omniverse 0.0.13

A collection of code for Omniverse.

Omniverse

• Omniverse
  • Building and Running the Jupyter Book Docker Image
    • Building the Docker Image
    • Running the Docker Container
    • Stopping the Docker Container
    • Further Enhancements
    • References and Further Readings
  • Release using GitHub Actions CI/CD Workflows
    • Semantic Versioning
      • Format
      • Example Versioning
      • Pre-release and Build Metadata
    • Release using GitHub Actions CI/CD Workflows
    • Example Workflow
    • References and Further Readings

🌌 Omniverse: A cosmic collection of machine learning, deep learning, data science, math, and software engineering explorations. Dive into the universe of knowledge! 🚀 To create a detailed Markdown section in the README.md file for instructing users on how to build and run the Dockerfile jupyterbook.Dockerfile, you should include steps that cover prerequisites, building the Docker image, tagging it with the Git commit ID, and running the container. Additionally, to avoid hardcoding variables in the Docker build/run commands, you can use shell variables and command substitutions.

Building and Running the Jupyter Book Docker Image

This section provides detailed instructions on how to build the Dockerfile (docker/documentation/jupyterbook.Dockerfile) and run the Docker image. The image provides a containerized environment for building and serving the Jupyter Book website.

[!WARNING] This section is only tested on macOS Ventura 13.4.1.

First, ensure you are in the root directory of the repository, if not, change directories to the root directory:

~/ $ cd <path/to/omniverse>

Replace <path/to/omniverse> with the actual path to your repository's root directory.

Building the Docker Image

1. Set Environment Variables: Set the GIT_COMMIT_HASH, IMAGE_NAME, and IMAGE_TAG environment variables. These will be used to tag your Docker image uniquely.

   ~/omniverse $ export GIT_COMMIT_HASH=$(git rev-parse --short HEAD)
   ~/omniverse $ export IMAGE_NAME=omniverse
   ~/omniverse $ export IMAGE_TAG=$GIT_COMMIT_HASH
   

2. Build the Image: Execute the following Docker command to build the image, specifying the Dockerfile path and assigning the tag based on the previously set environment variables.

   ~/omniverse $ docker build \
   >   --file docker/documentation/jupyterbook.Dockerfile \
   >   --tag $IMAGE_NAME:$IMAGE_TAG \
   >   .
   

Running the Docker Container

To run the Docker container:

~/omniverse $ docker run \
>   --publish 80:80 \
>   $IMAGE_NAME:$IMAGE_TAG

This command will start a container from the built image, mapping port 80 of the container to port 80 on the host machine. The website should now be accessible at http://localhost:80.

Stopping the Docker Container

To stop the Docker container:

~/omniverse $ export CONTAINER_ID=$(docker ps --filter ancestor=$IMAGE_NAME:$IMAGE_TAG --format "{{.ID}}")
~/omniverse $ docker stop $CONTAINER_ID

Further Enhancements

This is a relatively simple Dockerfile. Further enhancements can include, but not limited to:

• Add entrypoint script to start the Jupyter Book server.
• Use Docker Compose to manage multiple containers, for example, a container for development of the content and a container for serving the website.
• Current Docker image is used primarily for serving, and users may find it hard to directly develop inside the container. A better approach is to use a Docker image for development, and mount the source code directory to the container. This way, users can develop on their host machine, and the changes will be reflected in the container.

References and Further Readings

• How to run Nginx within a Docker container without halting?

Release using GitHub Actions CI/CD Workflows

Semantic Versioning

The conventional way to name software versions is by following Semantic Versioning (SemVer). Semantic Versioning is a widely adopted system for versioning software in a way that conveys meaning about the underlying changes. It helps in managing dependencies and avoiding compatibility issues.

Format

A Semantic Version number is typically formatted as MAJOR.MINOR.PATCH, where:

1. MAJOR version:

   • Incremented for incompatible API changes or major changes in functionality.
   • Indicates that the new version might not be backward compatible with previous major versions.

2. MINOR version:

   • Incremented for adding functionality in a backward-compatible manner.
   • Indicates new features or improvements which do not break existing functionalities.

3. PATCH version:

   • Incremented for backward-compatible bug fixes.
   • Focuses on resolving bugs and issues without adding new features or breaking existing functionality.

Example Versioning

• 1.0.0: The first stable release of a software.
• 1.0.1: A subsequent release that includes bug fixes but no new features.
• 1.1.0: A release that introduces new features but is still backward compatible with the 1.0.x series.
• 2.0.0: A release that makes changes significant enough to potentially break compatibility with the 1.x.x series.

Pre-release and Build Metadata

Semantic Versioning also supports additional labels for pre-release and build metadata:

• Pre-release versions can be denoted with a hyphen and additional identifiers (e.g., 2.0.0-alpha, 2.0.0-beta.1).
• Build metadata can be appended using a plus sign and additional identifiers (e.g., 2.0.0+build.20230315).

Release using GitHub Actions CI/CD Workflows

Follow the guide here for detailed instructions on how to release a Python package using GitHub Actions CI/CD workflows.

One thing that is not mentioned in the guide, but is a good practice, is to do all the necessary pre-release checks before releasing your package, that includes all the pre-merge checks, and additional checks such as running tests, linting, and building the documentation. This ensures that the release is of high quality and is ready to be used by others.

Furthermore, we add a release-docker workflow to build and publish a Docker image to Docker Hub. The workflow is triggered when a new release is published to PyPI. This approach is inspired by langchain's workflow for publishing a Docker image via GitHub Actions. The rationale behind incorporating a Docker release alongside the PyPI release is to ensure the package omniverse is able to be imported and used across different platforms.

Example Workflow

Update the version field in pyproject.toml to the new version, and commit the changes to the main branch (or any other branch that satisfies the on.push.branches condition in the workflow).

git commit -m "bump version to 0.0.13"
git tag -a v0.0.13 -m "Release version 0.0.13"
git push origin main
git push origin v0.0.13

Then the workflow will be triggered, and the package will be published to PyPI. It is worth noting that we will do a pre-release check before publishing the package.

References and Further Readings

• Packaging Projects - Python Packaging User Guide
• Publishing Package Distribution Releases Using GitHub Actions CI/CD Workflows - Python Packaging User Guide
• Publishing Python Packages
• Publishing Python Packages from GitHub Actions