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.
A website for this workshop can be found at https://cambridge-iccs.github.io/ml-training-material/.
- Learning Objectives
- Teaching material
- Preparation and prerequisites
- Installation and setup
- License information
The key learning objective from this workshop could be simply summarised as:
Provide the ability to develop ML models in PyTorch.
However, more specifically we aim to:
- provide an understanding of the structure of a PyTorch model and ML pipeline,
- introduce the different functionalities PyTorch might provide,
- encourage good research software engineering (RSE) practice, and
- exercise careful consideration and understanding of data used for training ML models.
With regards to specific ML content we cover:
- using ML for both classification and regression,
- artificial neural networks (ANNs) and convolutional neural networks (CNNs)
- treatment of both tabular and image data
The slides for this workshop can be viewed on the ICCS Summer School Website:
The slides are generated from markdown using quarto. The raw markdown and html files can be found in the slides directory.
The exercises for the course can be found in the exercises directory.
These take the form of partially complete jupyter notebooks.
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.
Worked solutions for all of the exercises can be found in the worked solutions directory.
These are for recapping after the course in case you missed anything, and contain ideal solutions complete with
docstrings, outfitted with
type hints,
linted, and conforming to the
black code style.
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.
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.
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
i.e. that an object, e.g. a dataset, can have associated functions/methods associated with it. - Basic use of the following libraries:
numpyfor mathematical and array operationsmatplotlibfor ploting and visualisationpandasfor storing and accessing tabular data
- Familiarity with the concept of a jupyter notebook
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 necessary knowledge.
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, wezterm, 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 session.
There are three options for participating in this workshop for which instructions are provided below:
- via a local install
- on Google Colab
- on binder
We recommend the local install approach, especially if you forked the repository, as it is the easiest way to keep a copy of your work and push back to GitHub.
However, if you experience issues with the installation process or are unfamiliar with the terminal/installation process there is the option to run the notebooks in Google Colab or on binder.
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 preserve any work you do we suggest you first fork the repository and then clone your fork. This will allow you to push your changes and progress from the workshop back up to your fork for future reference.
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' 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.
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.
From the current directory, launch the jupyter notebook server:
jupyter notebook
This command should then point you to the right location within your browser to use the notebook, typically http://localhost:8888/.
The following step is sometimes useful if you're having trouble with your jupyter notebook finding the virtual environment. You will want to do this before launching the jupyter notebook.
python -m ipykernel install --user --name=MLvenv
Running on Colab is useful as it allows you to access GPU resources.
To launch the notebooks in Google Colab click the following links for each of the exercises:
- Exercise 01 - Worked Solution 01
- Exercise 02 - Worked Solution 02
- Exercise 03 - Worked Solution 03
- Exercise 04 - Worked Solution 04
Notes:
- Running in Google Colab requires you to have a Google account.
- If you leave a Colab session your work will be lost, so be careful to save any work you want to keep.
If you cannot operate using a local install, and do not wish to sign up for a Google account, the repository can be launched on binder.
Notes:
- If you leave a binder session your work will be lost, so be careful to save any work you want to keep
- Due to the limited resources provided by binder you will struggle to run training in exercises 3 and 4.
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.
