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GPU.py
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27
GPU.py
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import time
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import torch
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import sys
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import os
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os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
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# 测试gpu计算耗时
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A = torch.ones(5000, 5000).to('cuda')
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B = torch.ones(5000, 5000).to('cuda')
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startTime2 = time.time()
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for i in range(100):
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C = torch.matmul(A, B)
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endTime2 = time.time()
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print('gpu:', round((endTime2 - startTime2) * 1000, 2), 'ms')
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sys.exit(1)
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# # 测试cpu计算耗时
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# A = torch.ones(5000, 5000)
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# B = torch.ones(5000, 5000)
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# startTime1 = time.time()
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# for i in range(100):
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# C = torch.matmul(A, B)
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# endTime1 = time.time()
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# print('cpu:', round((endTime1 - startTime1) * 1000, 2), 'ms')
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181
Performance_Monitor.py
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181
Performance_Monitor.py
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import time
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import psutil
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import openpyxl
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import subprocess
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from pynvml import *
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import pandas as pd
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import matplotlib.pyplot as plt
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import tkinter as tk
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from tkinter import ttk
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import numpy as np
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import asyncio
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import sys
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import os
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import trio
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class PerformanceMonitor():
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def __init__(self, command):
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self.command = command
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self.process = None
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self.psutil_process = None
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self.handle = None
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self.root = tk.Tk()
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self.root.title("")
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self.root.geometry("1500x600")
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self.tree_frame = ttk.Frame(self.root)
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self.tree_frame.pack(expand=True, fill="both")
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self.tree_scrollbar = ttk.Scrollbar(self.tree_frame)
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self.tree = ttk.Treeview(self.tree_frame, yscrollcommand=self.tree_scrollbar.set)
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self.tree_scrollbar.config(command=self.tree.yview)
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self.tree_scrollbar.pack(side="right", fill="y")
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self.tree.pack(expand=True, fill="both")
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self.tree["columns"] = ("Time (s)", "CPU (%)", "RAM (MB)", "GPU (%)", "VRAM (MB)")
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self.tree.heading("#0", text="Index")
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self.tree.heading("Time (s)", text="Time (s)")
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self.tree.heading("CPU (%)", text="CPU (%)")
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self.tree.heading("RAM (MB)", text="RAM (MB)")
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self.tree.heading("GPU (%)", text="GPU (%)")
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self.tree.heading("VRAM (MB)", text="VRAM (MB)")
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self.wb = openpyxl.Workbook()
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self.ws = self.wb.active
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self.ws.append(["Time (s)", "CPU (%)", "RAM (MB)", "GPU (%)", "VRAM (MB)"])
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self.total_cpu = 0
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self.total_ram = 0
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self.total_gpu = 0
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self.total_vram = 0
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self.record_count = 0
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async def start_process(self):
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if len(self.command) > 1 and os.path.isfile(self.command[1]):
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self.process = subprocess.Popen(self.command)
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self.root.title(f"Performance Monitor for {self.command[1]}")
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elif len(self.command) > 0:
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self.process = psutil.Process(int(self.command[0]))
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self.root.title(f"Performance Monitor for PID {self.command[0]}")
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else:
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print("Invalid command format. Please provide a PID or a script to execute.")
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sys.exit(1)
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try:
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self.psutil_process = psutil.Process(self.process.pid)
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except psutil.NoSuchProcess:
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print("Failed to create subprocess.")
