Intricate

github.com

A GPU accelerated library that creates/trains/runs neural networks in safe Rust code.

Architechture overview

Intricate has a layout very similar to popular libraries out there such as Keras.

Models

As said before, similar to Keras from Tensorflow, Intricate defines Models as basically a list of Layers and the definition for "layer" is as follows.

Layers

Every layer receives inputs and returns outputs, they must also implement a back_propagate method that will mutate the layer if needed and then return the derivatives of the loss function with respected to the inputs, written with I as the inputs of the layer, E as the loss and O as the outputs of the layer:

dE/dI <- Model <- dE/dO

These layers can be anything you want and just propagates the previous inputs to the next inputs for the next layer or for the outputs of the whole Model.

There are a few activations already implemented, but still many to be implemented.

XoR using Intricate

If you look at the examples/ in the repository you will find XoR implemented using Intricate. The following is basically just that example with some separate explanation.

Setting up the training data

let training_inputs = vec![
    vec![0.0, 0.0],
    vec![0.0, 1.0],
    vec![1.0, 0.0],
    vec![1.0, 1.0],
];

let expected_outputs = vec![
    vec![0.0],
    vec![1.0],
    vec![1.0],
    vec![0.0],
];

Setting up the layers

use intricate::layers::{
    activations::TanH,
    Dense
};
let mut layers: Vec<ModelLayer> = vec![
    Dense::new(2, 3), // inputs amount, outputs amount
    TanH::new (3),
    Dense::new(3, 1),
    TanH::new (1),
];

Creating the model with the layers

use intricate::Model;
// Instantiate our model using the layers
let mut xor_model = Model::new(layers);

We make the model mut because we will call fit for training our model which will tune each of the layers when necessary.

Setting up OpenCL's state

Since Intricate does use OpenCL under the hood for doing calculations, we do need to initialize a OpenCLState which is just a struct containing some necessary OpenCL stuff:

use intricate::utils::{
    setup_opencl,
    DeviceType
}
//              you can change this device type to GPU if you want
let opencl_state = setup_opencl(DeviceType::CPU).unwrap();

For our Model to be able actually do computations, we need to pass the OpenCL state into an init function inside of the model as follows:

xor_model.init(&opencl_state).unwrap();

Beware that as v0.3.0 of Intricate, any method called before init will panic because they do not have the necessary OpenCL state.

Fitting our model

For training our Model we just need to call the fit method and pass in some parameters as follows:

xor_model.fit(
    &training_inputs, 
    &expected_outputs, 
    TrainingOptions {
        learning_rate: 0.1,
        loss_algorithm: MeanSquared::new(), // The Mean Squared loss function
        should_print_information: true, // Should or not be verbose
        epochs: 10000,
    },
).unwrap(); // Will return an Option containing the last loss after training

As you can see it is extremely easy creating these models, and blazingly fast as well.

How to save and load models

For saving and loading models Intricate uses the savefile crate which makes it very simple and fast to save models.

Saving the model

To load and save data, as an example, say for the XoR model we trained above, we can just call the save_file function as such:

xor_model.sync_gpu_data_with_cpu().unwrap(); // sends the weights and biases from the GPU to the CPU
save_file("xor-model.bin", 0, &xor_model).unwrap();

Which will save all of the configuration of the XoR Model including what types of layers it has inside and the trained parameters of each layer.

Loading the model

As for loading our XoR model, we just need to call the counterpart of save_file: load_file.

let mut loaded_xor_model: Model = load_file("xor-model.bin", 0).unwrap();

Now of curse, savefile cannot load in the GPU state so if you want to use the Model after loading it, you must call the setup_opencl again and initialize the Model with the resulting OpenCLState.

Things to be done still

separate Intricate into more than one crate as to make development more lightweight with rust-analyzer
implement convolutional layers and perhaps even solve some image classification problems in a example
have some feature of Intricate, that should be optional, that would contain preloaded datasets, such as MNIST and others
write many more unit tests to make code safer, like a test for the backprop of every activation layer
perhaps write some kind of utility functions to help with writing repetitive tests for the backprop of activation functions
improve documentation of Intricate overall, like adding at least a general description for every mod

intricate 0.3.0