scalene python cpu&gpu 性能分析工具
scalene 使用一个python cpu&gpu 性能分析工具，同时也支持内存的分析，同时还提供了基于ai的智能优化推荐
包含的一些特性
- cli 支持多种输出格式
- 包含了一个web-gui
- 基于ai 的智能提示
参考使用
- 安装
pip install scalene
- 包含的cli
usage: scalene [-h] [--version] [--column-width COLUMN_WIDTH] [--outfile OUTFILE] [--html] [--json] [--cli] [--stacks] [--web] [--no-browser] [--viewer] [--reduced-profile]
[--profile-interval PROFILE_INTERVAL] [--cpu] [--cpu-only] [--gpu] [--memory] [--profile-all] [--profile-only PROFILE_ONLY] [--profile-exclude PROFILE_EXCLUDE]
[--use-virtual-time] [--cpu-percent-threshold CPU_PERCENT_THRESHOLD] [--cpu-sampling-rate CPU_SAMPLING_RATE] [--allocation-sampling-window ALLOCATION_SAMPLING_WINDOW]
[--malloc-threshold MALLOC_THRESHOLD] [--program-path PROGRAM_PATH] [--memory-leak-detector] [--on | --off]
Scalene: a high-precision CPU and memory profiler, version 1.5.36 (2024.03.04)
https://github.com/plasma-umass/scalene
command-line:
% scalene [options] your_program.py [--- --your_program_args]
or
% python3 -m scalene [options] your_program.py [--- --your_program_args]
in Jupyter, line mode:
%scrun [options] statement
in Jupyter, cell mode:
%%scalene [options]
your code here
options:
-h, --help show this help message and exit
--version prints the version number for this release of Scalene and exits
--column-width COLUMN_WIDTH
Column width for profile output (default: 132)
--outfile OUTFILE file to hold profiler output (default: stdout)
--html output as HTML (default: web)
--json output as JSON (default: web)
--cli forces use of the command-line
--stacks collect stack traces
--web opens a web tab to view the profile (saved as 'profile.html')
--no-browser doesn't open a web tab; just saves the profile ('profile.html')
--viewer opens the Scalene web UI.
--reduced-profile generate a reduced profile, with non-zero lines only (default: False)
--profile-interval PROFILE_INTERVAL
output profiles every so many seconds (default: inf)
--cpu profile CPU time (default: True )
--cpu-only profile CPU time (deprecated: use --cpu )
--gpu profile GPU time and memory (default: False )
--memory profile memory (default: True )
--profile-all profile all executed code, not just the target program (default: only the target program)
--profile-only PROFILE_ONLY
profile only code in filenames that contain the given strings, separated by commas (default: no restrictions)
--profile-exclude PROFILE_EXCLUDE
do not profile code in filenames that contain the given strings, separated by commas (default: no restrictions)
--use-virtual-time measure only CPU time, not time spent in I/O or blocking (default: False)
--cpu-percent-threshold CPU_PERCENT_THRESHOLD
only report profiles with at least this percent of CPU time (default: 1%)
--cpu-sampling-rate CPU_SAMPLING_RATE
CPU sampling rate (default: every 0.01s)
--allocation-sampling-window ALLOCATION_SAMPLING_WINDOW
Allocation sampling window size, in bytes (default: 10485767 bytes)
--malloc-threshold MALLOC_THRESHOLD
only report profiles with at least this many allocations (default: 100)
--program-path PROGRAM_PATH
The directory containing the code to profile (default: the path to the profiled program)
--memory-leak-detector
EXPERIMENTAL: report likely memory leaks (default: True)
--on start with profiling on (default)
--off start with profiling off
When running Scalene in the background, you can suspend/resume profiling
for the process ID that Scalene reports. For example:
% python3 -m scalene yourprogram.py &
Scalene now profiling process 12345
to suspend profiling: python3 -m scalene.profile --off --pid 12345
to resume profiling: python3 -m scalene.profile --on --pid 12345