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sys.exit(1)
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self.handle = nvmlDeviceGetHandleByIndex(0)
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async def get_record_interval(self, time_sec):
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if time_sec < 10:
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return 0.1
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elif time_sec < 60:
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return 1
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elif time_sec < 3600:
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return 60
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elif time_sec < 36000:
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return 300
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else:
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return 600
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async def monitor(self):
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start_time = time.perf_counter()
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next_record_time = 0
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try:
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while True:
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current_time = time.perf_counter()
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time_sec = current_time - start_time
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# if not self.psutil_process.is_running(): break
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if (not self.psutil_process.is_running() if os.name == 'nt' else self.psutil_process.status() == psutil.STATUS_ZOMBIE): break
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time.sleep(0.1)
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cpu_usage, ram_usage = await self.get_cpu_ram_usage()
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gpu_usage, vram_usage = await self.get_gpu_usage()
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cpu_usage = 0 if cpu_usage is None else cpu_usage
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ram_usage = 0 if ram_usage is None else ram_usage
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gpu_usage = 0 if gpu_usage is None else gpu_usage
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vram_usage = 0 if vram_usage is None else vram_usage
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self.total_cpu += cpu_usage
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self.total_ram += ram_usage
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self.total_gpu += gpu_usage
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self.total_vram += vram_usage
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self.record_count += 1
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if time_sec >= next_record_time:
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self.ws.append([round(time_sec, 3), round(cpu_usage, 3), round(ram_usage, 3), round(gpu_usage, 3), round(vram_usage, 3)])
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self.tree.insert("", "end", text=str(self.record_count), values=(round(time_sec, 3), round(cpu_usage, 3), round(ram_usage, 3), round(gpu_usage, 3), round(vram_usage, 3)))
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next_record_time = time_sec + await self.get_record_interval(time_sec)
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self.tree.yview_moveto(1)
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self.root.update()
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finally:
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await self.print_average_metrics()
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self.wb.save("Performance.xlsx")
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print("save the Performance.xlsx")
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# df = pd.read_excel("Performance.xlsx")
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# x_data = df["Time (s)"]
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# y_data_cpu = df["CPU (%)"]
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# y_data_ram = df["RAM (MB)"]
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# y_data_gpu = df["GPU (%)"]
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# y_data_vram = df["VRAM (MB)"]
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# plt.figure(figsize=(15, 8))
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# plt.plot(x_data, y_data_cpu, label='CPU', linewidth=2, color='r', marker='', markersize=6, markevery=20)
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# plt.plot(x_data, y_data_ram, label='RAM', linewidth=2, color='y', marker='', markersize=6, markevery=20)
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# plt.plot(x_data, y_data_gpu, label='GPU', linewidth=2, color='b', marker='', markersize=6, markevery=20)
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# plt.plot(x_data, y_data_vram, label='VRAM', linewidth=2, color='g', marker='', markersize=6, markevery=20)
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# plt.xticks(np.arange(0, max(x_data)+1, 10))
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# plt.yticks(np.arange(0, max(max(y_data_cpu), max(y_data_ram), max(y_data_gpu), max(y_data_vram))+1, 10))
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# plt.xlabel("Time (s)")
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# plt.ylabel('Average')
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# plt.title("Performance Metrics")
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# plt.legend()
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# plt.grid()
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# plt.show()
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async def get_cpu_ram_usage(self):
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try:
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cpu_usage = self.psutil_process.cpu_percent(interval=None)
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ram_usage = self.psutil_process.memory_info().rss / (1024 ** 2)
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return cpu_usage, ram_usage
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except psutil.NoSuchProcess:
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return None, None
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async def get_gpu_usage(self):
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gpu_process_info = nvmlDeviceGetComputeRunningProcesses(self.handle)
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gpu_usage, vram_usage = 0.0, 0.0
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for proc in gpu_process_info:
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if proc.pid == self.process.pid:
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gpu_usage = proc.usedGpuMemory * 100 / nvmlDeviceGetMemoryInfo(self.handle).total
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vram_usage = proc.usedGpuMemory / (1024 ** 2)
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break
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return gpu_usage, vram_usage
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async def print_average_metrics(self):
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if self.record_count > 0:
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avg_cpu = self.total_cpu / self.record_count
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avg_ram = self.total_ram / self.record_count
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avg_gpu = self.total_gpu / self.record_count
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avg_vram = self.total_vram / self.record_count
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print(f"Average CPU: {avg_cpu:.3f}% | Average RAM: {avg_ram:.3f}MB | Average GPU: {avg_gpu:.3f}% | Average VRAM: {avg_vram:.3f}MB")
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@staticmethod
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async def run():
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nvmlInit()
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if len(sys.argv) < 2: print("Usage: Performance.py <command>"); sys.exit(1)
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command = sys.argv[1:]
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monitor = PerformanceMonitor(command)
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await monitor.start_process()
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await monitor.monitor()
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nvmlShutdown()
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if __name__ == "__main__":
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trio.run(PerformanceMonitor.run)
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6
requirements.txt
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6
requirements.txt
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psutil
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openpyxl
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pynvml
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pandas
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matplotlib
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trio
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