接下来,我们可以编写一个简单的Python脚本来查看程序的内存使用率: importpsutil# 获取当前进程的内存使用情况process=psutil.Process()memory_info=process.memory_info()memory_percent=process.memory_percent()print("Memory usage: ",memory_info)print("Memory percent: ",memory_percent) 1. 2. 3. 4. 5. ...
importpsutil# 获取当前进程IDpid=os.getpid()# 获取当前进程process=psutil.Process(pid)# 获取当前进程的显存使用量(单位:字节)memory_usage=process.memory_info().rssprint(f"当前进程的显存使用量为:{memory_usage}字节") 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 上面的代码中,我们首先获取...
rss_memory =int(output.strip()) /1024print(f"Memory usage:{rss_memory:.2f}MB") time.sleep(1)exceptsubprocess.CalledProcessError:print(f"Process with PID{pid}does not exist.")breakif__name__ =="__main__": pid =int(input("Enter the PID of the process to monitor: ")) monitor_pro...
print(f"Virtual Memory Size(VMS) memory usage: {vms_memory / 1024**2:.2f} MB")在这段代码...
1 def memory_usage(): 2 mem_available = psutil.virtual_memory().available 3 mem_process = psutil.Process(os.getpid()).memory_info().rss 4 return round
def memory_usage_psutil(): # return the memory usage in MB import psutil,os process = psutil.Process(os.getpid()) 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 ...
print(f"Memory usage: {process.memory_info().rss / 1024 ** 2:.2f} MB") print_memory_usage() # Print memory usage before and after deleting large objects Outer merge 合并 ### method1 weigth_20240705 = show_model_list('tiger_csi1000_500','20240705').set_index('stock_code').loc[:...
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...
>>> sys.getsizeof(ob) / (1024*1024)3072.0001373291016### Check current memory usageofwhole process (include ob and installed packages, ...) >>> psutil.Process().memory_info().rss / (1024*1024)3234.19140625### Check structureof'ob'(Useful forclassobject) ...
A large string is created in each iteration, causing memory usage to increase. $ python -m memory_profiler mem_prof.py Run the example. The memory_usage FunctionThe memory_usage function from the memory_profiler library is used to get the current memory usage of a process or specific code ...