• Table of contents
• ML beam test PID Meetings
  • Summer 2024
    • May 28th, 2024
      • Near-term Goals
      • Homework
    • May 29th, 2024
      • Setting Up Jupyter Notebooks in Docker
      • Near-term Goals
      • Homework
    • May 30th, 2024
      • Setting Up Bash Script for Running Docker
      • Setting Up Bash Script for committing Docker container
      • Useful Docker Commands
      • Near-term Goals
      • Homework
    • May 31st, 2024
      • Clone a git repository into the Docker container by adding a line to the Dockerfile
      • Quick requirements.txt introduction
      • Useful Docker Commands
      • Near-term Goals
      • Homework
    • June 3rd, 2024
      • First Time General Docker Procedure
      • Running a snapshot
      • Near-term Goals
      • Homework
    • June 4th, 2024
      • Near-term Goals
      • Homework

ML beam test PID Meetings¶

---

Summer 2024¶

---

May 28th, 2024¶

• Darren ran with Python3.9
• Docker (containerization) Definition
  • Containerization is a technology that allows developers to package and run applications along with all their dependencies in isolated environments called containers. This ensures that the application runs consistently across different computing environments, from a developer's laptop to testing, staging, and production.
  • Docker is a popular platform that simplifies containerization. It provides tools to create, deploy, and manage containers. With Docker, developers can write code locally, share their work with colleagues, and deploy to production in a seamless and efficient manner. Docker containers are lightweight, fast, and portable, making them ideal for modern software development and deployment.

• A Crash Course for Summer Research
• GitHub and Python Introduction
  • Navigate to python_tutorials and read through the two html files (Introduction and python_tutorial_2)
    • Note, these are html files so you'll need

Near-term Goals¶

1. Setup Linux Subsystem for Windows
2. Fork the ML Beam Testing GitHub repository
3. Containerize ML Beam Testing GitHub repository with Docker

Homework¶

• Read chapters 0-3 and finish exercises

---

May 29th, 2024¶

Setting Up Jupyter Notebooks in Docker¶

1. Create a directory to store, build, and run Docker container
   

   cd /path/to/directory
   mkdir beamtest_dir
   cd beamtest_dir
   

2. Once in the new directory, create a Dockerfile
   

   touch Dockerfile
   

3. Using the text editor (e.g., vim, gedit, or emacs) of your choice, edit the Dockerfile with the following information
   

   # Use the official Python 3.9 image as the base image
   FROM python:3.9
   
   # Install Jupyter Notebook and other dependencies
   RUN pip install --no-cache-dir jupyter
   
   # Create a working directory
   WORKDIR /workspace
   
   # Expose the Jupyter Notebook port
   EXPOSE 8888
   
   # Set the default command to start Jupyter Notebook
   CMD ["jupyter", "notebook", "--ip=0.0.0.0", "--port=8888", "--no-browser", "--allow-root"]
   

4. Build the Docker image (e.g., helloworld)
   

   docker build -t helloworld .
   

5. Run the Docker container
   
   

   docker run -p 8888:8888 helloworld
   

6. Jupyter Notebook is now running. Navigate to a browser and type in either

• The custom token authorization screen
  

  http://localhost:8888
  

• To bypass the token screen, copy/paste the URL that splashes in the terminal into your browser

Near-term Goals¶

1. Try and get the python tutorial Jupyter notebooks working in a container.

Homework¶

• Read chapters 0-3 and finish exercises

---

May 30th, 2024¶

Setting Up Bash Script for Running Docker¶

1. Create a bash script
   

   touch run_docker.sh
   

2. Using the text editor (e.g., vim, gedit, or emacs) of your choice, edit run_docker.sh
   

   #!/bin/bash
   
   # Build docker image ($1 is the first bash script argument)
   docker build -t $1 .
   
   # Run the docker container (renaming it with the '_container' string)
   docker run -p 8888:8888 --name "${1}_container" $1
   

3. Adjust permissions with chmod to allow script execution
   

   chmod 755 run_docker.sh
   

4. Execute run_docker.sh with container name (e.g., helloworld)
   

   ./run_docker.sh helloworld
   

The docker image should be properly built and the container should be running. Follow instructions from yesterday to continue.

