Hey Here's a Magic Message Read It and Pass It on
Getting Started
Magic Commands for Profiling in Jupyter Notebook
Appraise the time and retentiveness complication of your lawmaking within your notebook
Jupyter Notebooks offers dynamic interaction with Python and allows us to create documents mixing code, text, images, and much more than. Notebooks are powered past IPython which provides interactive computing with Python and extends its capabilities in many means. One of them is the addition of Magic Commands.
Introduction to Magic Commands
Magic Commands are succinct solutions to mutual obstacles. Y'all probably encounter them already, maybe without knowing. They can be identified past their prefix % or %%.
Line Magics VS Jail cell Magics
There are two kinds of magic commands: Line Magics (% prefix) and Cell Magics (%% prefix). Line magics operate just on their line when Prison cell Magics operate on their full prison cell. To work, cell magics must exist on the cell first line, even earlier comments!
Permit's take an example to see magic commands at piece of work. One of the easiest ones is %fourth dimension, it measures the execution time of a statement.
By prefixing a statement with %time, we indicate to IPython that nosotros want to know the execution time of this line, the event is printed in the output. Like I said previously, line magics operate on their line then if the line is run multiple times line magic too.
It's not great to go total execution fourth dimension. However, there is an easy solution: %time is also available every bit cell magic. Permit'south endeavour again using %%time.
By prefixing the cell with a jail cell magic IPython know that it musts considers everything inside.
To acquire more about a specific magic command no demand to search the web, everything is available within the Jupyter notebook. Indeed, you tin can use %your_magic_command? to access command documentation.
Magic Commands for Profiling
In that location is enough of magic commands, all useful for different scenarios. Let's explore some of them dedicated to profiling.
%time
The first one is %time, similar showed previously it measures execution time. It'south groovy to know how many time a cell needed to run. Notwithstanding, measure from %time shouldn't be taken as the boilerplate run time, indeed the measure is based on merely ane run and tin vary from 1 to another.
%timeit
For more accuracy, %timeit is the solution. It will run code multiple times to compute mean execution time and its variance.
Yous can see in output vii runs and 100000 loops each, since our statement needs very little fourth dimension to execute IPython volition execute it 100000 times and divide full fourth dimension to become a more than precise measure, so this procedure is repeated 7 times to compute boilerplate time and variance.
IPython will automatically accommodate these values based on execution fourth dimension, fast code volition be run a lot of times, slower code fewer times. Yet, you can as well tell %timeit how many runs yous wish. Indeed, you can pass arguments to magic commands!
Here using -r and -n we precise that we want 20 runs executing the statement 100 times each.
What'southward more, %timeit can also be used as a magic prison cell. It volition then measure the fourth dimension execution of the whole prison cell instead of a single statement.
%prun
A program is composed of numerous statements interacting with each other. You can utilize %timeit to assess that all your elemental code blocks are fast but when running the total script one of them can call another one a tremendous amount of time leading to long execution time. To appraise time performance at the program calibration you need %prun.
To have a ameliorate insight about %prun let's define a small function.
We can see that %prun broken downward our plan into each of its sub-components. For each function nosotros have :
- ncalls: Number of times the function was called
- tottime: Full time passed in the function itself.
- percall: Average time needed past function call (tottime/ncalls).
- cumtime: Total fourth dimension passed inside the function and called sub-functions.
In this script, random is chosen 10000 times for a total execution time of 0.095s. Since random is called within a listing comprehension we can see that list comprehension has a higher cumtime than tottime.
With a more complex program using numerous functions, it becomes difficult to read %prun output. For easier comprehension, the output tin exist sorted on a parameter passed through -s.
In the same way that previously %prun can be used as cell magic when prefixed by %%.
%lprun
Similar %prun, %lprun allows to analyse time consumption of a whole program. However, information technology'southward even more than precise since it assess execution fourth dimension for every lines.
This magic command is not present by default in IPython and need a bit of installation.
The get-go line download the module containing %lprun. The second line imports it. Since magic commands are special commands they are imported with a special command instead of import. %load_ext is the magic control used to import new magic commands!
Note: ! is the abbreviation of %system, the magic command to run terminal command. Recollect when I said that y'all've probably already utilise magic command without knowing ?
Since a program is composed of numerous lines %lprun will non track them all. We demand to specify using -f which functions it must appraise. Multiples functions tin can be tracked by using multiple -f tags.
Here we can see the issue of the study, for each code line we have :
- Hits: Number of times were the line is executed.
- Fourth dimension: Total time used by the line.
- Per Striking: Average time needed by the line(Time/Hittingsouth).
- % Time: Percentage of total tracked time used by this line.
Hither the function is chosen in one case and doesn't contains any loop so each line is striking once. Like we previously nosotros tin see that the matrix cosmos takes most of the time.
%memeit
Here comes the two functions for memory profiling: %memeit and %mprun. They are the memory version of %time and %prun. These functions are not nowadays by default in IPython, we demand to install and load the memory profiler bundle to apply them.
And then, nosotros can use it like %time and %timeit, as a line magic or cell magic.
Here, we can come across that create_and_sum_matrix, increased the total used retentivity of the system by 35MiB upwards to 88MiB.
%mprun
%mprun is the retention version of %lprun. Information technology allows us to track memory consumption line past line inside given functions. However, %mprun has a constraint: it can't work with functions defined in the notebook.
To use it, you need to store your function in a Python file and import information technology. Gladly, you can create a Python file directly from your notebook using the cell magic %%file. Magic commands truly have a solution for everything.
Past prefixing the cell like this, all its code will exist saved in my_file.py. Now, we can import it and utilise %mprun.
We can encounter how memory is allocated and freed during the role. In the beginning, 55.4MiB is already used by the environment. Creating the matrix required 4MiB. However, deleting it only freed 3.2MiB! This is due to Python memory manager policies. If you lot want to learn more than about it, bank check this.
This shows why it'south and then of import to cheque retentiveness complication too. Retentivity used tin can be college than what we remember.
Conclusion
Assessing time and memory complexity is essential to forecast the resource consumption of an awarding. This tin can exist done inside a notebook using magic commands. Magic commands implement solutions to common problems. In this commodity, nosotros introduced half dozen of them, to mensurate time and memory consumption at different scales.
To go farther
To learn more nearly Magic Commands:
- %magic: The magic control to learn more about magic commands.
- Documentation: Online documentation for magic commands.
To acquire more than about IPython:
- Documentation: Online documentation for IPython.
References
[ane] IPython Documention on Magic Commands
[2] J. VanderPlas, Python Information Scientific discipline Handbook, 2016
Virtually me
Hey! I'm Rémi, a last year student in Computer science. I became passionate nigh Data Scientific discipline ii years agone, since and so I spend my time learning and practicing it. Experience free to connect with me on LinkedIn.
Source: https://towardsdatascience.com/magic-commands-for-profiling-in-jupyter-notebook-d2ef00e29a63
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