FLAME GPU 2 Tutorial (Python 3)

This repository provides a Tutorial on using the Python 3 interface for FLAME GPU 2.

Tutorial

The tutorial is a jupyter notebook which uses a predator-prey model to demonstrate how models can be described and implemented using FLAME GPU 2's python interface, and then simulated to observe population dynamics over the duration of a single simulation.

A number of exercises are presented to change and extend the behaviour of the model and observe the differences. Solutions are provided.

Instructor-led Tutorials

Instructor-led delivery of this tutorial will typically use hosted Jupyter solutions, such as cloud instances on InstanceHub or through Google Colab.

Please refer to the instructions provided during by the instructor(s) on how to access the tutorial in this case.

If you are interested in an instructor-led delivery of this tutorial, please contact us.

This tutorial was previously delivered at:

• 2021-09-08 SeptembRSE 2021 Workshop S1005: FLAME GPU 2: Complex Systems Simulation using GPUs
  • Delivered by Paul Richmond, Matthew Leach, Peter Heywood and Robert Chisholm
  • Introductory Talk Recording

Self-paced Tutorials

If you wish to follow this tutorial at your own pace, you can run this tutorial locally, or use a hosted service such as Google Colab.

Google Colab

The tutorial can be completed independently in the browser using Google Colab, which is a hosted fork of Jupyter.

• Open the tutorial on Google Colab
• If present, click the "Connect" button in the top right corner

If successful, the Connect button should be replaced with a green tick and RAM/Disk indicators, so it can be used like a regular Jupyter notebook. It's possible that a free instance will fail to be allocated, in which case please try again later. Free Google Colab instances use a variety of hardware, so performance may vary between instances.

Running this Tutorial Locally

Note: The notebook currently assumes you are using Python >3.10, with CUDA >12.0 on a Linux x86_64 machine. This will be corrected in the future.

To run this tutorial locally you will require:

• Python >= 3.10
• CUDA >= 12.0 and a Compute Capability >= 5.0 NVIDIA GPU
• Linux with glibc >= 2.28
  • Windows support/instructions will be introduced at a later date
• An x86_64 CPU
  • Including NVRTC
  • If you wish to run this tutorial on an ppc64le or arm64-sbsa based-system, you will need to build pyflamegpu from source. Please see the main FLAMEGPU/FLAMEGPU2 repository for instructions on building from source.

1. Clone the repository if you have not already done so

2. Create a new python venv or conda environment and activate it

   # I.e. if using a venv on linux
   python3 -m venv venv
   source venv/bin/activate

3. Install Jupyterlab (or jupyter notebook)

   # I.e. if using a venv and pip
   python3 -m pip install -U jupyterlab matplotlib

4. Launch jupyter lab or the legacy jupyter notebook interface and open FLAME_GPU_2_python_tutorial.ipynb

   # Using jupyter lab
   jupyter lab FLAME_GPU_2_python_tutorial.ipynb
   # Using jupyter notebook
   jupyter notebook FLAME_GPU_2_python_tutorial.ipynb

Known Issues

None currently.