The Living Thing / Notebooks :

Python debugging profiling and testing

The whole point of this thing is that it should be easy to fix

Reloading edited code

Sometimes it’s complicated to work out how to load some complicated dependency tree of stuff. There is an autoreload extension which in principle reloads everything that has changed.

%load_ext autoreload
%autoreload 2

If you don’t trust it do it manually. Use deepreload. You can even hack traditional reload to be deep.

import builtins
from IPython.lib import deepreload
builtins.reload = deepreload.reload

That didn’t work reliably for me. If you load them both at the same time, stuff gets weird. Don’t do that.

Also, this is incompatible with snakeviz. Errors ensue.

Debugging

interactive debugger

Let’s say there is a line in your code that fails:

1/0

In vanilla python if you want to debug the last exception (the post-mortem debugger) you do:

import pdb; pdb.pm()

and if you want to drop into a debugger from some bit of code, you write:

import pdb; pdb.set_trace()

or in python 3.7+

breakpoint()

and if you want to use a fancier debugger (ipdb is recommended):

import ipdb; ipdb.set_trace()

or:

import ipdb; ipdb.pm()

This ipdb thing doesn’t work in jupyter/ipython, which has some other fancy interaction loop going on.

Here’s the manual way to drop into the ipython debugger from code, according to Christoph Martin and David Hamann:

from IPython.core.debugger import Tracer; Tracer()()      # < 5.1
from IPython.core.debugger import set_trace; set_trace()  # >= v5.1

However, that’s not how you are supposed to do it. Persons of quality supposedly invoke their debuggers via so-called magics, e.g. the %debug magic to set a breakpoint.

%debug [--breakpoint filename:line_number_for_breakpoint]

Without the argument it activates post-mortem mode. Pish posh, who thinks in line-numbers? set_trace wastes less time for humans per default.

And if you want to drop automatically into the post mortem debugger for every error:

%pdb on

1/0

Props to Josh Devlin for explaining this and some other handy tips, and Gaël Varoquaux.

Useful debugger commands

! statement

Execute the (one-line) statement in the context of the current stack frame, even if it mirrors the name of a debugger command This is the most useful command, because the debugger parser is horrible and will always interpret anything it conceivably can as a debugger command instead of a python command, which is confusing and misleading. So preface everything with ! to be safe.

h(elp) [command]

Guess

w(here)

Print your location in current stack

d(own) [count]/up [count]

Move the current frame count (default one) levels down/ in the stack trace (to a newer frame).

b(reak) [([filename:]lineno | function) [, condition]]

The one that is tedious to do manually. Without argument, list all breaks and their metadata.

tbreak [([filename:]lineno | function) [, condition]]

Temporary breakpoint, which is removed automatically when it is first hit.

cl(ear) [filename:lineno | bpnumber [bpnumber …]]

Clear specific or all breakpoints

disable [bpnumber [bpnumber …]]/enable [bpnumber [bpnumber …]]

disable is the same as clear, but you can re-enable

ignore bpnumber [count]

ignore a breakpoint a specified number of times

condition bpnumber [condition]

Set a new condition for the breakpoint

commands [bpnumber]

Specify a list of commands for breakpoint number bpnumber. The commands themselves appear on the following lines. Type end to terminate the command list.

s(tep)

Execute the next line, even if that is inside an invoked function.

n(ext)

Execute the next line in this function.
unt(il) [lineno]

continue to line lineno, or the next line with a highetr number than the current one

r(eturn)

Continue execution until the current function returns.

c(ont(inue))

Continue execution, only stop when a breakpoint is encountered.

j(ump) lineno

Set the next line that will be executed. Only available in the bottom-most frame. It is not possible to jump into weird places like the middle of a for loop.

l(ist) [first[, last]]

List source code for the current file.

ll | longlist

List all source code for the current function or frame.

a(rgs)

Print the argument list of the current function.

p expression

Evaluate the expression in the current context and print its value.

pp expression

Like the p command, except the value of the expression is pretty-printed using the pprint module.

whatis expression

Print the type of the expression.

source expression

Try to get source code for the given object and display it.

display [expression]/undisplay [expression]

Display the value of the expression if it changed, each time execution stops in the current frame.

interact

Start an interactive interpreter (using the code module) whose global namespace contains all the (global and local) names found in the current scope.

alias [name [command]]/unalias name

Create an alias called name that executes command.

q(uit)

Pack up and go home

The alias one needs another look, right? How even does it…

As an example, here are two useful aliases from the manual, for the .pdbrc file:

# Print instance variables (usage `pi classInst`)
alias pi for k in %1.__dict__.keys(): print("%1.",k,"=",%1.__dict__[k])
# Print instance variables in self
alias ps pi self

Memory leaks

Python 3 has tracemalloc built in. this is a powerful python memory analyser, although bare-bones. Mike Lin walks you though it. Benoit Bernard explains various options that run on older pythons, including, most usefully IMO, obgraph which draws you an actual diagram of where the leaking things are. More full features, Pympler provide GUI-backed memory profiling, including the magically handy thing of tracking referrers using its refbrowser.

Code injection

pyrasite injects code into running python processes, which enables more exotic debuggery, and realtime object mutation and stuff and of course, memory and performance profiling.

Profiling

Maybe it’s not crashing, but taking too long? You want a profiler.

Easy mode: built-in profiler

Profile functions using cProfile:

import cProfile as profile
profile.runctx('print(predded.shape)', globals(), locals())

There are also memory allocation tools, although I’ve not used them and suspect they are no longer current.

Now visualise them using… uh… let me come back to that.

fancy/hip: py-spy

py-spy

[…] lets you visualize what your Python program is spending time on without restarting the program or modifying the code in any way. Py-Spy is extremely low overhead: it is written in Rust for speed and doesn’t run in the same process as the profiled Python program, nor does it interrupt the running program in any way. This means Py-Spy is safe to use against production Python code.[…]

This project aims to let you profile and debug any running Python program, even if the program is serving production traffic.[…]

Py-spy works by directly reading the memory of the python program using the process_vm_readv system call on Linux, the vm_read call on OSX or the ReadProcessMemory call on Windows.

Figuring out the call stack of the Python program is done by looking at the global PyInterpreterState variable to get all the Python threads running in the interpreter, and then iterating over each PyFrameObject in each thread to get the call stack.

Native ipython can run profiler magically:

%%prun -D somefile.prof
files = glob.glob('*.txt')
for file in files:
    with open(file) as f:
        print(hashlib.md5(f.read().encode('utf-8')).hexdigest())

snakeviz includes a handy magic to automatically save stats and launch the profiler. (Gotcha: you have to have the snakeviz cli already on the path when you launch ipython.)

%load_ext snakeviz
%%snakeviz
files = glob.glob('*.txt')
for file in files:
    with open(file) as f:
        print(hashlib.md5(f.read().encode('utf-8')).hexdigest())

This is incompatible with autoreload and gives weird errors if you run them both in the same session.

Visualising profiles

Testing

Too many bike sheds.

There are a lot of frameworks. The most common seem to be unittest, py.test and nose.