☠ This project is not maintained anymore. We highly recommend switching to py-spy which provides better performance and usability.

The profiling package is an interactive continuous Python profiler. It is inspired from Unity 3D profiler. This package provides these features:

• Profiling statistics keep the frame stack.
• An interactive TUI profiling statistics viewer.
• Provides both of statistical and deterministic profiling.
• Utilities for remote profiling.
• Thread or greenlet aware CPU timer.
• Supports Python 2.7, 3.3, 3.4 and 3.5.
• Currently supports only Linux.

Install the latest release via PyPI:

$ pip install profiling

To profile a single program, simply run the profiling command:

$ profiling your-program.py

Then an interactive viewer will be executed:

If your program uses greenlets, choose greenlet timer:

$ profiling --timer=greenlet your-program.py

With --dump option, it saves the profiling result to a file. You can browse the saved result by using the view subcommand:

$ profiling --dump=your-program.prf your-program.py
$ profiling view your-program.prf

If your script reads sys.argv, append your arguments after --. It isolates your arguments from the profiling command:

$ profiling your-program.py -- --your-flag --your-param=42

If your program has a long life time like a web server, a profiling result at the end of program is not helpful enough. Probably you need a continuous profiler. It can be achived by the live-profile subcommand:

$ profiling live-profile webserver.py

See a demo:

There's a live-profiling server also. The server doesn't profile the program at ordinary times. But when a client connects to the server, it starts to profile and reports the results to the all connected clients.

Start a profling server by the remote-profile subcommand:

$ profiling remote-profile webserver.py --bind 127.0.0.1:8912

And also run a client for the server by the view subcommand:

$ profiling view 127.0.0.1:8912

TracingProfiler, the default profiler, implements a deterministic profiler for deep call graph. Of course, it has heavy overhead. The overhead can pollute your profiling result or can make your application to be slow.

In contrast, SamplingProfiler implements a statistical profiler. Like other statistical profilers, it also has only very cheap overhead. When you profile you can choose it by just --sampling (shortly -S) option:

$ profiling live-profile -S webserver.py
                         ^^

Do you use timeit to check the performance of your code?

$ python -m timeit -s 'from trueskill import *' 'rate_1vs1(Rating(), Rating())'
1000 loops, best of 3: 722 usec per loop

If you want to profile the checked code, simply use the timeit subcommand:

$ profiling timeit -s 'from trueskill import *' 'rate_1vs1(Rating(), Rating())'
  ^^^^^^^^^

You can also profile your program by profiling.tracing.TracingProfiler or profiling.sampling.SamplingProfiler directly:

from profiling.tracing import TracingProfiler

# profile your program.
profiler = TracingProfiler()
profiler.start()
...  # run your program.
profiler.stop()

# or using context manager.
with profiler:
    ...  # run your program.

# view and interact with the result.
profiler.run_viewer()
# or save profile data to file
profiler.dump('path/to/file')

• q - Quit.
• space - Pause/Resume.
• \ - Toggle layout between NESTED and FLAT.
• ↑ and ↓ - Navigate frames.
• → - Expand the frame.
• ← - Fold the frame.
• > - Go to the hotspot.
• esc - Defocus.
• [ and ] - Change sorting column.

• FUNCTION
  1. The function name with the code location. (e.g. my_func (my_code.py:42), my_func (my_module:42))
  2. Only the location without line number. (e.g. my_code.py, my_module)

• CALLS - Total call count of the function.
• OWN (Exclusive Time) - Total spent time in the function excluding sub calls.
• /CALL after OWN - Exclusive time per call.
• % after OWN - Exclusive time per total spent time.
• DEEP (Inclusive Time) - Total spent time in the function.
• /CALL after DEEP - Inclusive time per call.
• % after DEEP - Inclusive time per total spent time.

• OWN (Exclusive Samples) - Number of samples which are collected during the direct execution of the function.
• % after OWN - Exclusive samples per number of the total samples.
• DEEP (Inclusive Samples) - Number of samples which are collected during the excution of the function.
• % after DEEP - Inclusive samples per number of the total samples.

There are some additional requirements to run the test code, which can be installed by running the following command.

$ pip install $(python test/fit_requirements.py test/requirements.txt)

Then you should be able to run pytest.

$ pytest -v

• Seungmyeong Yang who suggested this project.
• Pavel who inspired to implement -m option.

Written by Heungsub Lee at What! Studio in Nexon, and distributed under the BSD 3-Clause license.