This is a set of Cookiecutter templates in the form of Kedro starters. These starters allow to easily create a new project that doesn't implement any nodes or pipelines yet, but contains necessary tooling and follows all QuickStart ML Blueprints principles.
QuickStart ML Blueprints repository and documentation with detailed description of the way of work can be found here.
Initiating a project using one of the Kedro starters you will get out-of-the box:
- appropriate project structure matching Kedro and Cookiecutter standard that features configuration files, code testing framework, layered data-engineering convention and more
- VSCode Dev Containers and Docker setup files to create a transferrable working environment automatically
- MLFlow and Kedro-Viz
- A set of pre-configures environment management and code quality tools (Poetry, pre-commit hooks, linters)
- Accordingly to your target full-scale environment - Kedro plugins setup for easy transfer and running your local work on GCP, AWS, Azure or Kubeflow
There are a few branches in the repository that use basically the same template, but have environment-specific additions depending on where are you planning to run your full-scale solution after local prototyping phase:
local- if you plan to stay in local environmentlocal-gcp- if you plan to transfer your project to Google Cloud (VertexAI)- (to be added )
local-aws- if you plan to transfer your project to AWS (Sagemaker) - (to be added )
local-azure- if you plan to transfer your project to Azure (AzureML) - (to be added )
local-kuberflow- if you plan to transfer your project to Kubeflow
To use this Kedro starter you to have some Python 3 environment with Kedro installed. The method of installation is up to you (you can use Pyenv and Poetry, Conda, Virtual Env etc.) - this installation Kedro is only needed to create a project from a starter. After that, the project will use its own encapsulated Pyenv/Poetry environment with its own Kedro.
To create a new project using Kedro starter:
# For HTTPS cloning:
kedro new --starter=https://github.com/getindata/quickstart-ml-starter.git --checkout=<branch_name>
# For SSH cloning:
kedro new [email protected]:getindata/quickstart-ml-starter.git --checkout=<branch_name>
# Follow the prompts to name your project and optionally set cloud project details, then change directory into newly created project directory:
cd <my-project-name>After that, follow the way of work described in QuickStart ML Blueprints to develop your project.