Summary and Schedule
Welcome to learn about Machine learning using CMS open data during the Midsummer QCD school.
Setup Instructions | Download files required for the lesson | |
Duration: 00h 00m | 1. Introduction | WIP |
Duration: 00h 05m | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
Docker containers
You will be using the Python container as instructed in the Docker lesson.
You are expected to have worked though the CMS open data example in that python container as instructed in the Dataset scouting lesson. if you have not done it yet, do it now.
Get the code
Clone the code from the example repository in a working directory
that you have shared with the container. If you followed the
instructions in the Docker tutorial, the working directory in
cms_open_data_python
:
Add this code to your personal GitHub account.
In the GitHub Web UI, create a new repository as instructed in the Git tutorial.
Go to your GitHub area
(https://github.com/[yourgithubname]
), choose the
“Repositories” tab and click on New.
Choose qcd_school_ml
as the repository name, choose
Public and leave other options as they are. This will create the
repository and generate an instruction page.
On the terminal, check the remote:
OUTPUT
origin git@github.com:thaarres/qcd_school_ml.git (fetch)
origin git@github.com:thaarres/qcd_school_ml.git (push)
Change remote’s URL in order to be able to add the code to your personal GitHub account.
Check again the name of the current remote:
Push the code to your new repository with
Whoops!
We noticed that there are changes in the original repository. Let us
define it as upstream
:
Now you can pull the changes to the local repository:
and push them to your remote GitHub repository with
Check the container
Open the my_python
container and check that you see the
code repository:
The code should be visible under qcd_school_ml
.
About the file list
Take note the file names of the CMS open data files used in the
tutorial can be downloaded with the cernopendata-client
as
explained in the Dataset
scounting tutorial
BASH
docker run -i -t --rm docker.io/cernopendata/cernopendata-client get-file-locations --recid 63168 --protocol xrootd
docker run -i -t --rm docker.io/cernopendata/cernopendata-client get-file-locations --recid 33703 --protocol xrootd
A subset of them is already in the notebook, but this is how you would get them.
Callout
That’s it, you are ready to (machine) learn!