def monitor_memory(interval=5):while True:memory_info = psutil.virtual_memory() print(f"Memory Usage: {memory_info.percent}%") time.sleep(interval) # 每5秒打印一次内存使用情况 monitor_memory(5) ``` 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 5. 使用matplotlib绘制内存使用情况 为了更直观...
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 +--format=csv,nounits,noheader } GPUtil...
Pandas提供了大量能使我们快速便捷地处理数据的函数和方法。你很快就会发现,它是使Python成为强大而高效的数据分析环境的重要因素之一。本文主要介绍一下Pandas中pandas.DataFrame.memory_usage方法的使用。 原文地址:Python pandas.DataFrame.memory_usage函数方法的使用...
Pandas 纳入了大量库和一些标准的数据模型,提供了高效地操作大型数据集所需的工具。Pandas提供了大量能使我们快速便捷地处理数据的函数和方法。你很快就会发现,它是使Python成为强大而高效的数据分析环境的重要因素之一。本文主要介绍一下Pandas中pandas.DataFrame.memory_usage方法的使用。 Python pandas.DataFrame.memory_u...
float64 rain_1h 48204 non-null float64 snow_1h 48204 non-null float64 clouds_all 48204 non-null int64 weather_main 48204 non-null object weather_description 48204 non-null object traffic_volume 48204 non-null int64 dtypes: float64(3), int64(2), object(3) memory usage: 3.3+ MB ''' ...
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. ...
(3) memory usage: 57.8+ MB None *** 空值user_id 0 goods_id 0 cat 0 behavior 0 time 0 sex 0 addr 0 device 0 price 0 amount 0 dtype: int64 *** 行为项 pv 679668 cart 42714 fav 20601 buy 14582 Name: behavior, dtype: int64 *** 重复值 False 757565 dtype: int64 数据很干净...
<class 'pandas.core.frame.DataFrame'> RangeIndex: 9994446 entries, 0 to 9994445 Data columns (total 7 columns): userid int64 itemid int64 categoryid int64 type object time datetime64[ns] date object hour int64 dtypes: datetime64[ns](1), int64(4), object(2) memory usage: 533.8+ MB ...
viewrawcheck_memory_usage_1.py hostedwith by GitHub 为什么这样一个巨大的列表仅占48字节内存？这是因为range函数返回的类只表现为一个列表。范围比使用实际的数字列表更节省内存。你可以自己使用列表推导创建同一范围内的实际数字列表:import sysmyreallist = [x for x inrange(0, 10000)]print(sys....
Small memory usage Lets you share memory between multiple processes Index creation is separate from lookup (in particular you can not add more items once the tree has been created) Native Python support, tested with 2.7, 3.6, and 3.7.