diff --git a/episodes/01-introduction.md b/episodes/01-introduction.md new file mode 100644 index 0000000..38039a4 --- /dev/null +++ b/episodes/01-introduction.md @@ -0,0 +1,68 @@ +--- +title: "Introduction" +teaching: 30 +exercises: 60 +--- + +:::::::::::::::::::::::::::::::::::::: questions + +- Why is collaboration and knowledge sharing important in particle physics? +- What is the significance of open data and the open science community? +- What direction and challenges will we take in this hackathon? + +:::::::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: objectives + +- Understand the importance of collaboration and knowledge sharing in particle physics. +- Learn about the significance of open data and the role of the open science community. +- Get an overview of the different lessons and challenges in the hackathon. + +:::::::::::::::::::::::::::::::::::::::::::::::: + +## Welcome to the CMS Open Data Workshop & Hackathon 2024! + +### Importance of Collaboration and Knowledge Sharing + +In the field of particle physics, collaboration and knowledge sharing are crucial. The CMS Open Data Workshop & Hackathon aims to foster these values by providing a platform for participants to work together, share insights, and learn from one another. By collaborating on complex problems and sharing our findings, we can push the boundaries of what we know and achieve breakthroughs that would be impossible to accomplish alone. + +::::::::::::::::::::::: testimonial +*By working together, we can leverage our collective expertise and creativity to solve complex problems and advance the field of particle physics.* +::::::::::::::::::::::: + +### The Importance of Open Data and the Open Science Community + +Open data is a cornerstone of modern scientific research. By making data freely available to the public, we enable a broader range of scientists and enthusiasts to engage with it, leading to more robust and innovative discoveries. The open science community thrives on transparency, accessibility, and collaboration, and CMS Open Data is a perfect example of these principles in action. Through this hackathon, we aim to demonstrate the power of open data and encourage more people to contribute to the open science movement. + +::::::::::::::::::::::: testimonial +*Open data allows for greater transparency and reproducibility in research, fostering innovation and enabling more people to contribute to scientific discoveries.* +::::::::::::::::::::::: + +### Direction and Challenges of the Hackathon + +This section is tailored for our remote participants who do not have an active research task for Open Data. These activities are designed to provide a comprehensive learning experience and allow you to engage deeply with particle physics, data analysis, and machine learning from a distance. + +#### Key Activities + +1. **Particle Physics Playground**: Dive into fundamental concepts in particle physics through interactive exercises. Revisit the Particle Physics Primer pre-learning lesson to explore basic principles and engage with various scenarios to enhance your understanding. +2. **Particle Discovery Lab**: Analyze real particle collision data from the CMS experiment using Python containers. Clone repositories, follow instructions, and perform both basic and advanced data analysis tasks to identify different particles and gain hands-on experience with real-world data. +3. **Machine Learning 1**: Get introduced to the application of machine learning in high-energy physics (HEP). Learn initial concepts and practical applications of ML techniques in analyzing particle collision data. +4. **Machine Learning 2**: Delve deeper into supervised and unsupervised learning methods. Follow detailed instructions to apply these techniques for classifying particle collisions, and gain practical experience in data preparation, model training, and evaluation. +5. **Analysis Grand Challenge**: Participate in a more extensive exercise using up-to-date HEP software tools. Engage with tasks such as creating synthetic data from older CMS data, tackling generative modeling challenges, and validating complex models. + + +These activities are designed to provide a rich learning experience and help you contribute meaningfully to the open science community. Enjoy exploring and collaborating with fellow participants throughout the hackathon! + +::::::::::::::::::::::::::::: callout +## Challenge Yourself and Collaborate! + +Join us in this exciting journey of discovery and innovation. By participating in the CMS Open Data Workshop & Hackathon, you are contributing to a global community of scientists and enthusiasts working towards a common goal. Let's push the boundaries of our knowledge together! +::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: keypoints + +- Collaboration and knowledge sharing are essential in advancing particle physics research. +- Open data enables greater transparency, accessibility, and innovation in scientific research. +- This hackathon offers a variety of