mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-08-07 03:57:45 +00:00
Linlang
3dbd703828
* fix: prevent calendar index overflow when signal data ends early * fix: make test_end optional to resolve Qlib backtest calendar misalignment * fix: enhance GPU information output in get_gpu_info function * fix: improve GPU information output in get_gpu_info function for better clarity --------- Co-authored-by: Xu Yang <peteryang@vip.qq.com>
67 lines
2.6 KiB
Python
67 lines
2.6 KiB
Python
import platform
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import subprocess
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import sys
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from importlib.metadata import distributions
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def print_runtime_info():
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print("=== Python Runtime Info ===")
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print(f"Python {sys.version} on {platform.system()} {platform.release()}")
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def get_gpu_info():
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try:
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# Option 1: Use PyTorch
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import torch
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if torch.cuda.is_available():
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print("\n=== GPU Info (via PyTorch) ===")
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print(f"CUDA Version: {torch.version.cuda}")
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print(f"GPU Count: {torch.cuda.device_count()}")
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if torch.cuda.device_count() > 0:
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gpu_name_list = []
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gpu_total_mem_list = []
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gpu_allocated_mem_list = []
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gpu_cached_mem_list = []
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for i in range(torch.cuda.device_count()):
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gpu_name_list.append(torch.cuda.get_device_name(i))
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gpu_total_mem_list.append(torch.cuda.get_device_properties(i).total_memory)
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gpu_allocated_mem_list.append(torch.cuda.memory_allocated(i))
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gpu_cached_mem_list.append(torch.cuda.memory_reserved(i))
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for i in range(torch.cuda.device_count()):
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print(f" - GPU {i}: {gpu_name_list[i]}")
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print(f" Total Memory: {gpu_total_mem_list[i] / 1024**3:.2f} GB")
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print(f" Allocated Memory: {gpu_allocated_mem_list[i] / 1024**3:.2f} GB")
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print(f" Cached Memory: {gpu_cached_mem_list[i] / 1024**3:.2f} GB")
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print(" - All GPUs Summary:")
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print(f" Total Memory: {sum(gpu_total_mem_list) / 1024**3:.2f} GB")
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print(f" Total Allocated Memory: {sum(gpu_allocated_mem_list) / 1024**3:.2f} GB")
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print(f" Total Cached Memory: {sum(gpu_cached_mem_list) / 1024**3:.2f} GB")
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else:
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print("No CUDA GPU detected (PyTorch)!")
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else:
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print("\nNo CUDA GPU detected (PyTorch).")
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except ImportError:
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# Option 2: Use nvidia-smi
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try:
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result = subprocess.run(
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["nvidia-smi", "--query-gpu=name,memory.total,memory.used", "--format=csv"],
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capture_output=True,
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text=True,
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)
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if result.returncode == 0:
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print("\n=== GPU Info (via nvidia-smi) ===")
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print(result.stdout.strip())
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else:
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print("\nNo GPU detected (nvidia-smi not available).")
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except FileNotFoundError:
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print("\nNo GPU detected (nvidia-smi not installed).")
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if __name__ == "__main__":
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print_runtime_info()
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get_gpu_info()
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