Overview¶
The Python Performance Analysis Library (py-pal) is a profiling tool for the Python programming language. With py-pal one can approximate the time complexity (big O notation) of Python functions in an empirical way. The arguments of the function and the executed opcodes serve as a basis for the analysis.
To the docs.
Installation¶
Requirements¶
- An installation of the CPython implementation of the Python programming language of version greater or equal to 3.7
- A compiler for the C/C++ programming language:
- On Microsoft Windows, we use the Buildtools für Visual Studio 2019:
https://visualstudio.microsoft.com/de/thank-you-downloading-visual-studio/?sku=BuildTools&rel=16
On Linux, any C compiler supported by Cython e.g. g++
Install py-pal via pip by running:¶
This project requires CPython and a C compiler to run. Install CPython >= 3.7, then install py-pal by running:
pip install py-pal
or
python -m pip install py-pal
Command line usage of the py-pal module¶
python -m py_pal <target-module/file>
or
py-pal <target-module/file>
There are multiple aliases to the same command: py-pal, py_pal and pypal. If py-pal is executed this way, all functions called in the code are captured and analyzed. The output is in the form of a pandas data frame.
See the help message:
py-pal -h
py-pal can perform cost analysis on a line-by-line basis:
py-pal <file> -l/–line
The –separate flag can be used to examine the cost function of individual arguments (caution: this function assumes the independence of the arguments):
py-pal <file> -s/–separate
The output of the results can be noisy, to limit this you can use –filter-function to filter the desired functions from the result. Regular expressions are supported:
py-pal <file> -ff/–filter-function .*_sort
Similarly, the result can also be filtered by modules with –filter-module, e.g. to exclude importlib modules
py-pal <file> -fm/–filter-module “^(?!<frozen.*>).*”
To save the results in a specified folder use –out:
py-pal <file> -o/–out results
The output format can be changed with –format:
py-pal <file> -o/–out results –format json
With the additional specification of the –plot flag, the cost functions of the result set are stored as images:
py-pal <file> -o/–out results -p/–plot
For the –log-level flag see the development docs.
Example, creating plots for selected functions:
py-pal tests/examples/sort.py -o results -p -f sort
Programmatic usage of the py-pal module¶
To profile a single function and get the complexity estimate there is profile_function.
from py_pal.core import profile_function
from py_pal.data_collection.opcode_metric import OpcodeMetric
from py_pal.datagen import gen_random_growing_lists
from algorithms.sort import bubble_sort
profile_function(OpcodeMetric(), gen_random_growing_lists(), bubble_sort)
The profile decorator:
from py_pal.core import profile, DecoratorStore
@profile
def test():
pass
# Must be called at some point
test()
estimator = AllArgumentEstimator(DecoratorStore.get_call_stats(), DecoratorStore.get_opcode_stats())
res = estimator.export()
By using the profile decorator, it is possible to annotate Python functions such that only the annotated Python functions will be profiled. It acts similar to a whitelist filter.
Another possibility is to use the context-manager protocol:
from py_pal.analysis.estimator import AllArgumentEstimator
from py_pal.data_collection.tracer import Tracer
with Tracer() as t:
pass
estimator = AllArgumentEstimator(t.get_call_stats(), t.get_opcode_stats())
res = estimator.export()
# Do something with the resulting DataFrame
print(res)
The most verbose way to use the py-pal API:
from py_pal.analysis.estimator import AllArgumentEstimator
from py_pal.data_collection.tracer import Tracer
t = Tracer()
t.trace()
# Your function
pass
t.stop()
estimator = AllArgumentEstimator(t.get_call_stats(), t.get_opcode_stats())
res = estimator.export()
# Do something with the resulting DataFrame
print(res)
All examples instantiate a tracer object that is responsible for collecting the data. After execution, the collected data is passed to the analysis module. Finally, an estimate of the asymptotic runtime of the functions contained in the code is obtained.
Modes¶
In the current version py-pal offers only the profiling mode. Although py_pal.datagen offers some functions for
generating inputs, py-pal must be combined with appropriate test cases to realize a performance testing mode. An
automatic detection and generation of appropriate test inputs does not exist at the moment.
Limitations¶
The profiling approach implemented by the py-pal modules does not distinguish between different threads executing a Python function. Actually it is a major problem to profile a Python script which makes use of threads. The bytecode counting strategy will increase all counters of Python functions on the current call stack no matter what threads is executing it. Thus, the data points will not be accurate to what really happened during the profiled execution of the script.