v1.4.0

Summary

This release adds training a model on encrypted data and introduces latency optimization for the inference of tree-based models such as XGBoost, random forest, and decision trees. This optimization offers 2-3x speed-ups in typical quantization settings and allows even more accurate, high bit-width tree-based models to run with good latency.

Links

Docker Image: zamafhe/concrete-ml:v1.4.0
Docker Hub: https://hub.docker.com/r/zamafhe/concrete-ml/tags
pip: https://pypi.org/project/concrete-ml/1.4.0
Documentation: https://docs.zama.ai/concrete-ml

v1.4.0

Feature

• SGDClassifier training in FHE (0893718)
• Support Expand Equal ONNX op (cf3ce49)
• Add rounding feature on cml trees (064eb82)
• Add multi-output support (fef23a9)
• Allow QuantizedAdd produces_output_graph (0b57c71)
• Encrypted gemm support - 3d inputs - better rounding control - sgd training test (111c7e3)

Fix

• Add --no-warnings flag to linkchecker (1dc547e)
• Fix wrong assumption in ReduceSum operator's axis parameter (1a592d7)
• Mark flaky tests due to issue in simulation (4f67883)
• Update learning rate default value for XGB models (e4984d6)

Documentation

• Improve XGBClassifier notebook (b1906a5)
• Update api doc (d8e9e64)
• Update Apple Silicon install information (4c0c02f)