lessons and challenges suitable for participants with different interests and skill levels. + +:::::::::::::::::::::::::::::::::::::::::::::::: diff --git a/episodes/02-pdl.md b/episodes/02-pdl.md deleted file mode 100644 index 963ae98..0000000 --- a/episodes/02-pdl.md +++ /dev/null @@ -1,88 +0,0 @@ ---- -title: "Particle Discovery Lab" -teaching: 30 -exercises: 60 ---- -:::::::::::::::::::::::::::::::::::::: questions - -How can we identify different particles in collision data? -What are the characteristics of muons in the dataset? -How do we perform basic and advanced data analysis in particle physics? -:::::::::::::::::::::::::::::::::::::::::::::::: - -::::::::::::::::::::::::::::::::::::: objectives - -Reconstruct decays of an unknown particle X to 2 muons. -Use histograms to display the calculated mass of particle X. -Learn to fit and subtract background contributions from data. -Understand uncertainty propagation throughout the analysis. -Identify the discovered particle and compare its properties to known values. -:::::::::::::::::::::::::::::::::::::::::::::::: - -## Particle Discovery Lab - -The goal of this exercise is to reconstruct decays of an unknown particle X (initial state) to 2 muons (final state). To achieve this goal, participants need to display histograms for the calculated mass of particle X and learn about fitting and subtracting background distributions from data. - -Uncertainty propagation concepts are included at each step of the analysis. After isolating the signal distribution, participants will determine which particle they have discovered and compare its properties (mass and width) to known properties. - -## Overview -The Particle Discovery Lab is designed to introduce participants to the fascinating world of particle physics by working with actual data from the CMS experiment. This hands-on experience will provide valuable insights into the process of particle identification and the analysis techniques used by physicists. - -### Identifying Particles -Participants will learn to identify different particles by analyzing their collision data. Key characteristics such as energy, momentum, and decay patterns will be examined to distinguish between various particles. The focus will be on identifying muons and electrons, which are fundamental components in many particle physics studies. - -### Basic and Advanced Data Analysis -The lab will guide participants through both basic and advanced data analysis tasks. Initially, they will perform simple tasks such as plotting histograms and calculating basic statistics. As they progress, more advanced techniques will be introduced, including fitting data to theoretical models and performing complex statistical analyses. - -## Instructions for the Exercise -To get started with the Particle Discovery Lab, follow the instructions found in [this repository](https://github.com/DanielaMerizalde/CMS-Workshop). - -### Get Ready - -1. Have the `my_python` container ready. The steps to install Docker and create this container are mentioned in the pre-exercises. -2. Git clone this repository inside the `my_python` container and ensure the following files have been created: `pollsf.py`, `MuonAnalysis.ipynb`, and `DoubleMuParked_100K.pkl`. -3. **Optional (strongly recommended):** Download the files `pollsf.py`, `MuonAnalysis.ipynb`, and `DoubleMuParked_100K.pkl`. Then upload these files to your Google Drive and open them with Google Colab. - -### Steps - -1. Run the command `docker start -i my_python`. If the repository was correctly cloned, you should have a file called `MuonAnalysis.ipynb`. Open this file with a text editor. One way to do this is to run `nano MuonAnalysis.ipynb`. -2. Read the text and uncomment the code lines. Then save the changes and close the text editor. Test the changes by running the command `python MuonAnalysis.ipynb`. -3. Continue opening this file, completing the code tasks, and testing the results by running the command `python MuonAnalysis.ipynb`. - -#### Note - -Notice that the graphs generated by the `matplotlib` library will not be displayed due to the lack of an interface inside the container. Therefore, instead of using the command `plt.show()`, change that command to `plt.savefig('name_of_graph.png')`. Then, a file called `name_of_graph.png` will appear in your container. Copy that image to your local machine and then open it. Recall from the pre-exercises that to copy a file from your container, you must exit the container and run the following command: - -```sh -docker cp name_of_the_container:path_of_the_file_inside_the_container local_path_outside_the_container -``` - -## Visualize with CMS Spy WebGL -To enhance your understanding and visualization of the particle collision events, use the CMS Spy WebGL visualizer. This tool provides a 3D visualization of the CMS collision data, allowing you to better grasp the spatial distribution and interactions of particles. - -::::::::::::::::::::::::::::: callout - -From the previous description, it is noticeable that copying