Summary and Setup
Welcome to Performance Profiling & Optimisation (Python) Training!
The training curriculum for this course is designed for researchers that are writing Python and lack formal computer science training. The curriculum covers how to assess where time is being spent during execution of a Python program, it also provides a high level understanding of how code executes and how this maps to the limiting factors of performance and good practice.
If you are now comfortable using Python, this course may be of interest to supplement and advance your programming knowledge. This course is particularly relevant if you are writing research code and desire greater confidence that your code is both performant and suitable for publication.
This is an all-day course, however it normally finishes by early afternoon.
If you would like to register to take the course, check the registration information.
Learning Objectives
After attending this training, participants will be able to:
- identify the most expensive functions and lines of code using
cprofileandline_profiler. - evaluate code to determine the limiting factors of it’s performance.
- recognise and implement optimisations for common limiting factors of performance.
Prerequisites
Before joining Performance Profiling & Optimisation (Python) Training, participants should be able to:
- implement basic algorithms in Python.
- follow the control flow of Python code, and dry run the execution in their head or on paper.
See the Research Computing Training Hub for other courses to help with learning these skills.
Software Setup
Details
This course uses Python and was developed using Python 3.11, therefore it is recommended that you have a Python 3.11 or newer environment.
You may want to create a new Python virtual environment for the
course, this can be done with your preferred Python environment manager
(e.g. conda, pipenv), the required packages
can all be installed via pip.
The non-core Python packages required by the course are
pytest, snakeviz, line_profiler,
numpy, pandas and matplotlib
which can be installed via pip.
To complete some of the exercises you will need to use a text-editor or Python IDE, so make sure you have your favourite available.