i11-ai 0.1.2

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ai-python-common

A common repository for shared python code used within AI/ML projects

Development

The following section details local development setup for this python package.

Pre-Requisites

You will need the following tools setup on your computer

• Visual Studio Code
• Rancher Desktop for running docker
• devcontainers vscode extension

Developing a feature

Once you have all the pre-requisites configured, you can open this project folder within vscode and it will prompt you to open the project inside a container. Choose "yes" to proceed.

NOTE: The first time you open this project in container vscode will build all the container layers. This could take some time, so please be patient

Open a new terminal and you will be connected to the container. You can now use your standard git workflow to create a new branch and start updating code.

Enable pre-commit hook

Enable pre-commit hook to run all configured hooks

make setup

# sample output
No dependencies to install or update

Installing the current project: i11-ai (0.1.0)
pre-commit installed at .git/hooks/pre-commit

Running Tests

Use the Makefile to run tests

make test

# sample output
Installing dependencies from lock file

No dependencies to install or update

Installing the current project: i11-ai (0.1.0)
pre-commit installed at .git/hooks/pre-commit
======================================================================= test session starts =======================================================================
platform linux -- Python 3.11.9, pytest-8.2.2, pluggy-1.5.0
rootdir: /workspaces/ai-python-common
configfile: pyproject.toml
collected 1 item

tests/common_test.py .                                                                                                                                      [100%]

======================================================================== 1 passed in 0.02s ========================================================================

Release

• Create a new feature branch
• Bump the version number in pyproject.toml file
• Update CHANGELOG with relevant updates
• Create a new new PR and review with team
• Once PR is approved, merge the PR to main branch that will trigger a build to publish the package to PyPi