ts-scientific-data-and-ai-cloud-pipeline-examples

TetraScience Scientific Data and AI Cloud Pipeline Examples

Getting started

• Install Python 3.8 or higher (We recommend using pyenv if you have to work with multiple versions on the same machine.)
• Install Poetry for dependency management and packaging in Python.

Overview

Our Developer Documentation provides details on how to build self-service pipelines, by building out new protocols and task scripts.

Current Examples in this repo:

• Using R Scripts in Tetra Pipeline

Contribute

We welcome contributions from the community that document the use of our APIs to accelerate scientific workflows. Here are more details on how to contribute.

Disclaimer

The examples in this repository are not fully supported product features. They are example use cases to demonstrate the capability of TetraScience's APIs. You are responsible for any code maintenance, deployment, and validation using these examples.