tuna is a Python profile viewer inspired by the amazing SnakeViz.

Create a runtime profile with ``` python -mcProfile -o program.prof yourfile.py ``` or an import profile with ``` python -X importprofile yourfile.py 2> import.log ``` and show it with ``` tuna program.prof ```

Programming language: Python
License: GNU General Public License v3.0 only
Latest version: v0.5.2

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tuna is a modern, lightweight Python profile viewer inspired by SnakeViz. It handles runtime and import profiles, has minimal dependencies, uses d3 and bootstrap, and avoids certain errors present in SnakeViz (see below) and is faster, too.

Create a runtime profile with

python -mcProfile -o program.prof yourfile.py

or an import profile with

python -X importtime yourfile.py 2> import.log

and show it with

tuna program.prof

Why tuna doesn't show the whole call tree

The whole timed call tree cannot be retrieved from profile data. Python developers made the decision to only store parent data in profiles because it can be computed with little overhead. To illustrate, consider the following program.

import time

def a(t0, t1):

def b():
    a(1, 4)

def c(t):

def d(t):

if __name__ == "__main__":
    a(4, 1)

The root process (__main__) calls a() which spends 4 seconds in c() and 1 second in d(). __main__ also calls b() which calls a(), this time spending 1 second in c() and 4 seconds in d(). The profile, however, will only store that c() spent a total of 5 seconds when called from a(), and likewise d(). The information that the program spent more time in c() when called in root -> a() -> c() than when called in root -> b() -> a() -> c() is not present in the profile.

tuna only displays the part of the timed call tree that can be deduced from the profile. SnakeViz, on the other hand, tries to construct the entire call tree, but ends up providing lots of wrong timings.

SnakeViz output. Wrong. tuna output. Only shows what can be retrieved from the profile.


tuna is available from the Python Package Index, so simply do

pip install tuna

to install.


To run the tuna unit tests, check out this repository and type


IPython magics

tuna includes a tuna line / cell magic which can be used as a drop-in replacement for the prun magic. Simply run %load_ext tuna to load the magic and then call it like %tuna sleep(3) or


prun is still used to do the actual profiling and then the results are displayed in the notebook.


After forking and cloning the repository, make sure to run make dep to install additional dependencies (bootstrap and d3) which aren't stored in the repo.


This software is published under the GPLv3 license.

*Note that all licence references and agreements mentioned in the tuna README section above are relevant to that project's source code only.