v1.4.0-rc2

Summary

1.4.0 - Release Candidate - 2

Links

Docker Image: zamafhe/concrete-ml:v1.4.0-rc2
Docker Hub: https://hub.docker.com/r/zamafhe/concrete-ml/tags
pip: https://pypi.org/project/concrete-ml/1.4.0-rc2
Documentation: https://docs.zama.ai/concrete-ml

v1.4.0-rc2

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

• Update api doc (d8e9e64)
• Update Apple Silicon install information (4c0c02f)

v1.4.0-rc1

Summary

1.4.0 - Release Candidate - 1

Links

Docker Image: zamafhe/concrete-ml:v1.4.0-rc1
Docker Hub: https://hub.docker.com/r/zamafhe/concrete-ml/tags
pip: https://pypi.org/project/concrete-ml/1.4.0-rc1
Documentation: https://docs.zama.ai/concrete-ml

v1.4.0-rc1

Feature

• 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

• Update api doc (d8e9e64)
• Update Apple Silicon install information (4c0c02f)

v1.3.0

Summary

Adds SGDRegressor built-in model and some bugfixes.

Links

Docker Image: zamafhe/concrete-ml:v1.3.0
Docker Hub: https://hub.docker.com/r/zamafhe/concrete-ml/tags
pip: https://pypi.org/project/concrete-ml/1.3.0
Documentation: https://docs.zama.ai/concrete-ml

v1.3.0

Feature

• Add SGD regressor (abb143c)

Fix

• Fix shape output mismatch for KNNClassifier (6de7c6e)

v1.2.1

Summary

Bug fix for XGBoostRegressor.

Links

Docker Image: zamafhe/concrete-ml:v1.2.1
pip: https://pypi.org/project/concrete-ml/1.2.1
Documentation: https://docs.zama.ai/concrete-ml

v1.2.1

v1.2.0

Summary

This new version of Concrete ML adds support for hybrid deployment and K-nearest neighbor classification. Hybrid deployment with FHE is an approach that improves on-premise deployment by converting parts of the model to remote FHE computation, in order to protect model intellectual property (IP), ensure license compliance and facilitate usage monitoring. The 1.2 version also adds an improvement to the built-in neural networks, making them 10x faster out-of-the-box.

Links

Docker Image: zamafhe/concrete-ml:v1.2.0
pip: https://pypi.org/project/concrete-ml/1.2.0
Documentation: https://docs.zama.ai/concrete-ml

v1.2.0

Feature

• Enable import of fitted linear sklearn models (771c7ff)
• Support QAT models in hybrid model (526b000)
• Expose statuses to compile torch (8abddf6)
• Add KNN classifier in CML (1c33ec8)
• Add power of two scaling adapter for roundPBS (546fac9)
• Add hybrid FHE models (be6aa6e)

Fix

• Fix confusing print in CNN tutorial of advanced-examples (9136c47)
• Fix path parsing and default in hybrid serving (afd049a)
• Fix flaky padding test (6aaf5f0)
• Fix issues with OMP library (2b61846)
• Make sure structured pruning and unstructured pruning work well together (ada18ab)
• Fix structured pruning crash not caught by test (cafd8d1)
• Fix bad top1 accuracy in cifar_brevitas_training use case (f0a984e)
• Fix flaky double_fit test (3da6408)
• Remove workaround for simulating linear models (3f622bc)
• Re-compute quantization params when re-fitting linear models (3bad62e)

Documentation

• Fix and improve credit scoring use case example (e4db376)
• Update contribution part (f2822d1)
• Document KNN, PoT, Hybrid models (68a0b4c)
• Update mnist CNN (f80c90b)
• Update mnist Fully Connected example with PoT + rounding (6e3d003)
• Update cifar_brevitas_training accuracy using representative calibration set (39480ef)
• Correct n_bits markdown value in the LLM use case notebook (0cf1174)

v1.2.0-rc0

Summary

Trying out the new release process.

Links

Docker Image: zamafhe/concrete-ml:v1.2.0-rc0
pip: https://pypi.org/project/concrete-ml/1.2.0-rc0
Documentation: https://docs.zama.ai/concrete-ml

v1.2.0-rc0

Feature

• Add hybrid FHE models (be6aa6e)

Fix

• Flaky test double_fit (3da6408)
• Re-compute quantization parameters when re-fitting linear models (3bad62e)

Documentation

• Add link to deployment use case examples (f9333b3)
• Correct mention of n_bits in LLM use case notebook text (0cf1174)

v1.1.0

Summary

Concrete-ML 1.1.0 adds optimization tools that speed-up the FHE inference time of neural-network models, up to a factor of 20x. Furthermore, this version also improves the support for built-in neural-networks and classical models.

Links

Docker Image: zamafhe/concrete-ml:v1.1.0
pip: https://pypi.org/project/concrete-ml/1.1.0
Documentation: https://docs.zama.ai/concrete-ml

v1.1.0

v1.0.3

Summary

Expose training parameters from scikit-learn in built-in models and add new advanced examples.

Links

Docker Image: zamafhe/concrete-ml:v1.0.3
pip: https://pypi.org/project/concrete-ml/1.0.3
Documentation: https://docs.zama.ai/concrete-ml

v1.0.3

Feature

• Support multi-input QM with inputs of different shapes (6e1315f)
• Add a function to build a quantized module from a model (34e5f68)
• Expose scikit-learn training attributes in built-in models (37ab8c0)
• Add serialization to QNN models (33312a8)

Documentation

• Add example notebook on LinearSVR (f42f0e6)
• Add svm classifier tutorial (49e13ec)
• Add DecisionTreeRegressor to advanced_examples (148e626)
• Add regressor comparison tutorial (6c7cbf7)

v1.0.2

Summary

Fix for the Gather operator to handle fancy indexing.

Links

Docker Image: zamafhe/concrete-ml:v1.0.2
pip: https://pypi.org/project/concrete-ml/1.0.2
Documentation: https://docs.zama.ai/concrete-ml

v1.0.2

v1.0.1

Summary

Fixing minor things, like few typos or rewording the documentation.

Links

Docker Image: zamafhe/concrete-ml:v1.0.1
pip: https://pypi.org/project/concrete-ml/1.0.1
Documentation: https://docs.zama.ai/concrete-ml

v1.0.1