line_profiler

(OLD REPO) Line-by-line profiling for Python - Current repo ->

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Quick Overview

line_profiler is a Python module that provides line-by-line profiling of functions. It allows developers to analyze the execution time of individual lines within a function, helping to identify performance bottlenecks and optimize code efficiently.

Pros

Provides detailed, line-by-line profiling information
Easy to use with decorators or as a command-line tool
Integrates well with IPython for interactive profiling
Supports both Python 2 and Python 3

Cons

Can introduce some overhead, especially for short-running functions
Limited to profiling at the line level, not suitable for more granular analysis
Requires compilation of C extensions, which may be challenging on some systems
Not actively maintained (last release in 2020)

Code Examples

Using the @profile decorator:

from line_profiler import LineProfiler

@profile
def slow_function():
    total = 0
    for i in range(1000000):
        total += i
    return total

slow_function()

Profiling multiple functions:

def function1():
    # Some code here

def function2():
    # Some code here

lp = LineProfiler()
lp_wrapper = lp(function1)
lp_wrapper()
lp.add_function(function2)
lp.print_stats()

Using with IPython:

%load_ext line_profiler
%lprun -f slow_function slow_function()

Getting Started

Install line_profiler:
```
pip install line_profiler
```

Import and use the @profile decorator:

from line_profiler import LineProfiler

@profile
def your_function():
    # Your code here

your_function()

Run the script with the line profiler:
```
kernprof -l your_script.py
```

View the results:

python -m line_profiler your_script.py.lprof

Competitor Comparisons

line_profiler

2,645

Line-by-line profiling for Python

Pros of line_profiler (pyutils)

More actively maintained with recent updates
Supports Python 3.7+ and PyPy
Includes additional features like memory profiling

Cons of line_profiler (pyutils)

May have compatibility issues with older Python versions
Potentially larger package size due to additional features

Code Comparison

line_profiler (rkern):

@profile
def slow_function(a, b, c):
    ...

lprofiler = LineProfiler()
lprofiler.add_function(slow_function)
lprofiler.run("slow_function(1, 2, 3)")

line_profiler (pyutils):

from line_profiler import LineProfiler

@profile
def slow_function(a, b, c):
    ...

lp = LineProfiler()
lp_wrapper = lp(slow_function)
lp_wrapper(1, 2, 3)
lp.print_stats()

Both repositories provide line-by-line profiling for Python code, but the pyutils version offers more recent updates and additional features. The rkern version may be more suitable for older Python environments or simpler profiling needs. The code usage is similar, with minor differences in initialization and execution of the profiler.

pyinstrument

6,432

🚴 Call stack profiler for Python. Shows you why your code is slow!

Pros of pyinstrument

Provides a more intuitive, hierarchical view of program execution
Minimal overhead, suitable for profiling production code
Supports both synchronous and asynchronous code profiling

Cons of pyinstrument

Less granular than line_profiler, focusing on function-level profiling
May not capture very short-running functions accurately

Code comparison

line_profiler:

@profile
def my_function():
    # Code to profile
    pass

# Run the profiler
kernprof -l script.py

pyinstrument:

from pyinstrument import Profiler

profiler = Profiler()
profiler.start()
# Code to profile
profiler.stop()

profiler.print()

Key differences

line_profiler requires decorating functions and using a separate command-line tool
pyinstrument can be easily integrated into existing code without modifications
line_profiler provides line-by-line timing information
pyinstrument offers a more high-level overview of program execution

Both tools have their strengths, with line_profiler excelling in detailed analysis of specific functions and pyinstrument providing a broader view of overall program performance.

py-spy

12,439

Sampling profiler for Python programs

Pros of py-spy

Non-intrusive profiling: Can profile Python programs without modifying the source code or restarting the application
Low overhead: Minimal impact on the performance of the profiled program
Supports profiling multi-threaded programs and subprocesses

Cons of py-spy

Less detailed output: Provides function-level profiling rather than line-by-line profiling
Limited compatibility: May not work with all Python implementations or on all operating systems

Code comparison

line_profiler:

@profile
def slow_function():
    time.sleep(2)
    print("Function completed")

slow_function()

py-spy:

