memray python 内存profiler 工具简单试用
memray python 内存profiler 工具，功能还是很强大的，以下是一个简单使用
参考使用
- 安装
pip install memray
- 支持的cli
usage: memray [-h] [-v] [-V] {run,flamegraph,table,live,tree,parse,summary,stats,transform,attach,detach}
Memory profiler for Python applications
Run `memray run` to generate a memory profile report, then use a reporter command
such as `memray flamegraph` or `memray table` to convert the results into HTML.
Example:
$ python3 -m memray run -o output.bin my_script.py
$ python3 -m memray flamegraph output.bin
positional arguments:
{run,flamegraph,table,live,tree,parse,summary,stats,transform,attach,detach}
Mode of operation
run Run the specified application and track memory usage
flamegraph Generate an HTML flame graph for peak memory usage
table Generate an HTML table with all records in the peak memory usage
live Remotely monitor allocations in a text-based interface
tree Generate a tree view in the terminal for peak memory usage
parse Debug a results file by parsing and printing each record in it
summary Generate a terminal-based summary report of the functions that allocate most memory
stats Generate high level stats of the memory usage in the terminal
transform Generate reports files in different formats
attach Begin tracking allocations in an already-started process
detach End the tracking started by a previous ``memray attach`` call
options:
-h, --help show this help message and exit
-v, --verbose Increase verbosity. Option is additive and can be specified up to 3 times
-V, --version Displays the current version of Memray
Please submit feedback, ideas, and bug reports by filing a new issue at
https://github.com/bloomberg/memray/issues
- 参考使用
// 生成结果数据
python -m memray run -o output.bin app.py
// 通过火焰图查看数据
python -m memray flamegraph output.bin
- 效果
说明
memray 与以前介绍的scalene 都是很不错的工具，对于性能分析场景，都值得试用下
