Dask is an open-source Python library for parallel/distributed computing.
- This tutorial explains two fundamental problem paralleization concepts: domain decomposition and functional decomposition.
- It exemplifies how these two concepts can be achieved using Dask.
- It exemplifies how Dask enable parallel computing/distributed, out-of-core computations and scalability (from a local machine to cloud computing systems)
This tutorial start at 0.Introduction.
Run this tutorial using your own machine
- Create a virtual enviroment using
conda
orpip
. - Install the
requirements.txt
- Run the jupyterlab enviroment.
Run this tutorial using Vagrant
Vagrant automates the deployment of virtual machines. The Vagrantfile
defines a ready to use jupyterlab virtual machine for this tutorial. Use the following commands to deploy the machine.
Install vagrant
sudo apt update
sudo apt install virtualbox
sudo apt install vagrant
Provision the virtual machine
vagrant up
Get into the virtual machine
vagrant ssh
Stop the virtual machine
vagrant ssh
Destroy the virtual machine
vagrant ssh
Use the following url to access the jupyterlab enviroment.
This tutorial was presented in
- This project is partially supported by CyberColombia
- We thank Chameleon Cloud for its support in compute time.
- We thank Coiled for their technical support and compute time provided on their ready-to-use Dask Cloud Computing Platform.
- We thank Naty Clementi from Coiled for her support and suggestions to improve this tutorial.