Setting Up Bash Script for committing Docker container¶

This should be done while a docker container is running and performed in another terminal.

1. Create a bash script
   

   touch commit_docker.sh
   

2. Using the text editor (e.g., vim, gedit, or emacs) of your choice, edit commit_docker.sh
   

   #!/bin/bash
   
   # Create a formatted string with date to organize committed containers
   # - `date` is a command that prints or sets the system date and time.
   # - `+%H` extracts the hour in 24-hour format.
   # - `+%M` extracts the minute.
   # - `+%S` extracts the second.
   # - `+%Y` extracts the year.
   # - `+%m` extracts the month.
   # - `%d` extracts the day.
   f_date=$(date +%Y-%m-%d_h%Hm%Ms%S)
   
   # While a docker container is running (with root name given as first argument), 
   # commit to a dated container so that changes in the container are saved
   docker commit "${1}_container" "${1}_${f_date}" 
   

3. Adjust permissions with chmod to allow script execution
   

   chmod 755 commit_docker.sh
   

4. Execute run_docker.sh with container name (e.g., helloworld)
   

   ./commit_docker.sh helloworld
   

Useful Docker Commands¶

• To see all docker images
  

  docker images
  

• To see all docker containers running
  

  docker ps
  

• To end specific docker container
  

  docker stop <container_id>
  

• To end all docker container
  

  docker stop $(docker ps -q)
  

Near-term Goals¶

1. Try and get the python tutorial Jupyter notebooks working in a container.
2. Take a look at the GitHub ML Beam Test Repository
• In particular, look at the script solid_beamtest_hallc_2022/aiml/Pencil_Beam/Pencil_Cher_ML.ipynb
  • Try to get this notebook working in a container by creating a requirements.txt file (use this site as a general guide)
  • We will cover this in detail tomorrow

Homework¶

• Read chapters 0-3 and finish exercises

---

May 31st, 2024¶

Clone a git repository into the Docker container by adding a line to the Dockerfile¶

• Using the text editor (e.g., vim, gedit, or emacs) of your choice, edit the Dockerfile with the following information (the python tutorial GitHub repo is used as an example)
  

  # Use the official Python 3.9 image as the base image
  FROM python:3.9
  
  # Install Jupyter Notebook and other dependencies
  RUN pip install --no-cache-dir jupyter
  
  # Create a working directory
  WORKDIR /workspace
  
  # Clone GitHub repo
  RUN git clone https://github.com/trottar/UVA_summer_students.git
  
  # Expose the Jupyter Notebook port
  EXPOSE 8888
  
  # Set the default command to start Jupyter Notebook
  CMD ["jupyter", "notebook", "--ip=0.0.0.0", "--port=8888", "--no-browser", "--allow-root"]
  

Quick requirements.txt introduction¶

• requirements.txt is a way to easily install many python packages by recursively reading through a text file
• This text file can either be generated...
  • Manually, by writing the required python packages into the text file.
    • Note: it is good practice to specify the version of the package. Such as...
      

      matplotlib==3.3.4
      

  • Automatically, by running the following command
    

    pip freeze > requirements.txt
    

    • Note: This command will include all packages installed.

• A Docker container can install all these packages upon building its image by adding a few lines to the Dockerfile
• Using the text editor (e.g., vim, gedit, or emacs) of your choice, edit the Dockerfile with the following information
  

  # Use the official Python 3.9 image as the base image
  FROM python:3.9
  
  # Use the most up-to-date version of pip3.9
  RUN pip install --upgrade pip
  
  # Install git and ssh
  RUN apt-get update && apt-get install -y git openssh-client
  
  # Create a working directory
  WORKDIR /workspace
  
  # Clone GitHub
  RUN git clone https://github.com/trottar/solid_beamtest_hallc_2022.git
  
  # copy the dependencies file to the working directory
  COPY requirements.txt .
  
  # install dependencies
  RUN pip install -r requirements.txt
  
  # Expose the Jupyter Notebook port
  EXPOSE 8888
  
  # Set the default command to start Jupyter Notebook
  CMD ["jupyter", "notebook", "--ip=0.0.0.0", "--port=8888", "--no-browser", "--allow-root"]
  