the image file to your local machine every time it is generated could be a tedious task. Therefore, to save time, it is better to complete all the MuonAnalysis.ipynb code on Google Colab. Notice that MuonAnalysis.ipynb will work only if the files pollsf.py, MuonAnalysis.py, and DoubleMuParked_100K.pkl are in the same drive file. An adapted version for running the script on Google Colab can be found in this repository. Then, download pollsf.py, MuonAnalysis.py, and DoubleMuParked_100K.pkl to your local machine. Finally, copy those files into the my_python container, then generate all the images inside your script and copy the images to your local machine to view them. - -::::::::::::::::::::::::::::: - -### Recommendations for Hackathon Activities - -Participants in the hackathon can leverage their skills and the themes explored in the Particle Discovery Lab to tackle innovative challenges and projects. Here are some suggested activities: - -- **Advanced Particle Identification Algorithms**: Develop and implement advanced algorithms for particle identification using collision data. -- **Enhanced Data Visualization Tools**: Create interactive tools for exploring and analyzing CMS collision data in real-time. -- **Integration of Machine Learning**: Apply machine learning techniques to automate data analysis and improve particle identification accuracy. -- **Collaborative Analysis Projects**: Form teams to tackle complex analysis challenges or develop new methodologies for studying particle interactions. -- **Educational Outreach and Visualization**: Design educational materials or demos that explain particle physics principles using CMS collision data. -- **Open Data Innovation**: Develop tools or platforms to enhance accessibility and usability of CMS Open Data for the scientific community. - -These activities encourage innovation, collaboration, and exploration of particle physics concepts beyond the basic lab exercises. - - -::::::::::::::::::::::::::::::::::::: keypoints - -Introduction to particle collision data. -Techniques for identifying particles such as muons and electrons. -Methods for performing both basic and advanced data analysis. -:::::::::::::::::::::::::::::::::::::::::::::::: \ No newline at end of file diff --git a/episodes/01-ppp.md b/episodes/02-ppp.md similarity index 99% rename from episodes/01-ppp.md rename to episodes/02-ppp.md index bc732a0..588d8da 100644 --- a/episodes/01-ppp.md +++ b/episodes/02-ppp.md @@ -60,6 +60,7 @@ Participants will be introduced to various tools and techniques used in particle ### Additional Resources + - **Particle Physics Primer Videos**: Watch public-oriented videos for a broad overview. - **Standard Model References**: Review materials on the Standard Model and its historical development. - **Advanced Lectures**: For those interested, watch the remaining lectures covering neutrino physics and dark matter. diff --git a/episodes/03-pdl.md b/episodes/03-pdl.md new file mode 100644 index 0000000..812169c --- /dev/null +++ b/episodes/03-pdl.md @@ -0,0 +1,88 @@ +--- +title: "Particle Discovery Lab" +teaching: 30 +exercises: 60 +--- +:::::::::::::::::::::::::::::::::::::: questions + +How can we identify different particles in collision data? +What are the characteristics of muons in the dataset? +How do we perform basic and advanced data analysis in particle physics? +:::::::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: objectives + +Reconstruct decays of an unknown particle X to 2 muons. +Use histograms to display the calculated mass of particle X. +Learn to fit and subtract background contributions from data. +Understand uncertainty propagation throughout the analysis. +Identify the discovered particle and compare its properties to known values. +:::::::::::::::::::::::::::::::::::::::::::::::: + + +## Particle Discovery Lab + +The goal of this exercise is to reconstruct decays of an unknown particle X (initial state) to 2 muons (final state). Participants will display histograms for the calculated mass of particle X and learn about fitting and subtracting background distributions from data. + +Uncertainty propagation concepts are included at each step of the analysis. After isolating the signal distribution, participants will determine which particle they have discovered and compare its properties (mass and width) to known values. + +### Get Ready + +1. **Prepare Your Environment**: + - Ensure that the `my_python` container is ready. Refer to the pre-exercise instructions for Docker setup and container creation. + +2. **Set Up and Launch Jupyter Lab**: + - Execute the following commands in your terminal: + + ```sh + docker start -i my_python + mkdir Particle_Discovery_Lab + cd Particle_Discovery_Lab + git clone https://github.com/DanielaMerizalde/CMS-Workshop.git + cd .. + jupyter-lab --ip=0.0.0.0 --no-browser + ``` + + - After running the last command, open the provided link in your browser to access Jupyter Lab. + +3. **Verify Files**: + - Ensure that the following files are present in the `Particle_Discovery_Lab` directory after cloning the repository: + - `pollsf.py` + - `Particle_Discovery_Lab.ipynb` + - `DoubleMuParked_100K.pkl` + +### Instructions for the Exercise + +1. **Launch Jupyter Lab**: + - In Jupyter Lab, navigate to `Particle_Discovery_Lab.ipynb` from the file menu and open it. + +2. **Complete the Notebook**: + - Follow the instructions in the notebook to complete the analysis interactively. You will perform tasks such as plotting histograms, fitting data, and analyzing uncertainties. + + +::::::::::::::::::::::::::::: Visualize with CMS Spy WebGL + +To enhance your understanding and visualization of the particle collision events, use the CMS Spy WebGL visualizer. This tool provides a 3D visualization of the CMS collision data, allowing you to better grasp the spatial distribution and interactions of particles. + +::::::::::::::::::::::::::::: + +### Recommendations for Hackathon Activities + +Participants in the hackathon can leverage their skills and the themes explored in the Particle Discovery Lab to tackle innovative challenges and projects. Here are some suggested activities: + +- **Advanced Particle Identification Algorithms**: Develop and implement advanced algorithms for particle identification using collision data. +- **Enhanced Data Visualization Tools**: Create interactive tools for exploring and analyzing CMS collision data in real-time. +- **Integration of Machine Learning**: Apply machine learning techniques to automate data analysis and improve particle identification accuracy. +- **Collaborative Analysis Projects**: Form teams to tackle complex analysis challenges or develop new methodologies for studying particle interactions. +- **Educational Outreach and Visualization**: Design educational materials or demos that explain particle physics principles using CMS collision data. +- **Open Data Innovation**: Develop tools or platforms to enhance accessibility and usability of CMS Open Data for the scientific community. + +These activities encourage innovation, collaboration, and exploration of particle physics concepts beyond the basic lab exercises. + + +::::::::::::::::::::::::::::::::::::: keypoints + +Introduction to particle collision data. +Techniques for identifying particles such as muons and electrons. +Methods for performing both basic and advanced data analysis. +:::::::::::::::::::::::::::::::::::::::::::::::: \ No newline at end of file diff --git a/episodes/03-ml-1.md b/episodes/04-ml-1.md similarity index 100% rename from episodes/03-ml-1.md rename to episodes/04-ml-1.md diff --git a/episodes/04-ml-2.md b/episodes/05-ml-2.md similarity index 83% rename from episodes/04-ml-2.md rename to episodes/05-ml-2.md index 7984eef..5d5bbb0 100644 --- a/episodes/04-ml-2.md +++ b/episodes/05-ml-2.md @@ -24,7 +24,7 @@ exercises: 0 Machine learning techniques, such as Convolutional Neural Networks (CNNs) and autoencoders, play pivotal roles in analyzing particle physics data. This section provides insights into their architectures, training processes, and practical applications within the field. -## Convolutional Neural Networks (CNNs) +## Supervised Learning - Convolutional Neural Networks (CNNs) #### Purpose and Architecture @@ -39,9 +39,13 @@ CNNs are specialized neural networks designed for processing grid-like data, suc This project explores the application of deep learning techniques in high-energy physics using data from the CMS experiment at the LHC. The repository includes analyzers, scripts, and notebooks to process collision data and train convolutional neural networks (CNNs) for particle classification. By transforming collision data into images and using various CNN architectures, the project aims to classify high-energy particle collision outcomes with high accuracy. The `poet_realdata.py` script and `MuonAnalyzer_realdata.cc` analyzer are based on the original configuration and analyzers used in the final version of the [CMS Open Data Workshop 2022](https://cms-opendata-workshop.github.io/2023-07-11-cms-open-data-workshop/), ensuring consistency and relevance of the data analysis techniques and tools employed in this project with those taught during the workshop. +::::::::::::::::::::::::::::::::::::: Credit +This section and the associated project was developed by [José David Ochoa Flores](https://www.linkedin.com/in/jos%C3%A9-david-ochoa-flores-907a32195/), [Daniela Merizalde](https://www.linkedin.com/in/daniela-merizalde/), [Xavier Tintin](https://www.linkedin.com/in/xavier-tintin/), [Edgar Carrera](https://www.linkedin.com/in/caredg/), [David Mena](https://www.linkedin.com/in/david-mena-76104012b/), and [Diana Martinez](https://www.linkedin.com/in/diana-martinez-mosquera-92250041/), a collaborative research project in data science and particle physics from [Universidad San Francisco de Quito](https://www.usfq.edu.ec/en) and [Escuela Politéncica Nacional](https://www.epn.edu.ec). **Link to repo:** [GitHub](https://github.com/xaviertintin/cnn-hep-thesis/tree/main) +**Link to paper:** [Springer