SOSTAT2021: 2nd IAA-CSIC Severo Ochoa School on Statistics, Data Mining, and Machine Learning

A Severo Ochoa School of the Instituto de Astrofísica de Andalucía (CSIC)

School webpage: https://www.granadacongresos.com/sostat2021

Workshop Tutorials
Execution of the tutorials
Two alternative ways to execute the tutorials
- 1. In your local machine
  - Install conda
  - Get the contents of the school
- 2. Using the myBinder cloud
Credits and acknowledgements

Workshop Tutorials

Gwendolyn Eadie
- Sampling from distributions with R
- Bayesian inference with candy
Željko Ivezić
- ZIlecture1: Gentle Introduction to Big Data in Astronomy and Basics
- ZIlecture2: Introduction to ML; density estimation and regression
Abigail Stevens
- boot_jack_workbook (solutions)
- time_series_workbook (solutions)
Daniela Huppenkothen
- Practical Machine Learning with SDSS Data (solutions)
- A Tutorial on Neural Networks

Execution of the tutorials

The IAA-CSIC Severo Ochoa Center provides a JupyterHub server available during the duration of the school here:

https://spsrc-jupyter.iaa.csic.es/sostat2021/

Login with the credentials provided to you. These instances will have persistent storage throughout the duration of the school. All virtual machines and their contents will be removed by the 2021-12-12.. In case of any problem, please fill an issue here or ask in the #help Slack channel.

Two alternative ways to execute the tutorials

1. In your local machine

Install conda

We recommend using conda to manage the dependencies. Miniconda is a light-weight version of Anaconda. First we show how to install Miniconda if you don't have it already. More details here

You can skip this step if you already have conda available in your path.

Miniconda for Linux:

curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash ./Miniconda3-latest-Linux-x86_64.sh
rm ./Miniconda3-latest-Linux-x86_64.sh

Miniconda for macOS:

curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
bash Miniconda3-latest-MacOSX-x86_64.sh
rm Miniconda3-latest-MacOSX-x86_64.sh

That is all, conda should be available in your path. Note that the installation will suggest you to modify your bashrc so conda is always available, which is a good idea in general. Alternatively, if you want the Miniconda installation to be encapsulated in your working directory without affecting the rest of your system you can install it with the following option. The first command only needs to be done once, and the second one needs to be done everytime you open a new terminal.

cd /your/working/directory/
bash ./Miniconda3-latest-Linux-x86_64.sh -b -p my_conda_env
source my_conda_env/etc/profile.d/conda.sh

Get the contents of the school

Download this repository and create the conda environment with the dependencies

git clone https://github.com/spsrc/sostat2021.git
cd sostat2021
conda env create -f environment.yml
conda activate sostat2021

If you want to use Jupyer Lab, start it and navigate to tutorials/index.html with:

jupyter lab

2. Using the myBinder cloud

At any moment, also after the school, you can still run the tutorials in myBinder.org following this link:

myBinder.org is a free and open organization providing free cloud resources. Therefore, the resources may be limited and the changes you make in the notebooks or the system are not persistent. Please, always keep a local copy of any file you want to keep, because Binder will automatically eliminate the virtual machine assigned to you after some time of inactivity.

3. Deploy with Docker

To deploy the same environment used in SOSTAT2021 you can create your own docker container following the instructions describedhere.

Credits and acknowledgements

This repository and the Jupyter Hub service for the tutorials are provided by the SKA Regional Centre Prototype (SPSRC), which is funded by the State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa" award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709), the European Regional Development Funds (EQC2019-005707-P), the Junta de Andalucía (SOMM17_5208_IAA), and the project RTI2018-096228-B-C31(MCIU/AEI/FEDER,UE) and the grants PTA2018-015980-I(MCIU,CSIC) and 54A Scientific Research and Innovation Program (Regional Council of Economy, Knowledge, Business and Universities, Regional Government of Andalusia and the European Regional Development Funds 2014-2020, program D1113102E3).

espsrc / sostat2021