#machine-learning #surrealdb #models #onnx #run-time #load #surreal-ml

surrealml-core

The core machine learning library for SurrealML that enables SurrealDB to store and load ML models

12 releases

0.1.3 Apr 16, 2024
0.1.2 Apr 2, 2024
0.1.1 Mar 25, 2024
0.0.8 Jan 26, 2024
0.0.1 Oct 31, 2023

#86 in Machine learning

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1,586 downloads per month
Used in 9 crates (5 directly)

Custom license

110KB
2K SLoC

Surml Core

An embedded ONNX runtime directly in the Rust binary when compiling result in no need for installing ONNX runtime separately or worrying about version clashes with other runtimes.

This crate is just the Rust implementation of the Surml API. It is advised that you just use this crate directly if you are running a Rust server. It must be noted that the version of ONNX needs to be the same as the client when using this crate. For this current version of Surml, the ONNX version is 1.16.0.

Compilation config

If nothing is configured the crate will compile the ONNX runtime into the binary. This is the default behaviour. However, if you want to use an ONNX runtime that is installed on your system, you can set the environment variable ONNXRUNTIME_LIB_PATH before you compile the crate. This will make the crate use the ONNX runtime that is installed on your system.

This houses reusable errors that are used across all the crates in the Surml ecosystem, and these errors can construct HTTP responses for the Axum and Actix web frameworks.

Nix Support

At this point in time NIX is not directly supported. The ONNXRUNTIME_LIB_PATH needs to be defined. This is explained in the Compilation config section.

Usage

Surml can be used to store, load, and execute ONNX models.

Storing and accessing models

We can store models and meta data around the models with the following code:

use std::fs::File;
use std::io::{self, Read, Write};

use surrealml_core::storage::surml_file::SurMlFile;
use surrealml_core::storage::header::Header;
use surrealml_core::storage::header::normalisers::{
    wrapper::NormaliserType,
    linear_scaling::LinearScaling
};


// load your own model here (surrealml python package can be used to convert PyTorch,
// and Sklearn models to ONNX or package them as surml files)
let mut file = File::open("./stash/linear_test.onnx").unwrap();
let mut model_bytes = Vec::new();
file.read_to_end(&mut model_bytes).unwrap();

// create a header for the model
let mut header = Header::fresh();
header.add_column(String::from("squarefoot"));
header.add_column(String::from("num_floors"));
header.add_output(String::from("house_price"), None);

// add normalisers if needed
header.add_normaliser(
    "squarefoot".to_string(),
    NormaliserType::LinearScaling(LinearScaling { min: 0.0, max: 1.0 })
);
header.add_normaliser(
    "num_floors".to_string(),
    NormaliserType::LinearScaling(LinearScaling { min: 0.0, max: 1.0 })
);

// create a surml file
let surml_file = SurMlFile::new(header, model_bytes);

// read and write surml files
surml_file.write("./stash/test.surml").unwrap();
let new_file = SurMlFile::from_file("./stash/test.surml").unwrap();
let file_from_bytes = SurMlFile::from_bytes(surml_file.to_bytes()).unwrap();

Executing models

We you load a surml file, you can execute the model with the following code:

use surrealml_core::storage::surml_file::SurMlFile;
use surrealml_core::execution::compute::ModelComputation;
use ndarray::ArrayD;
use std::collections::HashMap;


let mut file = SurMlFile::from_file("./stash/test.surml").unwrap();

let compute_unit = ModelComputation {
    surml_file: &mut file,
};

// automatically map inputs and apply normalisers to the compute if this data was put in the header
let mut input_values = HashMap::new();
input_values.insert(String::from("squarefoot"), 1000.0);
input_values.insert(String::from("num_floors"), 2.0);

let output = compute_unit.buffered_compute(&mut input_values).unwrap();

// feed a raw ndarray into the model if no header was provided or if you want to bypass the header
let x = vec![1000.0, 2.0];
let data: ArrayD<f32> = ndarray::arr1(&x).into_dyn();

// None input can be a tuple of dimensions of the input data
let output = compute_unit.raw_compute(data, None).unwrap();

ONNX runtime assets

We can find the ONNX assets with the following link:

https://github.com/microsoft/onnxruntime/releases/tag/v1.16.2

Dependencies

~5–22MB
~272K SLoC