# No code modification required
# Run from command line:
# py-spy record -o profile.svg -- python your_script.py

Summary

py-spy offers non-intrusive profiling with low overhead, making it suitable for production environments and long-running applications. However, it provides less granular information compared to line_profiler. line_profiler offers more detailed, line-by-line profiling but requires code modification and may have a higher performance impact. The choice between the two depends on the specific profiling needs and the target environment.

scalene

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Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals

Pros of Scalene

Provides more comprehensive profiling, including CPU, GPU, and memory usage
Supports profiling of multi-threaded and multi-process applications
Offers a web-based GUI for visualizing profiling results

Cons of Scalene

May have a higher overhead compared to line_profiler for simple profiling tasks
Requires Python 3.6 or later, while line_profiler supports older Python versions

Code Comparison

Scalene usage:

from scalene import scalene_profiler

@scalene_profiler
def my_function():
    # Your code here

line_profiler usage:

from line_profiler import LineProfiler

lp = LineProfiler()
@lp
def my_function():
    # Your code here

Both tools offer decorators for profiling specific functions, but Scalene provides more detailed information about resource usage across different aspects of the program's execution. While line_profiler focuses primarily on line-by-line CPU time, Scalene offers a broader view of performance metrics, making it more suitable for complex applications with diverse resource requirements.

memory_profiler

4,321

Monitor Memory usage of Python code

Pros of memory_profiler

Focuses on memory usage profiling, providing detailed memory consumption analysis
Offers line-by-line memory usage information for Python scripts
Includes a command-line tool (mprof) for generating memory usage plots

Cons of memory_profiler

Generally slower execution compared to line_profiler
May have less precise timing information for individual lines of code
Requires more setup and configuration for optimal use

Code Comparison

memory_profiler:

from memory_profiler import profile

@profile
def my_func():
    a = [1] * (10 ** 6)
    b = [2] * (2 * 10 ** 7)
    del b
    return a

line_profiler:

@profile
def my_func():
    a = [1] * (10 ** 6)
    b = [2] * (2 * 10 ** 7)
    del b
    return a

The main difference is the import statement for memory_profiler. line_profiler uses a separate command-line tool (kernprof) to run the profiling, so no import is needed in the code itself.

Both tools provide valuable insights for different aspects of performance optimization, with memory_profiler focusing on memory usage and line_profiler on execution time.

profiling

2,966

Was an interactive continuous Python profiler.

Pros of profiling

Supports both line-by-line and function profiling
Provides a live, interactive web interface for real-time profiling
Can profile multi-threaded applications

Cons of profiling

May have higher overhead compared to line_profiler
Less mature and potentially less stable than line_profiler
Requires additional dependencies for the web interface

Code Comparison

line_profiler:

@profile
def my_function():
    # Function code here
    pass

# Run the profiler
kernprof -l script.py

profiling:

from profiling import profile

@profile
def my_function():
    # Function code here
    pass

# Run the profiler
python -m profiling run script.py

Both tools use decorators for profiling specific functions, but profiling offers more flexibility in terms of profiling methods and visualization options. line_profiler is more focused on line-by-line profiling and is generally considered faster and more lightweight. profiling provides a more comprehensive suite of profiling tools, including real-time monitoring, but may have a higher performance impact on the profiled code.

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README

line_profiler and kernprof

line_profiler is a module for doing line-by-line profiling of functions. kernprof is a convenient script for running either line_profiler or the Python standard library's cProfile or profile modules, depending on what is available.

They are available under a BSD license_.

.. _BSD license: https://raw.githubusercontent.com/rkern/line_profiler/master/LICENSE.txt

.. contents::

Installation

Note: As of version 2.1.2, pip install line_profiler does not work. Please install as follows until it is fixed in the next release::

git clone https://github.com/rkern/line_profiler.git
find line_profiler -name '*.pyx' -exec cython {} \;
cd line_profiler
pip install . --user

Releases of line_profiler can be installed using pip::

$ pip install line_profiler

Source releases and any binaries can be downloaded from the PyPI link.

http://pypi.python.org/pypi/line_profiler

To check out the development sources, you can use Git_::

$ git clone https://github.com/rkern/line_profiler.git

You may also download source tarballs of any snapshot from that URL.

