ML beam test PID Meetings » History » Revision 23
Revision 22 (Richard Trotta, 05/29/2024 12:32 PM) → Revision 23/73 (Richard Trotta, 05/29/2024 12:32 PM)
{{>toc}}
h1. ML beam test PID Meetings
---
h2. Summer 2024
---
h3. May 27th, 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":https://drive.google.com/file/d/1-TPLo5VGSwCUDjqj_j4g--p4FbBgYHbw/view?usp=sharing\
* "GitHub and Python Introduction":https://github.com/trottar/UVA_summer_students
** Navigate to "python_tutorials":https://github.com/trottar/UVA_summer_students/tree/master/python_tutorials and read through the two html files ("Introduction":https://github.com/trottar/UVA_summer_students/blob/master/python_tutorials/Introduction.ipynb and "python_tutorial_2":https://github.com/trottar/UVA_summer_students/blob/master/python_tutorials/python_tutorial_2.ipynb)
*** Note, these are html files so you'll need
h4. Near-term Goals
# Setup Linux Subsystem for Windows
# Fork the ML Beam Testing GitHub repository
# Containerize ML Beam Testing GitHub repository with Docker
h4. Homework
* "Read chapters 0-3 and finish exercises":https://drive.google.com/file/d/1-TPLo5VGSwCUDjqj_j4g--p4FbBgYHbw/view?usp=sharing
---
h3. May 27th, 2024
h4. Setting Up Jupyter Notebooks in Docker
# Create a directory to store, build, and run Docker container
<pre><code class="bash">
cd /path/to/directory
mkdir beamtest_dir
cd beamtest_dir
</code></pre>
# Once in the new directory, create a Dockerfile
<pre><code class="bash">
touch Dockerfile
</code></pre>
# Using the text editor (e.g., vim, gedit, or emacs) of your choice, edit the Dockerfile with the following information
<pre><code class="bash">
# 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"]
</code></pre>
# Build the Docker image (e.g., helloworld)
<pre><code class="bash">
docker build -t helloworld .
</code></pre>
# Run the Docker container
</code></pre>
# Build the Docker image (e.g., helloworld)
<pre><code class="bash">
docker run -p 8888:8888 helloworld
</code></pre>
# Jupyter Notebook is now running. Navigate to a browser and type in either
* The custom token authorization screen</code></pre>
# Build the Docker image (e.g., helloworld)
<pre><code class="bash">
http://localhost:8888
</code></pre>
* To bypass the token screen, copy/paste the URL that splashes in the terminal into your browser