QUB Development Weeks: High Performance Python
Code and more for the QUB Development Weeks event 'High Performance Python'
Getting Started:
- Check if Anaconda is installed - this isn't essential in general for using Python, as there is other ways to set up and use Python, but to avoid runing into versioning problems between different Python / library distributions I'm standardising this course with conda environments.
This means you'll be installing the same version of Python as I'm using as I give this course, as well as the same versions of the same external packages that I use - in a temporary virtual environment that won't mess up any other Python installs you have.
To check if anaconda is installed properly, open a terminal (Windows - search Command Prompt or git-bash, MacOS - search terminal, linux - you know how)
In the terminal run "conda list". If you get a list of packages, or even anything other than "no command found" or similar - you're all good!
Close the terminal now.
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If not already installed, download and install Anaconda
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At this point I'll assume I've shown you how to download / clone this repository (or you already knew how and did it yourself). Open a terminal in this directory (where this repository is):
Windows - in file manager, navigate to this repo, right click anywhere and click 'command prompt here'
MacOS - in Finder, navigate to this repo, then (top left of screen) click 'Finder'->Services->'new Terminal at Folder'
Linux - cd to this repo
and run "conda create --name qubcourse --file conda_package_list.txt python=3.7.5", which will create a new virtual environment, with a specific version of Python, and will install the same versions of the same packages I had installed when I made this course.
This will take a while, so I'll probably have started going through the earlier parts of the course during these downloading and installing.
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In your terminal, run "conda activate qubcourse". This tells it to use the virtual environment we made a minute ago. When we're done, we can run "conda deactivate" to go back to the way things were before.
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In your terminal, still in this directory, run "jupyter notebook" - this should cause a webpage to open, with a list of what's in this directory. To start with, click on the 'PythonRefresher.ipynb' link.
That's us fully set up and started!
Note: In 2019 split into 'Python Refresher Course' (winter) and 'High Performance Python' (summer) due to length of additional content
Stable: Python Refresher
Unstable / Untidy: High Performance Sections (mostly tidy enough)
2019 Updates Now Out
Python Refresher Course Overview:
Commenting
Basic Data Types
Conditional Logic & Basic Loops
Comprehensions
Functions & Lambdas
Intro to OOP Python
Generators & Iterators
Handling Files
Numpy & Scipy
Pandas
Matplotlib
Scikit-Learn
Free External Resources (IDEs, Books, Library / Domain-Specific Tutorials, List of Useful Libraries)
Team Members
| First Name | Last Name | 2018 | 2019 |
|---|---|---|---|
| Conor | Duffy | Y | N |
| Silas | O'Toole | Y | N |
| Luke | Roantree | Y | Y |
Special thanks to:
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Dr. Garbriele De Chiara - for helping organise the event and booking the computer suites
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Victoria Coulter - for obtaining funding, setting up the registration, and more
What This Doesn't Cover:
Python Refresher:
Interacting with computer via os and sys (2019 Updates touch on some)
Installing extra libraries
Machine learning libraries other than Sklearn
High Performance Python summer-2019 release updates to include:
Multi-Threading
Parallel Processing (joblib and mpi4py)
Cython other than via Jupyter Notebook
Pythran
f2py