Source releases will require a C compiler in order to build line_profiler. In addition, git checkouts will also require Cython_ >= 0.10. Source releases on PyPI should contain the pregenerated C sources, so Cython should not be required in that case.

kernprof is a single-file pure Python script and does not require a compiler. If you wish to use it to run cProfile and not line-by-line profiling, you may copy it to a directory on your PATH manually and avoid trying to build any C extensions.

.. _git: http://git-scm.com/ .. _Cython: http://www.cython.org .. _build and install: http://docs.python.org/install/index.html

line_profiler

The current profiling tools supported in Python 2.7 and later only time function calls. This is a good first step for locating hotspots in one's program and is frequently all one needs to do to optimize the program. However, sometimes the cause of the hotspot is actually a single line in the function, and that line may not be obvious from just reading the source code. These cases are particularly frequent in scientific computing. Functions tend to be larger (sometimes because of legitimate algorithmic complexity, sometimes because the programmer is still trying to write FORTRAN code), and a single statement without function calls can trigger lots of computation when using libraries like numpy. cProfile only times explicit function calls, not special methods called because of syntax. Consequently, a relatively slow numpy operation on large arrays like this, ::

a[large_index_array] = some_other_large_array

is a hotspot that never gets broken out by cProfile because there is no explicit function call in that statement.

LineProfiler can be given functions to profile, and it will time the execution of each individual line inside those functions. In a typical workflow, one only cares about line timings of a few functions because wading through the results of timing every single line of code would be overwhelming. However, LineProfiler does need to be explicitly told what functions to profile. The easiest way to get started is to use the kernprof script. ::

$ kernprof -l script_to_profile.py

kernprof will create an instance of LineProfiler and insert it into the __builtins__ namespace with the name profile. It has been written to be used as a decorator, so in your script, you decorate the functions you want to profile with @profile. ::

@profile
def slow_function(a, b, c):
    ...

The default behavior of kernprof is to put the results into a binary file script_to_profile.py.lprof . You can tell kernprof to immediately view the formatted results at the terminal with the [-v/--view] option. Otherwise, you can view the results later like so::

$ python -m line_profiler script_to_profile.py.lprof

For example, here are the results of profiling a single function from a decorated version of the pystone.py benchmark (the first two lines are output from pystone.py, not kernprof)::

Pystone(1.1) time for 50000 passes = 2.48
This machine benchmarks at 20161.3 pystones/second
Wrote profile results to pystone.py.lprof
Timer unit: 1e-06 s

File: pystone.py
Function: Proc2 at line 149
Total time: 0.606656 s

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
   149                                           @profile
   150                                           def Proc2(IntParIO):
   151     50000        82003      1.6     13.5      IntLoc = IntParIO + 10
   152     50000        63162      1.3     10.4      while 1:
   153     50000        69065      1.4     11.4          if Char1Glob == 'A':
   154     50000        66354      1.3     10.9              IntLoc = IntLoc - 1
   155     50000        67263      1.3     11.1              IntParIO = IntLoc - IntGlob
   156     50000        65494      1.3     10.8              EnumLoc = Ident1
   157     50000        68001      1.4     11.2          if EnumLoc == Ident1:
   158     50000        63739      1.3     10.5              break
   159     50000        61575      1.2     10.1      return IntParIO

The source code of the function is printed with the timing information for each line. There are six columns of information.

* Line #: The line number in the file.

* Hits: The number of times that line was executed.

* Time: The total amount of time spent executing the line in the timer's
  units. In the header information before the tables, you will see a line
  "Timer unit:" giving the conversion factor to seconds. It may be different
  on different systems.

* Per Hit: The average amount of time spent executing the line once in the
  timer's units.

* % Time: The percentage of time spent on that line relative to the total
  amount of recorded time spent in the function.

* Line Contents: The actual source code. Note that this is always read from
  disk when the formatted results are viewed, *not* when the code was
  executed. If you have edited the file in the meantime, the lines will not
  match up, and the formatter may not even be able to locate the function
  for display.

If you are using IPython, there is an implementation of an %lprun magic command which will let you specify functions to profile and a statement to execute. It will also add its LineProfiler instance into the builtins, but typically, you would not use it like that.

