await asyncio.sleep(1) async def monitor_memory(process_id, duration): process = psutil.Process(process_id) start_time = time.time() while time.time() - start_time < duration: memory_info = process.memory_info() memory_percent = process.memory_percent() print(f"内存使用量: {memory_inf...
memory_info=pynvml.nvmlDeviceGetMemoryInfo(handle)total_memory=memory_info.total used_memory=memory_info.used free_memory=total_memory-used_memory 1. 2. 3. 4. 这四行代码使用显卡的句柄来获取显存的信息,包括总大小和已使用大小。 print("显存剩余大小:"+str(free_memory)+" bytes") 1. 这行代码将...
import os info = psutil.virtual_memory() print(u'内存使用:',psutil.Process(os.getpid()).memory_info().rss/1024/1024/1024) print(u'总内存:',info.total/1024/1024/1024) print(u'内存占比:',info.percent) print(u'cpu个数:',psutil.cpu_count()) 1. 2. 3. 4. 5. 6. 7. 命令行 ...
>>> psutil.Process().memory_info().rss / (1024 * 1024) 3234.19140625 ### Check structure of 'ob' (Useful for class object) >>> objgraph.show_refs([ob], filename='sample-graph.png') ### Check memory for pandas.DataFrame >>> from sklearn.datasets import load_boston >>> data = ...
mem_info(): mem = psutil.virtual_memory() mem1 = str(mem.total/1024/1024/1024) mem2 = str(mem.free/1024/1024/1024) print("内存总数为:",mem1[0:3],"G") print("空闲内存总数:", mem2[0:3], "G")get_cpu_info()get_mem_info()注意事项 Python中所有字符需要是英文格式 ...
x += i # xrange return x def main(): print test() gc.collect() p = psutil.Process(os.getpid()) 25 print p.get_memory_info() if __name__ == "__main__": main() 对⽐比 range 和 xrange 所需的 RSS 值. range: meminfo(rss=93339648L, vms=2583552000L)! xrange: meminfo(...
show_memory_info('after a created') func() show_memory_info('finished')### 输出 ###initial memory used: 6.67578125 MB#after a created memory used: 199.30859375 MB#finished memory used: 199.30859375 MB 或者把列表返回,在主程序中接收,引用依然存在,垃圾回收就不会被触发,大量内存仍然被占用着 def...
defmonitor_memory_usage():process=psutil.Process()memory_usage=process.memory_info().rss/1024/1024# 获取内存使用情况(MB)returnmemory_usageprint("Memory Usage:",monitor_memory_usage(),"MB") 并发和异步编程中的内存管理 线程安全的内存管理:在多线程环境中,需要注意内存管理的线程安全性,避免出现竞态条...
get_memory_info(): tmpdict = {} c = wmi.WMI () cs = c.Win32_ComputerSystem() os = c.Win32_OperatingSystem() pfu = c.Win32_PageFileUsage() tmpdict["MemTotal"] = int(cs[0].TotalPhysicalMemory)/1024/1024 tmpdict["MemFree"] = int(os[0].FreePhysicalMemory)/1024 tmpdict["...
mem = process.memory_info()[0] / float(2 ** 20) return mem 发现进程的内存占用一直再上涨,而这从逻辑上来说是不正常的,所以想到程序可能发生了Memory Leak。 python程序的Mem Leak python程序不可能像C/C++一样出现malloc了的内存没有free这样的Memory Leak。但也会遇到“逻辑上没free”的情况,如下代码...