Link](https://link.springer.com/chapter/10.1007/978-3-031-45438-7_3) +::::::::::::::::::::::::::::::::::::: -## Autoencoders +## Unsupervised Learning - Autoencoders #### Purpose and Architecture @@ -56,8 +60,13 @@ Autoencoders are unsupervised learning models that learn efficient data represen The QCD School 2024 ML project is an educational initiative designed to introduce participants to the application of machine learning in high energy physics, specifically through anomaly detection using unsupervised learning. The project provides a hands-on tutorial for designing and implementing a tiny autoencoder (AE) model, which is trained to identify potentially new physics events from proton collision data obtained from the CMS Open Data. In this example you will learn to compress and decompress data using the autoencoder, train it on background data, and evaluate its performance on both background and New Physics simulated samples. The project also covers advanced techniques like quantization-aware training using QKeras and model deployment on FPGA firmware with hls4ml, providing a comprehensive learning experience that bridges theoretical concepts with practical implementation. +::::::::::::::::::::::::::::::::::::: Credit +This section and the QCD School 2024 ML project were developed by [Thea Klaeboe Aarrestad](https://www.linkedin.com/in/thea-klaeboe-aarrestad/), a particle physicist at ETH Zurich specializing in real-time AI and FPGA inference in the CMS experiment at CERN. + **Link to repo:** [GitHub](https://github.com/thaarres/qcd_school_ml/) +::::::::::::::::::::::::::::: + ## Key Differences - **Supervised vs. Unsupervised**: CNNs require labeled data for training (supervised), while autoencoders learn from unlabeled data (unsupervised). diff --git a/episodes/05-agc.md b/episodes/06-agc.md similarity index 91% rename from episodes/05-agc.md rename to episodes/06-agc.md index 48ee71c..59e4e89 100644 --- a/episodes/05-agc.md +++ b/episodes/06-agc.md @@ -39,9 +39,10 @@ Participants will work on a cross-section measurement using 2015 CMS Open Data. 5. **Statistical Inference**: Performing statistical analysis to infer the cross-section measurement. 6. **Visualization**: Creating relevant visualizations for each step of the analysis. -### Working with Older Data +### Working with CMS Open Data + +Using older CMS data presents unique challenges, such as analyzing data formats within the CMSSW software framework. Participants will learn strategies for overcoming these obstacles, ensuring their analyses are robust and accurate even when working with older CMS Open Data. -Using older CMS data presents unique challenges, such as dealing with outdated formats and incomplete datasets. Participants will learn strategies for overcoming these obstacles, ensuring their analyses are robust and accurate even when working with less-than-ideal data. ### Reproducibility and Scalability diff --git a/index.md b/index.md index a819429..2235b8c 100644 --- a/index.md +++ b/index.md @@ -2,64 +2,20 @@ site: sandpaper::sandpaper_site --- -:::::::::::::::::::::::::::::::::::::: questions +## Introduction -- Why is collaboration and knowledge sharing important in particle physics? -- What is the significance of open data and the open science community? -- What direction and challenges will we take in this hackathon? +Welcome to the CMS Open Data Workshop & Hackathon 2024! This lesson is designed specifically for our remote participants to guide you through the essential aspects of this year's event. As you join us from different locations around the world, this guide will help you understand the importance of collaboration, the significance of open data, and how you can engage with various challenges and activities throughout the hackathon. -:::::::::::::::::::::::::::::::::::::::::::::::: - -::::::::::::::::::::::::::::::::::::: objectives - -- Understand the importance of collaboration and knowledge sharing in particle physics. -- Learn about the significance of open data and the role of the open science community. -- Get an overview of the different lessons and challenges in the hackathon. - -:::::::::::::::::::::::::::::::::::::::::::::::: - -## Welcome to the CMS Open Data Workshop & Hackathon 2024! - -### Importance of Collaboration and Knowledge Sharing - -In the field of particle physics, collaboration and knowledge sharing are crucial. The CMS Open Data Workshop & Hackathon aims to foster these values by providing a platform for participants to work together, share insights, and learn from one another. By collaborating on complex problems and sharing our findings, we can push the boundaries of what we know and achieve breakthroughs that would be impossible to accomplish alone. - -::::::::::::::::::::::: testimonial -*By working together, we can leverage our collective expertise and creativity to solve complex problems and advance the field of particle physics.