v0.1.0

Summary

First release of Concrete-ML package

Links

Docker Image: zamafhe/concrete-ml:v0.1.0
pip: https://pypi.org/project/concrete-ml/0.1.0
Documentation: https://docs.zama.ai/concrete-ml/0.1.0

v0.1.0

Feature

• Add tests for more torch functions that are supported, mention them in the docs (0478854)
• Add FHE in xgboost notebook (1367d4e)
• Make all classifier demos run in FHE for the datasets and in VL for the domain grid (d95af58)
• Remove workaround reshape and remaining 3dmatmul (28ea1eb)
• Change predict to predict_proba for average_precision (a057881)
• Allow FHE on xgboost (7b5c118)
• Add CNN notebook (4acca2f)
• Optimize QuantizeAdd to use TLUs when one of the inputs is a constant(1ffcdfb)
• Different n_bits for weights/activations/outputs (321d151)
• Add virtual lib management to SklearnLinearModelMixin (596d16e)
• Add quantized CNN. (1a78593)
• Start refactoring tree based models (8e62cf8)
• Set symmetric quantization by default in PTQ (8fcd307)
• Add random forest + benchmark (5630f17)
• Allow base_score with xgboost (17d5cc4)
• Add predict_proba to logistic regression (9aaeec5)
• Add xgboost (699603d)
• Add NN regression benchmarks (9de2ba4)
• Add symetric quantization (needed for tree output) (4a173ee)
• Implement LinearSVC (d048077)
• Implement LinearSVRegression (36df77e)
• Remove identity nodes from ONNX models (9719c08)
• Add binary + multiclass logistic regression (85c25df)
• Improve r2 test for low variance targets. (44ec0b3)
• Add sklearn linear regression model (060a4c6)
• Add virtual lib basic class (ad32509)
• Improve NN benchmarks (ae8313e)
• Add NN benchmarks and sklearn wrapper for FHE NNs (e73a514)
• More efficient numpy_gemm, since traced (609f1df)
• Integrate hummingbird (01c3a4a)
• Add ONNX quantized implementation for MatMul and Add (716fc43)
• Allow multiple inputs for a QuantizedModule (1fa530d)
• Allow QuantizedModule to handle complicated NN topologies (da91e40)
• Let's allow (alpha, beta) == (1, 0) in Gemm (4b9927a)
• Manage constant folding in PTQ (a0c56d7)
• Replace numpy.isclose with r2 score (65f0a6e)
• Replace the torch quantization functions with ones usable with ONNX (ecdeb50)
• Add test when input is float to quantized module (d58910d)
• Let user chose its error type (e5d7440)
• Post training quantization for ONNX repr (8b051df)
• Adding more activations and numpy functions (73d885c)
• Let's have relu and relu6 (f64c3bf)
• Add quantized tanh (ca9c6e5)
• Add classification benchmarks, fix bugs in DecisionTreeClassifier (d66d7bf)
• Provide quantized versions of ONNX ops (b63eca2)
• Add darglint as a pluggin of flake8 (bb568e2)
• Use ONNX as intermediate format to convert torch models to numpy (072bd63)
• Add decision trees + update notebook (db163f5)
• Restore quantized model benchmarks (d1cfc4e)
• Port quantization and torch from concrete-numpy. (a525e8b)

Fix

• Remove fixmes, add HardSigmoid (847db99)
• Docs (8096acc)
• Safer default parameter for ensemble methods (8da0988)
• Increase n_bits for clear vs quantized comparison for decision tree (b9f1206)
• Fix notebook on macOS + some warnings (ab2a821)
• Xgboost handle the edge case where n_estimators = 1 (3673584)
• Issues in Classifier Comparison notebook (3053085)
• One more bug about convergence (c6cee4e)
• Fix convergence issues in tests (7b92bd8)
• Remove metric evaluation for n_bits < 16 (7c4bd0e)
• Wrong xgboost init (2ed49b6)
• Workaround while #518 is being investigated (7f521f9)
• Looks like a mistake (69e9b15)
• Speedup qnn tests (9d07f5c)
• Workaround for segfaults on macOS (798662f)
• Remove check_r2_score with argmax predictions (7d52750)
• Review (82abb12)
• Fully connected notebook ([1f7b92e](1f7b92e2623ebf45...