This package is for storing machine learning models with meta data in Rust so they can be used on the SurrealDB server.
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
- A basic understanding of Machine Learning: You should be familiar with ML concepts, algorithms, and model training processes.
- Knowledge of Python: Proficiency in Python is necessary as SurrealML involves working with Python-based ML models.
- Familiarity with SurrealDB: Basic knowledge of how SurrealDB operates is required since SurrealML integrates directly with it.
- Python Environment Setup: A Python environment with necessary libraries installed, including SurrealML, PyTorch or SKLearn (depending on your model preference).
- SurrealDB Installation: Ensure you have SurrealDB installed and running on your machine or server
We have removed PyO3 for a raw dynamic C lib written in rust. This is how working with Python and we can also link this dynamic C lib to other languages such as JavaScript. The new Python client is housed in the clients
directory. Please visit this for the updated installation and API docs.
Running CI locally can be done with the following command:
cargo make --no-workspace preflightThis runs a series of tests in docker containers for dynamic C lib loading and core tests for sklearn, tensorflow, and pytorch.