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Time And Memory Profile Parser

Project description

TAMPPA: Time And Memory Profile PArser


As a maiden attampt, I hope to make it super useful for the community. Please report bugs and make pull requests to improve it.


Introduction:

TAMPPA is a supporting package for the popular profilers

Both the packages do an excellent job by providing profiling results on the terminal.

Total time: 0.181071 s
File: main.py
Function: linearRegressionfit at line 35

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
    35                                           @profile
    36                                           def linearRegressionfit(Xt,Yt,Xts,Yts):
    37         1         52.0     52.0      0.1      lr=LinearRegression()
    38         1      28942.0  28942.0     75.2      model=lr.fit(Xt,Yt)
    39         1       1347.0   1347.0      3.5      predict=lr.predict(Xts)
    40                                           
    41         1       4924.0   4924.0     12.8      print("train Accuracy",lr.score(Xt,Yt))
    42         1       3242.0   3242.0      8.4      print("test Accuracy",lr.score(Xts,Yts))

But, there seems to be no method to get these stats in a exportable file that can be used with flexibility.

On dumping the logs to a .txt file still requires an individual to parse data from the text by self and then convert the content into a .csv file; which is a common format for sharing statistical data and plotting using MATPLOTLIB.

This is exactly what TAMPPA does ! It outputs one .csv file per function and another text file func_names.txt and again_func_names.txt for accessing these files easily.

Pre-requisites:

Note: Both the parsers need a .txt file to parse results from

  • Run both the profilers or the profiler whose results you need as a csv, and save the logs on the console to a .txt file. For e.g saving the memory profiling results of the python application mainm.py and saving the results to mem_res_1.txt
$ python -m memory_profiler mainm.py > mem_res_1.txt
  • Avoid printing anything on the console. Try it with python main.py and nothing should be printed to the console. So, comment out all the print and log statements.

Installation

Any particular release can be installed using pip:

$ pip install tamppa

To enter development mode,

$ git clone https://github.com/pra-dan/TAMPPA.git

Usage

Refer to the following once the Installation is over.

Time Profile Parser

Initially, if we have only the .txt file.

.
└── tim_prof_results.txt
0 directories, 1 file

Run tim_parse or time parser, in a Python environment ($ python)

>>> from tamppa import tim_parse
>>> tim_parse("tim_prof_results.txt")

On successful execution, the lonely directory is populated as

.
├── again_func_names.txt
├── import_data_tim_.csv
├── linearRegressionfit_tim_.csv
├── parse_data_tim_.csv
├── randForestRegressorfit_tim_.csv
└── tim_prof_results.txt

0 directories, 6 files

Additionally, a plot is also generated as mem_res

Memory Profile Parser

Similarly, if we have only the .txt file for the memory_profiler.

.
└── mem_res_1.txt

0 directories, 1 file

Run mem_parse or time parser, in a Python environment ($ python)

>>> from tamppa import mem_parse
>>> mem_parse("mem_res_1.txt")

On successful execution, the lonely directory is populated as

.
├── func_names.txt
├── function_wise_time_results.csv
├── import_data_mem_.csv
├── linearRegressionfit_mem_.csv
├── mem_res_1.txt
├── parse_data_mem_.csv
└── randForestRegressorfit_mem_.csv

0 directories, 7 files

Additionally, a plot is also generated as mem_res

TODOs:

  • [ ] (Provide the entire package a executable-like interface; such that the parsers can be called simply as $ mem_parse file.txt -plot true)

  • [ ] (Add flags to toggle plots for both parsers)

References:

Project details


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