Reproducible analyses
Schedule
| Setup | Install software required for the lesson | |
| 00:00 | 1. Introduction |
What makes research data analyses reproducible?
Is preserving code, data, and containers enough? |
| 00:11 | 2. First example |
How to run analyses on REANA cloud?
What are the basic REANA command-line client usage scenarios? How to monitor my analysis using REANA web interface? |
| 00:31 | 3. Developing serial workflows |
How to write serial workflows?
What is declarative programming? How to develop workflows progressively? Can I temporarily override workflow parameters? Do I always have to build new Docker image when my code changes? |
| 01:01 | 4. HiggsToTauTau analysis: serial | Challenge: write the HiggsToTauTau analysis serial workflow and run it on REANA |
| 01:26 | 5. Coffee break | Coffee break |
| 01:41 | 6. Developing parallel workflows |
How to scale up and run thousands of jobs?
What is a DAG? What is a Scatter-Gather paradigm? How to run Yadage workflows on REANA? |
| 02:06 | 7. HiggsToTauTau analysis: parallel | Challenge: write the HiggsToTauTau analysis parallel workflow and run it on REANA |
| 02:36 | 8. A glimpse on advanced topics |
Can I publish workflow results on EOS?
Can I use Kerberos to access restricted resources? Can I use CVMFS software repositeries? Can I dispatch heavy computations to HTCondor? Can I dispatch heavy computations to Slurm? Can I open Jupyter notebooks on my REANA workspace? Can I connect my GitLab repositories with REANA? |
| 02:56 | 9. Wrap-up |
What have we learned today?
Where to go from here? |
| 03:01 | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.