42 lines
1.1 KiB
Python
42 lines
1.1 KiB
Python
# import json
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# import numpy as np
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#
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#
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# def get_llm_action(index) -> np.ndarray:
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# with open('data/llm_action.json', 'r') as file:
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# data = json.load(file)
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# normalized_index = index % len(data)
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# action = np.array(data[normalized_index])
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# return action
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#
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# data = get_llm_action(2)
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# print(data)
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import numpy as np
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import json
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from data_manager import DataManager
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data_manager = DataManager()
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llm_actions = json.load(open('data/results.json', 'r'))
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for action in llm_actions:
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data_manager.add_llm_element(action)
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month = np.random.randint(1, 13) # choose 12 month
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day = np.random.randint(1, 20)
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llm_action = data_manager.get_llm_data(month, day, 1)
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print(len(data_manager.LLM))
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# import torch
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#
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#
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# def get_available_gpus():
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# if torch.cuda.is_available():
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# num_gpus = torch.cuda.device_count()
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# print(f"Number of available GPUs: {num_gpus}")
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# for i in range(num_gpus):
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# print(f"GPU {i}: {torch.cuda.get_device_name(i)}")
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# else:
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# print("No GPUs are available.")
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#
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#
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# get_available_gpus()
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