For IPython 0.11+, you can install it by editing the IPython configuration file ~/.ipython/profile_default/ipython_config.py to add the 'line_profiler' item to the extensions list::

c.TerminalIPythonApp.extensions = [
    'line_profiler',
]

To get usage help for %lprun, use the standard IPython help mechanism::

In [1]: %lprun?

These two methods are expected to be the most frequent user-level ways of using LineProfiler and will usually be the easiest. However, if you are building other tools with LineProfiler, you will need to use the API. There are two ways to inform LineProfiler of functions to profile: you can pass them as arguments to the constructor or use the add_function(f) method after instantiation. ::

profile = LineProfiler(f, g)
profile.add_function(h)

LineProfiler has the same run(), runctx(), and runcall() methods as cProfile.Profile as well as enable() and disable(). It should be noted, though, that enable() and disable() are not entirely safe when nested. Nesting is common when using LineProfiler as a decorator. In order to support nesting, use enable_by_count() and disable_by_count(). These functions will increment and decrement a counter and only actually enable or disable the profiler when the count transitions from or to 0.

After profiling, the dump_stats(filename) method will pickle the results out to the given file. print_stats([stream]) will print the formatted results to sys.stdout or whatever stream you specify. get_stats() will return LineStats object, which just holds two attributes: a dictionary containing the results and the timer unit.

kernprof

kernprof also works with cProfile, its third-party incarnation lsprof, or the pure-Python profile module depending on what is available. It has a few main features:

* Encapsulation of profiling concerns. You do not have to modify your script
  in order to initiate profiling and save the results. Unless if you want to
  use the advanced __builtins__ features, of course.

* Robust script execution. Many scripts require things like __name__,
  __file__, and sys.path to be set relative to it. A naive approach at
  encapsulation would just use execfile(), but many scripts which rely on
  that information will fail. kernprof will set those variables correctly
  before executing the script.

* Easy executable location. If you are profiling an application installed on
  your PATH, you can just give the name of the executable. If kernprof does
  not find the given script in the current directory, it will search your
  PATH for it.

* Inserting the profiler into __builtins__. Sometimes, you just want to
  profile a small part of your code. With the [-b/--builtin] argument, the
  Profiler will be instantiated and inserted into your __builtins__ with the
  name "profile". Like LineProfiler, it may be used as a decorator, or
  enabled/disabled with `enable_by_count()` and `disable_by_count()`, or
  even as a context manager with the "with profile:" statement.

* Pre-profiling setup. With the [-s/--setup] option, you can provide
  a script which will be executed without profiling before executing the
  main script. This is typically useful for cases where imports of large
  libraries like wxPython or VTK are interfering with your results. If you
  can modify your source code, the __builtins__ approach may be
  easier.

The results of profile script_to_profile.py will be written to script_to_profile.py.prof by default. It will be a typical marshalled file that can be read with pstats.Stats(). They may be interactively viewed with the command::

$ python -m pstats script_to_profile.py.prof

Such files may also be viewed with graphical tools like kcachegrind_ through the converter program pyprof2calltree_ or RunSnakeRun_.

.. _kcachegrind: http://kcachegrind.sourceforge.net/html/Home.html .. _pyprof2calltree: http://pypi.python.org/pypi/pyprof2calltree/ .. _RunSnakeRun: http://www.vrplumber.com/programming/runsnakerun/

Frequently Asked Questions

Why the name "kernprof"?

I didn't manage to come up with a meaningful name, so I named it after myself.
Why not use hotshot instead of line_profile?

hotshot can do line-by-line timings, too. However, it is deprecated and may disappear from the standard library. Also, it can take a long time to process the results while I want quick turnaround in my workflows. hotshot pays this processing time in order to make itself minimally intrusive to the code it is profiling. Code that does network operations, for example, may even go down different code paths if profiling slows down execution too much. For my use cases, and I think those of many other people, their line-by-line profiling is not affected much by this concern.
Why not allow using hotshot from kernprof.py?

I don't use hotshot, myself. I will accept contributions in this vein, though.
The line-by-line timings don't add up when one profiled function calls another. What's up with that?

Let's say you have function F() calling function G(), and you are using LineProfiler on both. The total time reported for G() is less than the time reported on the line in F() that calls G(). The reason is that I'm being reasonably clever (and possibly too clever) in recording the times. Basically, I try to prevent recording the time spent inside LineProfiler doing all of the bookkeeping for each line. Each time Python's tracing facility issues a line event (which happens just before a line actually gets executed), LineProfiler will find two timestamps, one at the beginning before it does anything (t_begin) and one as close to the end as possible (t_end). Almost all of the overhead of LineProfiler's data structures happens in between these two times.

