/tmva-tutorial

Collection of Notebook for Tutorials on TMVA

Primary LanguageJupyter Notebook

TMVA Tutorial

These are tutorials on TMVA given at workshops and Machine Learning events in 2017 and 2018

The most recent one has been given at the 1st Terascale School on Machine Learning at Desy, see agenda at https://indico.desy.de/indico/event/21278/

The tutorials consist of notebook which can be run on SWAN ( https://swan.cern.ch )

In order to run the tutorial we recommend that you:

  1. Open a Terminal in SWAN
  2. Clone your repository (it will appear in your cernbox folder)
git clone  https://github.com/lmoneta/tmva-tutorial.git

  • The tutorials will be in the directory notebooks. Older tutorials could be retrieved using the corresponding git branches

  • The notebooks used in this tutorial use either C++ or Python. Python notebooks have the suffix _py appended to their name. In some case a corresponding ROOT macro is available, which can run directly from the ROOT prompt. The macro are just an example how one can create easly a ROOT macro or a Python script from a given C++ or Python based notebook.

  • More notebooks, some written also in Python, are available in the Machine Learning Gallery of SWAN, https://swan.web.cern.ch/content/machine-learning

  • Additional tutorials of TMVA are available in the tutorials/tmva directory of the main ROOT git repository. You can find them also here https://root.cern.ch/doc/master/group__tutorial__tmva.html

  • The updated TMVA Users Guide is available at https://root.cern.ch/download/doc/tmva/TMVAUsersGuide.pdf

Swan and Jupyter notebooks quick start

If you have never used jupyter notebooks, you can find some quickstart information at the following links What is a jupyter notebook?, Notebook Basics, Running Code.

For information on using SWAN, see the SWAN website: https://swan.web.cern.ch In a nutshell, "SWAN (Service for Web based ANalysis) is a platform to perform interactive data analysis in the cloud". It gives you access to the LCG software stacks and to your CERNBOX files.

  • You need to have an active CERNBOX to use swan (https://cernbox.cern.ch/). If you don't have a CERN account, please contact us directly and some temporary accounts can be made available. In that case you need to feel this form to get access to the account, https://docs.google.com/forms/d/1ed4LsFBg9BmAHvfWYJDL4Svdmfk-tB-xbIyIxMPUmuc/ We will provide you the account name and password.

  • For using the new Deep Learning features of TMVA (e.g. CNN) one needs now to use the Development Bleeding Edge Version of SWAN which is built using the current ROOT master version.

  • If you think think the Jupyter notebook is stuck, open a terminal on swan and execute top: if the cpu is not being used (0%) by any of your processes, you may have to restart the Jupyter kernel.

IMPORTANT:

  • Please only have one notebook at the time on swan (you have to select the "running" tab and shutdown the notebook when done)
  • Limit the number of events you use on swan (order 10000-100000) and the avoid expensive methods: SWAN is meant for fast prototyping.
    • Each swan container has 2 GB of ram assigned, using a dataset with too many events may hit the memory limit.