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import gc
import datetime
import pynvml
import torch
import numpy as np
class MemTracker(object):
Class used to track pytorch memory usage
frame: a frame to detect current py-file runtime
detail(bool, default True): whether the function shows the detail gpu memory usage
path(str): where to save log file
verbose(bool, default False): whether show the trivial exception
device(int): GPU number, default is 0
def __init__(self, frame, detail=True, path='', verbose=False, device=0):
self.frame = frame
self.print_detail = detail
self.last_tensor_sizes = set()
self.gpu_profile_fn = path + f'{}-gpu_mem_track.txt'
self.verbose = verbose
self.begin = True
self.device = device
self.func_name = frame.f_code.co_name
self.filename = frame.f_globals["__file__"]
if (self.filename.endswith(".pyc") or
self.filename = self.filename[:-1]
self.module_name = self.frame.f_globals["__name__"]
self.curr_line = self.frame.f_lineno
def get_tensors(self):
for obj in gc.get_objects():
if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(
tensor = obj
if tensor.is_cuda:
yield tensor
except Exception as e:
if self.verbose:
print('A trivial exception occured: {}'.format(e))
def track(self):
Track the GPU memory usage
handle = pynvml.nvmlDeviceGetHandleByIndex(self.device)
meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
self.curr_line = self.frame.f_lineno
where_str = self.module_name + ' ' + self.func_name + ':' + ' line ' + str(self.curr_line)
with open(self.gpu_profile_fn, 'a+') as f:
if self.begin:
f.write(f"GPU Memory Track | {} |"
f" Total Used Memory:{meminfo.used/1000**2:<7.1f}Mb\n\n")
self.begin = False
if self.print_detail is True:
ts_list = [tensor.size() for tensor in self.get_tensors()]
new_tensor_sizes = {(type(x), tuple(x.size()), ts_list.count(x.size()),*4/1000**2)
for x in self.get_tensors()}
for t, s, n, m in new_tensor_sizes - self.last_tensor_sizes:
f.write(f'+ | {str(n)} * Size:{str(s):<20} | Memory: {str(m*n)[:6]} M | {str(t):<20}\n')
for t, s, n, m in self.last_tensor_sizes - new_tensor_sizes:
f.write(f'- | {str(n)} * Size:{str(s):<20} | Memory: {str(m*n)[:6]} M | {str(t):<20} \n')
self.last_tensor_sizes = new_tensor_sizes
f.write(f"\nAt {where_str:<50}"
f"Total Used Memory:{meminfo.used/1000**2:<7.1f}Mb\n\n")
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