v1.5.0

Summary

Concrete ML v1.5 introduces several significant enhancements in this release, including a DataFrame API that enables working with encrypted stored data, a new option that speeds up neural networks by 2-3 times, an improved FHE simulation mode to quickly evaluate the impact of the speed-up on neural network accuracy.

What's Changed

Features:

• Add encrypted dataframe API (d2d6250)
• Add an option to allow approximate rounding to speed up NNs (9ef890e)
• Support ONNX conv1d operator (09ad7a6)
• Implement quantized unfold operation (fa3ef88)

Improvements:

• More accurate FHE simulation
• Encrypted aggregation of the outputs of tree ensembles
• Allow different quantization bits for tree model leaves and inputs

Fix

• Import skorch without errors due to bad docstrings (81de55c)
• Add support to AvgPool's missing parameters (15a8340)

Resources

• Documentation:
  • New structure and landing page (85cb962)
  • Add links to credit card approval space in use case examples (df81aca)
  • Improve contributing section (1696799)
  • Document n_bits for compile torch functions (0306c65)
  • Add explanation of encrypted training and federated learning (57dbdff)
  • Add documentation about scaling (9252f57)
  • Add dataframe documentation (#576) (d3bf5ac)

Links

Docker Image: zamafhe/concrete-ml:v1.5.0
Docker Hub: https://hub.docker.com/r/zamafhe/concrete-ml/tags
pip: https://pypi.org/project/concrete-ml/1.5.0
Documentation: https://docs.zama.ai/concrete-ml

v1.5.0-rc1

Summary

Add support for encrypted data-frames and approximate rounding. Fix AvgPool's count_include_pad missing parameter error.

Links

Docker Image: zamafhe/concrete-ml:v1.5.0-rc1
Docker Hub: https://hub.docker.com/r/zamafhe/concrete-ml/tags
pip: https://pypi.org/project/concrete-ml/1.5.0-rc1
Documentation: https://docs.zama.ai/concrete-ml

v1.5.0-rc1

Feature

• Add new approx rounding (9ef890e)
• Encrypted data-frame (d2d6250)
• Support conv1d operator (09ad7a6)
• Implement quantized unfold (fa3ef88)

Fix

• Fix survey link (e5661c1)
• Reinstate apidoc generated tags (2878a07)
• Make skorch import fail without error (81de55c)
• Replace python release install with setup-python (899b9f1)
• Fix concurrency issue in release process (1295ea9)
• Add support to AvgPool's missing parameters (15a8340)
• Update README.md (ba1fdad)

Documentation

• Add dataframe documentation (#576) (d3bf5ac)
• Update main landing pages (cfb862e)
• New structure and landing page (85cb962)
• Update operator list in torch support's documentation section (b617740)
• Add links to credit card approval space in use case examples (df81aca)
• Improve contributing section (1696799)
• Document n_bits for compile torch functions (0306c65)
• Add explanation of encrypted training and federated learning (57dbdff)
• Add documentation about scaling (9252f57)

v1.5.0-rc0

Summary

Add support to torch's Conv1d and Unfold operators

Links

Docker Image: zamafhe/concrete-ml:v1.5.0-rc0
Docker Hub: https://hub.docker.com/r/zamafhe/concrete-ml/tags
pip: https://pypi.org/project/concrete-ml/1.5.0-rc0
Documentation: https://docs.zama.ai/concrete-ml

v1.5.0-rc0

Feature

• Support conv1d operator (09ad7a6)
• Implement quantized unfold (fa3ef88)

Fix

• Make skorch import fail without error (81de55c)
• Replace python release install with setup-python (899b9f1)
• Fix concurrency issue in release process (1295ea9)
• Add support to AvgPool's missing parameters (15a8340)
• Update README.md (ba1fdad)

Documentation

• Update operator list in torch support's documentation section (b617740)
• Add links to credit card approval space in use case examples (df81aca)
• Improve contributing section (1696799)
• Document n_bits for compile torch functions (0306c65)
• Add explanation of encrypted training and federated learning (57dbdff)
• Add documentation about scaling (9252f57)

v1.4.1

Summary

Update Concrete-Python to 2.5.1 and fixes AvgPool's missing parameters.

Links

Docker Image: zamafhe/concrete-ml:v1.4.1
Docker Hub: https://hub.docker.com/r/zamafhe/concrete-ml/tags
pip: https://pypi.org/project/concrete-ml/1.4.1
Documentation: https://docs.zama.ai/concrete-ml

v1.4.1

Fix

• Make skorch import fail without error (5863a4b)
• Replace python release install with setup-python (f41c65c)
• Update README.md (8bef8e5)
• Add support to AvgPool's missing parameters (559d99c)

Documentation

• Document n_bits for compile torch functions (065df89)
• Add explanation of encrypted training and federated learning (8002ed8)
• Add documentation about scaling (1bad5cc)

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)

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)