Overview
What are Ritual Machine Learning Workflows?
Ritual provides easy-to-use abstractions for users to create AI/ML workflows that can be deployed on Infernet nodes.
The infernet-ml
(opens in a new tab) library is a Python SDK that provides a set of tools and extendable classes for creating and
deploying machine learning workflows. It is designed to be easy to use, and provides a consistent interface for data pre-processing, inference, and post-processing.
pre-processing, inference, and post-processing of data.
Batteries Included
We provide a set of pre-built workflows for common use-cases. We have workflows for running ONNX models, Torch models, any Huggingface model via Huggingface inference client (opens in a new tab) and even for closed-source models such as OpenAI's GPT-4.
Getting Started
Head over to the next section for installation and a quick walkthrough of the ML workflows.
Tutorials & Videos
Head over to Ritual Learn (opens in a new tab) for more end-to-end tutorials and examples that use this library, including:
- Prompt to NFT: A tutorial on using Infernet to create & mint an NFT generated by stable diffusion from a prompt.
- Running a Torch/ONNX Model: We import the same model in two formats: as an
.onnx
file & as a.torch
file, and useinfernet-ml
(opens in a new tab)'s workflows to invoke it either from a smart contract or frominfernet-node
's REST API. - TGI Inference: A tutorial on running Mistral-7b on Infernet, and optionally delivering its output to a smart-contract.
- GPT-4 Inference: A tutorial on how to use our closed-source-inference workflows to easily integrate with OpenAI's completions API.