• Note: I also made some other adjustments such as...
  • Moved the pip installation of jupyter directly into the requirements.txt
  • Added an installation for git and ssh to avoid completely clean image creations.
  • Added a pip install --upgrade pip to assure pip is the latest version for python3.9

Useful Docker Commands¶

• Remove all stopped containers (use with caution)
  

  docker system prune -a
  

Near-term Goals¶

1. Take a look at the GitHub ML Beam Test Repository
• In particular, look at the script solid_beamtest_hallc_2022/aiml/Pencil_Beam/Pencil_Cher_ML.ipynb
  • Try to get this notebook working in a container by creating a requirements.txt file (use this site as a general guide)

Homework¶

• Read chapters 0-3 and finish exercises

---

June 3rd, 2024¶

First Time General Docker Procedure¶

1. Clear out previous images and containers
   

   docker system prune -a
   

2. Build the Docker image and run the container with bash script
   

   ./run_docker.sh beamtest
   

3. While the container is running, create snapshots of changes by committing
   

   ./commit_docker.sh beamtest
   

4. IMPORTANT Properly stop a container by checking its ID with docker ps then
   

   docker stop <contatiner_ID>
   

Running a snapshot¶

• Snapshots are built images so no rebuilding required
• To run a container of a snapshot...
1. Check out all built images/snapshots
   

   docker images
   

2. Run the specific snapshot
   

   docker run -p 8888:8888 <snapshot_name>
   

3. While the container of snapshot is running, create further snapshots of changes by committing
   

   ./commit_docker.sh beamtest
   

4. IMPORTANT Properly stop a container by checking its ID with docker ps then
   

   docker stop <contatiner_ID>
   

Near-term Goals¶

1. Take a look at the GitHub ML Beam Test Repository
• In particular, look at the script solid_beamtest_hallc_2022/aiml/Pencil_Beam/Pencil_Cher_ML.ipynb
  • Try to get this notebook working in a container by creating a requirements.txt file (use this site as a general guide)
• Compare the plots in this notebook with those in Darren's report to try and get an understanding.

Homework¶

• Read chapters 0-4 and finish exercises

---

June 4th, 2024¶

In a Docker container, grab CSV files from a download link to use in Jupyter scripts

• Using the text editor (e.g., vim, gedit, or emacs) of your choice, edit the Dockerfile with the following information
  

  # Use the official Python 3.9 image as the base image
  FROM python:3.9
  
  # Use the most up-to-date version of pip3.9
  RUN pip install --upgrade pip
  
  # Install git and ssh
  RUN apt-get update && apt-get install -y git openssh-client
  
  # Create a working directory
  WORKDIR /workspace
  
  # Clone GitHub
  RUN git clone https://github.com/trottar/solid_beamtest_hallc_2022.git
  
  # copy the dependencies file to the working directory
  COPY requirements.txt .
  
  # install dependencies
  RUN pip install -r requirements.txt
  
  # Create new directory (Sim_CSV) to store data files for simulations
  RUN mkdir -p /workspace/solid_beamtest_hallc_2022/aiml/Pencil_Beam/Sim_CSV
  
