auto-zkml 0.1.0

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auto-zkml

This package is designed to provide functionality that facilitates the transition from ML algorithms to ZKML. Its two main functionalities are:

• Serialization: saving a trained ML model in a specific format to be interpretable by other programs.

• model-complexity-reducer (mcr): Given a model and a training dataset, transform the model and the data to obtain a lighter representation that maximizes the tradeoff between performance and complexity.

It's important to note that although the main goal is the transition from ML to ZKML, auto-zkml can be useful in other contexts, such as:

• The model's weight needs to be minimal, for example for mobile applications.
• Minimal inference times are required for low latency applications.
• We want to check if we have created an overly complex model and a simpler one would give us the same performance (or even better).
• The number of steps required to perform the inference must be less than X (as is currently constrained by the ZKML paradigm).

Installation

Install from PyPi

For the latest release:

pip install auto-zkml

Installing from source

Clone the repository and install it with pip:

    git clone [e-mail]:gizatechxyz/auto-zkml.git
    cd auto-zkml
    pip install .

Serialization

To see in more detail how this tool works, check out this tutorial.

To run it:

from auto_zkml import serialize_model

serialize_model(YOUR_TRAINED_MODEL, "OUTPUT_PATH/MODEL_NAME.json")

mcr

To see in more detail how this tool works, check out this tutorial.

To run it:

model, transformer = mcr(model = MY_MODEL,
                         X_train = X_train,
                         y_train = y_train,
                         X_eval = X_test,
                         y_eval = y_test,
                         eval_metric = 'rmse',
                         transform_features = True)

Supported Models

Model	status
XGBRegressor	✅
XGBClassifier	✅
LGBMRegressor	✅
LGBMClassifier	✅
Logistic Regression	⏳
GARCH	⏳