SurrealMl

This package is for storing machine learning models with meta data in Rust so they can be used on the SurrealDB server.

What is SurrealML?

SurrealML is a feature that allows you to store trained machine learning models in a special format called 'surml'. This enables you to run these models in either Python or Rust, and even upload them to a SurrealDB node to run the models on the server

Prerequisites

1. A basic understanding of Machine Learning: You should be familiar with ML concepts, algorithms, and model training processes.
2. Knowledge of Python: Proficiency in Python is necessary as SurrealML involves working with Python-based ML models.
3. Familiarity with SurrealDB: Basic knowledge of how SurrealDB operates is required since SurrealML integrates directly with it.
4. Python Environment Setup: A Python environment with necessary libraries installed, including SurrealML, PyTorch or SKLearn (depending on your model preference).
5. SurrealDB Installation: Ensure you have SurrealDB installed and running on your machine or server

Running CI locally

Running CI locally can be done with the following command:

cargo make --no-workspace preflight

This runs a series of tests in docker containers for dynamic C lib loading and core tests for sklearn, tensorflow, and pytorch.

Modules

Here is where we house the rust modules for surrealml.

• core: This pure rust module handles the storage, loading, and running of ML models. Core is compiled into the SurrealDB server so the ML execution code runs on the server.