cauldron-ml 0.6.1

A CLI tool for building and deploying Kubeflow Pipelines in GCP Vertex AI

CauldronML: A CLI tool for building and deploying Kubeflow Pipelines in GCP Vertex AI

Quickstart (10 mins)

Before you start, we recommend installing a python environment manager. Cauldron uses uv in the build process for it's speed, but you don't need to use it locally. We prefer pyenv for it's user-friendliness and automatic switching between projects.

If you don't already have one installed, you can install pyenv in one line.

Installation requirements

Cauldron works with unix-based systems like MacOS and Linux. It does not currently work on Windows. For windows users, we recommend installing Windows Subsystem for Linux (WSL) but we have not tested this.

Install pyenv

curl https://pyenv.run | bash

Install a compatible python version and virtual envioronment

Python >=3.10 is supported, although python >=3.12 is recommended as this will allow you to use the latest versions of kubeflow (kfp) and google-cloud-aiplatform.

Start with a blank folder

mkdir my-project
cd my-project

Install python 3.12, create a new virtualenv and set it to auto-activate in the current folder

pyenv install 3.12
pyenv virtualenv 3.12 my-project
pyenv local my-project

Install cauldron-ml

pip install git+https://github.com/dprice80/cauldron-ml.git

Verify the installation and check the CLI tool is registered

caul --help

Help output should be displayed

Initialise Cauldron

caul init

This will create a yaml file called ~/.caulprofile. This file should be modified by the user with the following settings:

docker-repo: <repo-address>/subfolder  # no trailing slash
docker-base-image: python:3.12-slim  # By default, cauldron pipelines work with slim versions of python images (tested with 3.12).
user-prefix: jbloggs
production-project-name: foo-bar-prod
sandbox-project-name: foo-bar-sandbox
production-service-account: my-sa@[e-mail]
sandbox-service-account: my-sa@[e-mail]

Note that docker-repo should include the folder where the images are stored. The production-project-name and production-service-account can be the same as the sandbox accounts. The prefix and image tags are set within the CI pipeline workflow, not by cauldron, so the user can decide to run all production pipelines and development pipelines in the same GCP project, although we do not recommend this.

We will soon add a github action to take care of the build process, setting the arguments correctly etc. At MSM we use a matrix build strategy to build all our pipelines within a mono-repo, only building new images when we detect a change in that project.

caul create

caul build --base

caul build

caul deploy

caul run

You should now have a running pipeline.