0.3

Getting Started

Built-in Models

Deep Learning

Advanced topics

Using Hummingbird

Hummingbird is a third party open-source library that converts machine learning models into tensor computations. Many algorithms (see supported algorithms) are converted using a specific backend (torch, torchscript, ONNX and TVM).

Concrete-ML allows the conversion of an ONNX inference to NumPy inference (note that NumPy is always the entry point to run models in FHE with Concrete ML).

Usage

Hummingbird exposes a

`convert`

function that can be imported as follows from the `hummingbird.ml`

package:# Disable Hummingbird warnings for pytest.

import warnings

warnings.filterwarnings("ignore")

from hummingbird.ml import convert

This function can be used to convert a machine learning model to an ONNX as follows:

from sklearn.datasets import make_classification

from sklearn.linear_model import LogisticRegression

# Instantiate the logistic regression from sklearn

lr = LogisticRegression()

# Create synthetic data

X, y = make_classification(

n_samples=100, n_features=20, n_classes=2

)

# Fit the model

lr.fit(X, y)

# Convert the model to ONNX

onnx_model = convert(lr, backend="onnx", test_input=X).model

In theory, the resulting

`onnx_model`

could be used directly within Concrete-ML's `get_equivalent_numpy_forward`

method (as long as all operators present in the ONNX model are implemented in NumPy) and get the NumPy inference.In practice, there are some steps needed to clean the ONNX output and make the graph compatible with Concrete-ML, such as applying quantization where needed or deleting/replacing non-FHE friendly ONNX operators (such as *Softmax* and *ArgMax).*

Last modified 20d ago

Export as PDF

Copy link