
-
-    print('TensorFlow CUDA is available: {}'.format(tf.config.list_physical_devices('GPU')))
-
-    import torch
-
-    print('pyTorch CUDA is available: {}'.format(torch.cuda.is_available()))
-
-    with open(dataset_path + 'expert_data.csv', 'rb') as file:
-        raw_data = file.read()
-
-        print(raw_data)
-
-    tune.run(
-        'PPO',
-        queue_trials=True,  # Don't use this parameter unless you know what you do.
-        stop={'training_iteration': 10},
-        config={
-            'env': SimpleCorridor,
-            'env_config': {'corridor_length': 5},
-            'num_gpus': 1
-        }
-    )
-
-    complete_time = datetime.utcnow()
-
-    print('Python complete time: {} UTC'.format(complete_time))
-
-    print('Python resource time: {} UTC'.format(complete_time - start_time))