zkml transpiler

zkml on PyPI

Aleo transpiler for building zero-knowledge machine learning applications.

This project provides a Python library named zkml that transpiles scikit-learn machine learning models into a Leo project for inference. The resulting leo project can then be run and executed from Python. The project is at an early stage.

Supported machine learning models currently include:

• Decision tree classifiers
• Multilayer perceptron neural network regressor

Usage

The zkml Python library is available on PyPI for installation.

Prerequisites

Python

Ensure you have 3.9.6 or newer installed.

• Verify with:

  python3 --version
• If not installed, follow the instructions here.

Leo

Ensure you have Leo version 1.9.3 or newer installed.

• Verify with:

  leo --version
• If necessary, update:

  leo update
• Installation guide: Leo Installation

Installation

You can install the zkml Python library from PyPI using the following command:

pip3 install zkml

Note: On some systems, you may need to use pip instead of pip3. Alternatively, you can also install through the .whl file or in editable mode from the GitHub repository.

Usage

Below is a brief description of the classes and functions provided by the library. Detailed documentation is in progress and will be available soon. We encourage you to also check out the examples on GitHub.

• In a first step, you can receive an object of the class zkml.LeoTranspiler(model, validation_data)
  • For the model parameter, pass the trained scikit-learn model
  • For the validation_data parameter, pass the training or validation dataset. While this parameter is not strictly required, we recommend using it. The dataset is used to compute a fixed-point scaling factor and the required Leo integer types. Using the parameter helps to ensure numerical stability in the inference computation. The larger the dataset, the better.
  • When the model is a scikit-learn multilayer perceptron regressor, there are additional optional parameters you can pass. First, the string data_representation_type, which can take the values int or field. It determines if the data is represented as integers or fields inside the circuit. By default, it is int, the field mode is experimental. The second parameter you can pass is the boolean parameter layer_wise_fixed_point_scaling_factor. By default, it is true, which leads to a growing fixed point scaling factor per layer. It can reduce the constraint usage, as there will be no divisions after multiplying two fixed-point numbers. For deep MLP networks (more than 3 layers) or large fixed point scaling factors, however, it may lead to large integers inside the circuit and setting it to false may be worth trying out.
• In a second step, you can start the transpilation process through leo_transpiler.to_leo(path, project_name, model_as_input, fixed_point_scaling_factor)
  • For the path parameter, pass the path where the new leo project should be stored in
  • For the project_name parameter, pass the desired name of the leo project.
  • The boolean model_as_input parameter is optional and by default False. If set to False, the model parameters (i.e., thresholds, weights, biases) are hardcoded in the Leo code. If set to True, these model parameters are treated as additional circuit inputs.
  • The integer fixed_point_scaling_factor parameter is optional and by default None. By default, the transpiler automatically computes a recommended fixed-point scaling factor. In certain cases, this scaling factor can be (by a wide margin) too large, which can result in constraint-rich circuits or numerical instability in inference. With the parameter, you can set the fixed point scaling factor manually. We recommend using powers of 2, and trying out smaller powers values such as 8, 16, 32. Generally, a higher fixed point scaling factor leads to more accurate computations at the cost of a higher circuit constraint count.
• After transpiling, you can run the computation of the Leo project through leo_transpiler.run(input), and receive an object of class LeoComputation
  • For the input parameter, pass the inference data sample or dataset
• Similarly, after transpiling, you can execute the Leo project through leo_transpiler.execute(input), and receive an object of class ZeroKnowledgeProof
  • For the input parameter, pass the inference data sample or dataset

Building Python Apps

Please check out the examples on GitHub. Further documentation and tutorials as to how to use the zkml Python library will follow soon.