When a line event comes in, LineProfiler finds the function it belongs to. If it's the first line in the function, we record the line number and t_end associated with the function. The next time we see a line event belonging to that function, we take t_begin of the new event and subtract the old t_end from it to find the amount of time spent in the old line. Then we record the new t_end as the active line for this function. This way, we are removing most of LineProfiler's overhead from the results. Well almost. When one profiled function F calls another profiled function G, the line in F that calls G basically records the total time spent executing the line, which includes the time spent inside the profiler while inside G.

The first time this question was asked, the questioner had the G() function call as part of a larger expression, and he wanted to try to estimate how much time was being spent in the function as opposed to the rest of the expression. My response was that, even if I could remove the effect, it might still be misleading. G() might be called elsewhere, not just from the relevant line in F(). The workaround would be to modify the code to split it up into two lines, one which just assigns the result of G() to a temporary variable and the other with the rest of the expression.

I am open to suggestions on how to make this more robust. Or simple admonitions against trying to be clever.
Why do my list comprehensions have so many hits when I use the LineProfiler?

LineProfiler records the line with the list comprehension once for each iteration of the list comprehension.
Why is kernprof distributed with line_profiler? It works with just cProfile, right?

Partly because kernprof.py is essential to using line_profiler effectively, but mostly because I'm lazy and don't want to maintain the overhead of two projects for modules as small as these. However, kernprof.py is a standalone, pure Python script that can be used to do function profiling with just the Python standard library. You may grab it and install it by itself without line_profiler.
Do I need a C compiler to build line_profiler? kernprof.py?

You do need a C compiler for line_profiler. kernprof.py is a pure Python script and can be installed separately, though.
Do I need Cython to build line_profiler?

You should not have to if you are building from a released source tarball. It should contain the generated C sources already. If you are running into problems, that may be a bug; let me know. If you are building from a git checkout or snapshot, you will need Cython to generate the C sources. You will probably need version 0.10 or higher. There is a bug in some earlier versions in how it handles NULL PyObject* pointers.
What version of Python do I need?

Both line_profiler and kernprof have been tested with Python 2.7, and 3.2-3.4.

To Do

cProfile uses a neat "rotating trees" data structure to minimize the overhead of looking up and recording entries. LineProfiler uses Python dictionaries and extension objects thanks to Cython. This mostly started out as a prototype that I wanted to play with as quickly as possible, so I passed on stealing the rotating trees for now. As usual, I got it working, and it seems to have acceptable performance, so I am much less motivated to use a different strategy now. Maybe later. Contributions accepted!

Bugs and Such

Bugs and pull requested can be submitted on GitHub_.

.. _GitHub: https://github.com/rkern/line_profiler

Changes

2.1

* ENH: Add support for Python 3.5 coroutines
* ENH: Documentation updates
* ENH: CI for most recent Python versions (3.5, 3.6, 3.6-dev, 3.7-dev, nightly)
* ENH: Add timer unit argument for output time granularity spec

2.0

BUG: Added support for IPython 5.0+, removed support for IPython <=0.12

1.1

* BUG: Read source files as bytes.

1.0

ENH: kernprof.py is now installed as kernprof.
ENH: Python 3 support. Thanks to the long-suffering Mikhail Korobov for being patient.
Dropped 2.6 as it was too annoying.
ENH: The stripzeros and add_module options. Thanks to Erik Tollerud for contributing it.
ENH: Support for IPython cell blocks. Thanks to Michael Forbes for adding this feature.
ENH: Better warnings when building without Cython. Thanks to David Cournapeau for spotting this.

1.0b3


* ENH: Profile generators.
* BUG: Update for compatibility with newer versions of Cython. Thanks to Ondrej
  Certik for spotting the bug.
* BUG: Update IPython compatibility for 0.11+. Thanks to Yaroslav Halchenko and
  others for providing the updated imports.

1.0b2

BUG: fixed line timing overflow on Windows.
DOC: improved the README.

1.0b1


* Initial release.

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