### 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...
# 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...
Convert into numpy recarray to preserve the dtypesnp_array=df.to_records(index=False)deldfshape,dtype=np_array.shape,np_array.dtypeprint(f"np_array's size={np_array.nbytes/1e6}MB")# With shared memory# Start tracking memory usagetracemalloc.start()start_time=time.time()withSharedMemoryMana...
%%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....
import psutil cpu_percent = psutil.cpu_percent()memory_percent = psutil.virtual_memory().percentdisk_percent = psutil.disk_usage('/').percent print(f'CPU: {cpu_percent}%')print(f'Memory: {memory_percent}%')print(f'Disk: {disk_percent}%')9、自动化SSH登录脚本：使用paramiko模块自动化SSH...
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,如果这个值随着时间的推移而增长,那么可能存在内存泄漏问题,为了解决这些问题,我们需要检查程序中的循环引用、全局变量等可能导致内存泄漏的地方。
GetGPUsGetMemoryUsageShowResult 类图示例: classDiagram class GPUtil { +getGPUs() } class torch.cuda { +is_available() +device(device) +memory_allocated(device) } class subprocess { +check_output(cmd) } class nvidia-smi { +--query-gpu=memory.used ...
cubes = check_even(range(100000000)) t2 = time.clock() m2 = memory_profiler.memory_usage() time_diff = t2 - t1 mem_diff = m2[0] - m1[0] print(f"It took {time_diff} Secsand {mem_diff} Mb to execute this method") 运行后,上述代码的输出如下: It took2.9999999995311555e-05 Secs...
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
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, ...