  # Grab CSV files from OneDrive
  RUN wget -O /workspace/solid_beamtest_hallc_2022/aiml/Pencil_Beam/Sim_CSV/Bkg_CherChannels.csv "https://myuva-my.sharepoint.com/:x:/g/personal/nar2rk_virginia_edu/Ee9RmPVcNwdIjUTFcES0XXIB7H0mNnieXicFxWfVobY0vg?e=8TJFbN&download=1" 
  RUN wget -O /workspace/solid_beamtest_hallc_2022/aiml/Pencil_Beam/Sim_CSV/Sim_CherChannels.csv "https://myuva-my.sharepoint.com/:x:/g/personal/nar2rk_virginia_edu/EbeTrS8gka9GpzQx3ZMjev4BvrqUJMogjMQ6WfiaVdn7JA?e=N50RL9&download=1" 
  RUN wget -O /workspace/solid_beamtest_hallc_2022/aiml/Pencil_Beam/Sim_CSV/Sim_CherChannels_500k.csv "https://myuva-my.sharepoint.com/:x:/g/personal/nar2rk_virginia_edu/ESutJhLkFyFLqOTwHNPYy9YB50Tvwmx2La2MZrgTLtTUww?e=PPbXln&download=1" 
  RUN wget -O /workspace/solid_beamtest_hallc_2022/aiml/Pencil_Beam/Sim_CSV/Sim_Pencil.csv "https://myuva-my.sharepoint.com/:x:/g/personal/nar2rk_virginia_edu/EbWm9NHCFh1MhpPM72nkXQIBF4D23Y62KGDNK38-hr_kgA?e=iGtYba&download=1" 
  RUN wget -O /workspace/solid_beamtest_hallc_2022/aiml/Pencil_Beam/Sim_CSV/Sim_Pencil_AllEvents.csv "https://myuva-my.sharepoint.com/:x:/g/personal/nar2rk_virginia_edu/EaBQxnnHPcZFgVI3gizyWtcBzjLd9NNvToYnUmFREopwSA?e=kLX1Sd&download=1" 
  RUN wget -O /workspace/solid_beamtest_hallc_2022/aiml/Pencil_Beam/Sim_CSV/Sim_Pencil_AllEvents_TID1.csv "https://myuva-my.sharepoint.com/:x:/g/personal/nar2rk_virginia_edu/EYqKKiXhdIxNo1EuUhzuDYcB7PhYbD0Cuv0fiG1CfiJwdQ?e=a6XeeS&download=1" 
  RUN wget -O /workspace/solid_beamtest_hallc_2022/aiml/Pencil_Beam/Sim_CSV/Sim_Pencil_Bkg.csv "https://myuva-my.sharepoint.com/:x:/g/personal/nar2rk_virginia_edu/EbDFEIyjgPxDkMEd4gJYHFEB6fmfdPkbuNYGGC6QBM4hBQ?e=n1gIEW&download=1" 
  RUN wget -O /workspace/solid_beamtest_hallc_2022/aiml/Pencil_Beam/Sim_CSV/Sim_Pencil_CherChannels_AllEvents.csv "https://myuva-my.sharepoint.com/:x:/g/personal/nar2rk_virginia_edu/EQBKDvJ9_TxLoNTXfZfMxcIBFmFaDHdhfTILSv-C5hExhA?e=BZ2dfM&download=1" 
  RUN wget -O /workspace/solid_beamtest_hallc_2022/aiml/Pencil_Beam/Sim_CSV/Sim_Pencil_CherChannels_AllEvents_TID1.csv "https://myuva-my.sharepoint.com/:x:/g/personal/nar2rk_virginia_edu/EZRZ9H7IDdNMukCrELRv7SwB_QIORnLSUCDUJgZ2s_tj6g?e=GjLuwX&download=1" 
  
  # Expose the Jupyter Notebook port
  EXPOSE 8888
  
  # Set the default command to start Jupyter Notebook
  CMD ["jupyter", "notebook", "--ip=0.0.0.0", "--port=8888", "--no-browser", "--allow-root"]
  

Near-term Goals¶

1. Take a look at the GitHub ML Beam Test Repository
• In particular, look at the script solid_beamtest_hallc_2022/aiml/Pencil_Beam/Pencil_Cher_ML.ipynb
  • Try to get this notebook working in a container by creating a requirements.txt file (use this site as a general guide)
• Compare the plots in this notebook with those in Darren's report to try and get an understanding.

Homework¶

• Read chapters 0-4 and finish exercises