* -::::::::::::::::::::::: - -### The Importance of Open Data and the Open Science Community - -Open data is a cornerstone of modern scientific research. By making data freely available to the public, we enable a broader range of scientists and enthusiasts to engage with it, leading to more robust and innovative discoveries. The open science community thrives on transparency, accessibility, and collaboration, and CMS Open Data is a perfect example of these principles in action. Through this hackathon, we aim to demonstrate the power of open data and encourage more people to contribute to the open science movement. - -::::::::::::::::::::::: testimonial -*Open data allows for greater transparency and reproducibility in research, fostering innovation and enabling more people to contribute to scientific discoveries.* -::::::::::::::::::::::: - -### Direction and Challenges of the Hackathon - -During this hackathon, participants will engage in a variety of self-guided lessons that cover different aspects of particle physics, data analysis, and machine learning. Whether you're an undergraduate looking to understand the basics of particle physics or an experienced programmer seeking a challenging project, there is something for everyone. - -#### Key Activities - -1. **Particle Physics Playground**: Link back to the Particle Physics Primer pre-learning lesson that Matt made, and encourage participants to try out different exercises exploring fundamental concepts in particle physics. -2. **Particle Discovery Lab**: Share instructions to do the exercise in the Python container, git clone the repository in the container, and follow the instructions in either the Python script or the Jupyter notebook. Analyze real particle collision data from the CMS experiment, identify different particles, and perform both basic and advanced data analysis tasks. -3. **Machine Learning 1**: Introduction to using ML in HEP, covering initial aspects and practical applications. -4. **Machine Learning 2**: Share the supervised and unsupervised learning links with instructions on which container to use. Include a section to discuss results and next steps, applying machine learning techniques to classify different types of particle collisions and gain practical experience in data preparation, model training, and evaluation. -5. **Analysis Grand Challenge**: Short introduction to this larger exercise using up-to-the-moment HEP software tools. Provide the link and tackle generative modeling tasks using older CMS data, create synthetic data, and validate complex models. - -By the end of the hackathon, participants will have gained valuable skills and insights into high-energy physics, data analysis, and machine learning. They will also have the opportunity to contribute to the open science community by sharing their findings and collaborating with others. -::::::::::::::::::::::::::::: callout -## Challenge Yourself and Collaborate! +### What to Expect +In this workshop, you will engage in hands-on lessons covering particle physics, data analysis, and machine learning. You will work on real particle collision data, apply machine learning techniques, and tackle complex analysis tasks. Whether you're new to the field or an experienced participant, there's something for everyone. -Join us in this exciting journey of discovery and innovation. By participating in the CMS Open Data Workshop & Hackathon, you are contributing to a global community of scientists and enthusiasts working towards a common goal. Let's push the boundaries of our knowledge together! -::::::::::::::::::::::::::::: +Let’s embark on this exciting journey of discovery and collaboration together! ::::::::::::::::::::::::::::::::::::: keypoints -- Collaboration and knowledge sharing are essential in advancing particle physics research. -- Open data enables greater transparency, accessibility, and innovation in scientific research. -- This hackathon offers a variety of lessons and challenges suitable for participants with different interests and skill levels. +- Understand the Importance of Collaboration: Discover why working together and sharing knowledge is crucial in particle physics research. +- Learn About Open Data: Explore the role of open data and the open science community in advancing scientific discoveries. +- Get an Overview of Hackathon Challenges: Familiarize yourself with the different lessons and challenges designed to enhance your skills and contribute to the field. :::::::::::::::::::::::::::::::::::::::::::::::: diff --git a/learners/setup.md b/learners/setup.md index 3a0b944..393fcdb 100644 --- a/learners/setup.md +++ b/learners/setup.md @@ -2,4 +2,8 @@ title: Setup --- +::::::::::::::::::::::::::::::::::::: prereq + This lesson requires a computer with an internet connection and a bash shell (either native Linux, MacOs or Windows WSL2 Linux). You should have Docker installed and the [Docker pre-exercises](https://cms-opendata-workshop.github.io/workshop2023-lesson-docker/) finished so that you can access the containers `my_root` and `my_python` created as instructed there. + +::::::::::::::::::::::::::::::::::::::::::::::::