First, you must have Docker installed on your system. Then, build a container with
docker build -t app .
then run the command below inside the project folder:
docker run -it --rm app
The available simulations and their options will be printed, so you can run a simulation with e.g.
docker run -it --rm app models
You might have to change permission for the volume that will be mounted (e.g. chmod -R +2 brian2-sims), as some simulations generate and save files. A more convenient approach is to work with a docker volume (see docker volume --help).
The docker images were tagged and pushed to dockerhub, so you can also pull the image with docker pull pabloabur/app and run this image as shown above. Note that --gpu and --entrypoint flags can be used for using GPUs and probing the container, respectively. However, running with GPU support might require nvidia-container-toolkit. Similarly, you can use singularity to pull the image from dockerhub and run it (e.g. singularity run --bind $(pwd)/sim_data:/app/sim_data --nv app_latest.sif --backend cuda_standalone --save_path sim_data/bal_stdp balance_stdp).
We used conda-based environment because if you are working on a remote server, conda might be a better option when linking other libraries/resources (e.g. C++).
R was only used to generate fancy plots. I couldn't add R requirements to the dockerfile, but, if you wish to reproduce the plots, you can install the dependencies with Rscript requirements.R and run the plotting scripts outside the docker environment.