# Check memory usage before conversion print("Memory usage before conversion:") print(df_large.memory_usage().sum()) # Convert to more memory-efficient datatypes df_large['A'] =pd.to_numeric(df_large['A'], downcast='integer') df_large['B'] = pd.to_numeric(df_large['B'], downcas...
%%file demo.py from memory_profiler import profile @profile def addition(): a = [1] * (10 ** 1) b = [2] * (3 * 10 ** 2) sum = a+b return sum 现在,我们可以调用该函数 from demo import addition %memit addition() #Output Line # Mem usage Increment Line Contents === 2 36....
importsubprocess# 调用nvidia-smi命令获取显存占用情况output=subprocess.check_output(['nvidia-smi','--query-gpu=memory.used','--format=csv,nounits,noheader'])# 解析输出结果memory_used=[int(x)forxinoutput.decode().strip().split('\n')]print('GPU显存使用情况:{}'.format(memory_used)) 1. 2...
### Check memory for pandas.DataFrame >>> from sklearn.datasets import load_boston >>> data = load_boston() >>> data = pd.DataFrame(data['data']) >>> print(data.info(verbose=False, memory_usage='deep')) <class 'pandas.core.frame.Data...
def check_memory_leak(): objects = [] for i in range(100000): objects.append(i) return sys.getsizeof(objects) / (1024 * 1024) # MB print("Memory usage before leak:", check_memory_leak()) # 输出:32.5 MB 在这个例子中,我们创建了一个包含大量整数的列表,我们计算了这个列表的内存大小,...
memory_result = subprocess.check_output(memory_command, shell=True).decode().strip() # 转换结果为浮点数 cpu_usage = float(cpu_result) memory_usage = float(memory_result) # 返回 CPU 使用率和内存使用率 return cpu_usage, memory_usage
(work_with_shared_memory,shm.name,shape,dtype)for_inrange(cpu_count())]for_inas_completed(fs):pass# Check memory usagecurrent,peak=tracemalloc.get_traced_memory()print(f"Current memory usage {current/1e6}MB; Peak: {peak/1e6}MB")print(f'Time elapsed: {time.time()-start_time:.2f}s'...
def check_even(numbers): even = [] for num in numbers: if num % 2 == 0: even.append(num*num) return evenif __name__ == '__main__': m1 = memory_profiler.memory_usage() t1 = time.clock() cubes = check_even(range(100000000)) t2 = time.clock() m2 = memory_profiler.memory...
import subprocess subprocess.check_output(['git', 'pull', 'origin', 'main'])subprocess.check_output(['npm', 'install'])subprocess.check_output(['npm', 'run', 'build'])subprocess.check_output(['systemctl', 'restart', 'myapp'])4、监控日志文件脚本：监控指定的日志文件并在关键字出现时发送...
The intended usage of the -s switch is to check the labels' numerical slope over a significant time period for : >0it might mean a memory leak. ~0if 0 or near 0, the memory usage may be considered stable. <0to be interpreted depending on the expected process memory usage patterns, ...