usage=resource.getrusage(resource.RUSAGE_SELF)print(f"Total memory usage:{usage.ru_maxrss}bytes") 1. 2. 3. 4. 在这个示例中,usage.ru_maxrss表示当前Python程序占用的最大物理内存量,单位为字节。 监控内存使用情况 除了查看当前内存情况,我们还可以通过第三方库如psutil来监控Python程序的内存使用情况。psu...
importpsutil mem=psutil.virtual_memory()print(f"Total memory:{mem.total}bytes")print(f"Available memory:{mem.available}bytes")print(f"Used memory:{mem.used}bytes")print(f"Memory usage percentage:{mem.percent}%") 1. 2. 3. 4. 5. 6. 7. 这段代码使用psutil.virtual_memory()方法获取系统...
def get_cpu_usage(): return psutil.cpu_percent(interval=1) # 获取内存总空间和内存占用空间 def get_memory_usage(): total_memory = psutil.virtual_memory().total used_memory = psutil.virtual_memory().used return total_memory, used_memory # 获取系统磁盘总空间和系统磁盘占用空间 def get_disk_...
cpu_usage = self.get_cpu_usage() total_memory, used_memory = self.get_memory_usage() total_disk_space, used_disk_space = self.get_disk_usage() # 构建系统信息的字典 system_info = { 'cpu_usage': cpu_usage, 'memory_total': total_memory, 'memory_used': used_memory, 'disk_space_to...
%%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....
+ | 1 * Size:(1000,) | Memory: 0.004 M | <class 'torch.nn.parameter.Parameter'> At __main__ <module>: line 15 Total Used Memory:1387.5 Mb 通过上面的报告,很容易发现一个问题。 首先我们知道VGG19所有层的权重大小加起来大约是548M(这个数值来源于Pytorch官方提供的VGG19权重文件大小),我们将...
import psutil cpu_percent = psutil.cpu_percent(interval=1) print(f'CPU usage: {cpu_percent}%') 2、获取内存信息 我们可以使用psutil.virtual_memory()来获取系统的内存使用情况。 import psutil mem_info = psutil.virtual_memory() print(f'Total memory: {mem_info.total / (1024**3):.2f} GB')...
import memory_profiler %load_ext memory_profiler %mprun -f convert_cms_f convert_cms_f(1000, 'f') convert_cms_f函数在单独的文件中定义,然后导入。结果如下: Line # Mem usage Increment Occurrences Line Contents === 1 63.7 MiB 63.7 MiB 1 def convert_cms_f(cm, unit='m'): 2 ''' 3...
print 'Memoryusage: {0}(MB)'.format(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024) print "Mostcommontypes:" objgraph.show_most_common_types() print "heapis:" print "{0}".format(h) by_refs = h.byrcs print "byreferences: {0}".format(by_refs) ...
You can combine both theinclude-childrenandmultiprocessflags to show the total memory of the program as well as each child individually. If using the API directly, note that the return frommemory_usagewill include the child memory in a nested list along with the main process memory. ...