hannahwoodward / ml-training-material

Training material on ML with PyTorch by ICCS

ICCS Practical Machine Learning with PyTorch

Open with binder: https://mybinder.org/v2/gh/hannahwoodward/ml-training-material/HEAD

This repository contains documentation, resources, and code for the Introduction to Machine Learning with PyTorch session designed and delivered by Jack Atkinson (@jatkinson1000) and Jim Denholm (@jdenholm) of ICCS.
The material has been delivered at both the ICCS and NCAS summer schools.
All materials, including slides and videos, are available such that individuals can cover the course in their own time.

Contents

• Preparation and prerequisites
• Installation and setup
• Teaching material
• License information

Preparation and prerequisites

To get the most out of the session we assume a basic understanding in a few areas and for you to do some preparation in advance. Expected knowledge is outlined below, along with resources for reading if you are unfamiliar.

Mathematics and Machine Learning

Basic mathematics knowledge:

• calculus - differentiating a function
• matrix algebra - matrix multiplication and representing data as a matrix
• regression - fitting a function to data

Neural Networks:

• Awareness of high-level concepts
• We recommend the video series by 3Blue1Brown, at least chapters 1-3.

Python

The course will be taught in python using pyTorch. Whilst no prior knowledge of pyTorch is expected we assume users are familiar with the basics of Python3. This includes:

• Basic mathematical operations
• Writing and running scripts/programs
• Writing and using functions
• The concept of object orientation (that an object, e.g. a dataset, can have associated functions/methods associated with it.)
• Basic use of the following libraries:
  • numpy for mathematical and array operations
  • matplotlib for ploting and visualisation
  • pandas for storing and accessing tabular data
• Familiarity with the concept of a jupyter notebook

git and GitHub

You will be expected to know how to

• clone and/or fork a repository,
• commit, and
• push.

The workshop from the 2022 ICCS Summer School should provide the neccessary knowledge.

Preparation

In preparation for the course please ensure that your computer contains the following:

• A text editor - e.g. vim/neovim, gedit, vscode, sublimetext etc. to open and edit code files
• A terminal emulator - e.g. GNOME Terminal, kitty, Windows Terminal (windows only), iTerm (mac only)
• python virtual environment (see Installation and setup)

Note for Windows users: We have linked suitable applications for windows in the above lists. However, you may wish to refer to Windows' getting-started with python information for a complete guide to getting set up on a Windows system.

If you require assistance or further information with any of these please reach out to us before a training sesison.

Installation and setup

1. Clone or fork the repository

Navigate to the location you want to install this repository on your system and clone via https by running:

git clone https://github.com/Cambridge-ICCS/ml-training-material.git

This will create a directory ml-training-material/ with the contents of this repository.

Please note that if you have a GitHub account and want to push any work you might do back up for future reference we suggest you fork the repository and then clone your fork.

2. Create a virtual environment

Before installing any Python packages it is important to first create a Python virtual environment. This provides an insulated environment inside which we can install Python packages without polluting the operating systems's Python environment.

If you have never done this before don't worry: it is very good practise, especially when you are working on multiple projects, and easy to do.

python3 -m venv MLvenv

This will create a directory called MLvenv containing software for the virtual environment. To activate the environment run:

source MLvenv/bin/activate

You can now work on python from within this isolated environment, installing packages as you wish without disturbing your base system environment.

When you have finished working on this project run:

deactivate

to deactivate the venv and return to the system python environment.

You can always boot back into the venv as you left it by running the activate command again.

3. Install dependencies

It is now time to install the dependencies for our code, for example PyTorch. The project has been packaged with a pyproject.toml so can be installed in one go. From within the root directory in a active virtual environment run:

pip install .

This will download the relevant dependencies into the venv as well as setting up the datasets that we will be using in the course.

Teaching Material

Slides

The slides for this workshop can be viewed on the ICCS Summer School Website:

• Teaching
• Climate Applications

The slides are generated from markdown using quarto. The raw markdown and html files can be found in the slides directory.

Exercises

The exercises for the course can be found in the exercises directory.
These take the form of partially complete jupyter notebooks.

Videos

Videos from past workshops may be useful if you are following along independently.
These can be found on the ICCS youtube channel under the 2023 Summer School materials.

License

The code materials in this project are licensed under the MIT License.

The teaching materials are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.