Skip to content

Local installation

The Workbench Python package is compatible with Python 3.11+. Follow this guide to install Python on your machine.

Note

This guide assumes you have a basic understanding of Python. If you're new to Python then we recommend you check out these W3 Schools learning resources first.

Package install

Workbench is privately-managed so isn't publicly available as a package via pip, conda or uv. You'll need to install directly from the Github private repo using the Personal Access Token (PAT) and URL provided to you by Hoppa.

  • git+https://{PAT_TOKEN}@github.com/hoppa-technologies/hoppa-workbench.git

We recommend installing Workbench and all package dependencies into a Python virtual environment. You can access modules in your Python code by including import workbench or from workbench.bindings import ... for sub-module classes, functions etc.

Environment variables

Workbench uses system environment variables to securely manage secrets, tokens and other settings necessary to interact with other services when being used locally. It's recommended to store these variables in a .env file when developing locally. An example .env.example file lists the main environment variables that Workbench classes will expect to reference.

You should never commit your .env file to GitHub, Azure DevOps or any other repository as this expose your environment variables to others and compromise the security of your data. The .gitignore file is configured to exclude .env files.

Code samples

The Python package comes bundled with a set of sample Jupyter notebooks and a sandbox file for experimenting with key concepts.

We'd recommend using Jupyter notebooks for local development as it encourages building and executing chunks of code in a similar fashion to workflows.

Getting started

User guide