(venv) (venv) (base) ➜ dremio-sqlalchemy scalene app.py
297511 E8:CB:ED:5A:52:41
(venv) (venv) (base) ➜ dremio-sqlalchemy scalene --help
usage: scalene [-h] [--version] [--column-width COLUMN_WIDTH] [--outfile OUTFILE] [--html] [--json] [--cli] [--stacks] [--web] [--no-browser] [--viewer] [--reduced-profile]
[--profile-interval PROFILE_INTERVAL] [--cpu] [--cpu-only] [--gpu] [--memory] [--profile-all] [--profile-only PROFILE_ONLY] [--profile-exclude PROFILE_EXCLUDE]
[--use-virtual-time] [--cpu-percent-threshold CPU_PERCENT_THRESHOLD] [--cpu-sampling-rate CPU_SAMPLING_RATE] [--allocation-sampling-window ALLOCATION_SAMPLING_WINDOW]
[--malloc-threshold MALLOC_THRESHOLD] [--program-path PROGRAM_PATH] [--memory-leak-detector] [--on | --off]
Scalene: a high-precision CPU and memory profiler, version 1.5.36 (2024.03.04)
https://github.com/plasma-umass/scalene
command-line:
% scalene [options] your_program.py [--- --your_program_args]
or
% python3 -m scalene [options] your_program.py [--- --your_program_args]
in Jupyter, line mode:
%scrun [options] statement
in Jupyter, cell mode:
%%scalene [options]
your code here
options:
-h, --help show this help message and exit
--version prints the version number for this release of Scalene and exits
--column-width COLUMN_WIDTH
Column width for profile output (default: 132)
--outfile OUTFILE file to hold profiler output (default: stdout)
--html output as HTML (default: web)
--json output as JSON (default: web)
--cli forces use of the command-line
--stacks collect stack traces
--web opens a web tab to view the profile (saved as 'profile.html')
--no-browser doesn't open a web tab; just saves the profile ('profile.html')
--viewer opens the Scalene web UI.
--reduced-profile generate a reduced profile, with non-zero lines only (default: False)
--profile-interval PROFILE_INTERVAL
output profiles every so many seconds (default: inf)
--cpu profile CPU time (default: True )
--cpu-only profile CPU time (deprecated: use --cpu )
--gpu profile GPU time and memory (default: False )
--memory profile memory (default: True )
--profile-all profile all executed code, not just the target program (default: only the target program)
--profile-only PROFILE_ONLY
profile only code in filenames that contain the given strings, separated by commas (default: no restrictions)
--profile-exclude PROFILE_EXCLUDE
do not profile code in filenames that contain the given strings, separated by commas (default: no restrictions)
--use-virtual-time measure only CPU time, not time spent in I/O or blocking (default: False)
--cpu-percent-threshold CPU_PERCENT_THRESHOLD
only report profiles with at least this percent of CPU time (default: 1%)
--cpu-sampling-rate CPU_SAMPLING_RATE
CPU sampling rate (default: every 0.01s)
--allocation-sampling-window ALLOCATION_SAMPLING_WINDOW
Allocation sampling window size, in bytes (default: 10485767 bytes)
--malloc-threshold MALLOC_THRESHOLD
only report profiles with at least this many allocations (default: 100)
--program-path PROGRAM_PATH
The directory containing the code to profile (default: the path to the profiled program)
--memory-leak-detector
EXPERIMENTAL: report likely memory leaks (default: True)
--on start with profiling on (default)
--off start with profiling off
When running Scalene in the background, you can suspend/resume profiling
for the process ID that Scalene reports. For example:
% python3 -m scalene yourprogram.py &
Scalene now profiling process 12345
to suspend profiling: python3 -m scalene.profile --off --pid 12345
to resume profiling: python3 -m scalene.profile --on --pid 12345
- 使用
scalene app.py
- 效果
说明
scalene 同时还有一个与比较工具的比较，scalene 支持的功能还是很多的，很值得试用下，尤其是ai 开发的场景
