concrete-ml

Summary

Concrete-ML now gives the user the possibility to deploy models in a client-server setting, separating encryption and decryption from execution, which can now be done by a remote machine. The release also adds support for new models and for new neural network layers, but also allows importing ONNX directly, thus supporting some keras/tensorflow models. Furthermore, this release provides some support for importing Quantization Aware Training neural networks, which contain quantizers in the operation graph and can be built with brevitas.

Links

Docker Image: zamafhe/concrete-ml:v0.3.0
pip: https://pypi.org/project/concrete-ml/0.3.0
Documentation: https://docs.zama.ai/concrete-ml

v0.3.0

Feature

• Allow recompiling from onnx model (9b69e73)
• Adding support for p_error (fe03441)
• Adding GELU activation (c732d15)
• Add random_state to models for client server reproducibility (6f887d0)
• Add QAT notebook (64c4512)
• Import Brevitas QAT networks (6c40a0c)
• Integration of the encrypt decrypt api (3c2a68a)
• Support more input types in predict() (76e142c)
• Support more input types in fit() (1fa74f7)
• Add Round and Pow operators (d6880ce)
• Ability to import Quantization Aware Training networks (c1bb947)
• Compile user supplied ONNX to support keras/tf (fae3dc5)
• Implement Generalized Linear Regression models (8e8e025)
• Add SoftSign activation (3ce338e)
• Adding more activations (e43ce5c)
• Implement Poisson Regression (09eefa5)
• Use the 8b of precision of Concrete Numpy (249c712)
• Add ONNX flatten support (c5f215f)
• Handle more tree-based classifiers (950cc6c)
• Add Batch Normalization ONNX operator (7969739)
• Add Where, Greater, Mul, Sub ONNX operator support (f939149)
• Add ONNX Average Pooling and Pad operator (40f1ef9)
• Add more activation functions (26b2221)

Fix

• Make tree inference faster by creating new numpy boolean operators (206caa5)
• Set a compatible version for protobuf (97ccfc0)
• Improve IRIS FCNN FHE accuracy and visualization (02e497c)
• Replace init call by set_params (111419e)
• Fix wrong fit_benchmark in linear models (f257def)
• Fix GridSearchCV on trees (b614285)
• Support decision tree with custom classes (baa3b4d)

Documentation

• Major refresh of 0.3 doc (e5e3205)
• Add sentiment classification notebook (68ae7d0)
• Restrict hyperparameters in titanic notebook for faster inference (9b63c8a)
• QAT explanation (9430ba6)
• Document ONNX compilation (972d05e)
• Explain quantized vs float ops and fusing (fb9b409)
• Add doc for pandas support (34652ce)
• Add notebook and docs client server api (99ff1e7)
• Developing custom models (1c6f571)
• Explain built-in quantized Neural Networks (972d464)
• Add a notebook for Kaggle Titanic competition (0a44853)

Summary

Fixing issues to updates in some dependancies that we used with a not-fixed version

Links

Docker Image: zamafhe/concrete-ml:v0.2.1
pip: https://pypi.org/project/concrete-ml/0.2.1
Documentation: https://docs.zama.ai/concrete-ml

v0.2.1

Fix

• Set a compatible version for protobuf (4dd46cf)
• Force ONNX package version to 1.11.0 in CLM 0.2 (fa90586)

Documentation

• Update the LICENSE (38c1062)

Summary

Use Concrete Numpy 0.5.
Add multi-class classification to XGBoost.
Fixing some minor broken links or issues.

Links

Docker Image: zamafhe/concrete-ml:v0.2.0
pip: https://pypi.org/project/concrete-ml/0.2.0
Documentation: https://docs.zama.ai/concrete-ml/ (old link https://docs.zama.ai/concrete-ml/0.2.0 has been moved)

v0.2.0

Breaking Changes (as compared to 0.1.x)

• run method is renamed to encrypt_run_decrypt after changes in Concrete-Numpy 0.5.0. Individual APIs to encrypt/run/decrypt separately will be available in a further release of Concrete-ML

Feature

• Some breaking changes in Concrete Numpy API. (ecbb26e)
• Using Concrete Numpy 0.5 (ee5987b)
• Add multiclass xgboost (9247f35)
• Adding a set_version_and_push command to the makefile (ab853c2)
• Add multiclass capability to decision trees (df0a64d)

Fix

• Fixing Pypi's Homepage button link (bf52514)

Breaking

• The API .forward_fhe.run() has been renamed into .forward_fhe.encrypt_run_decrypt() (ecbb26e)

Documentation

• Reorganize index page (f1bafff)

Summary

Use Concrete Numpy 0.4.
Fixing some minor broken links or issues.

Links

Docker Image: zamafhe/concrete-ml:v0.1.1
pip: https://pypi.org/project/concrete-ml/0.1.1
Documentation: https://docs.zama.ai/concrete-ml/0.1.1

v0.1.1

Feature

• Add multiclass capability to decision trees (6f9651e)
• Update to Concrete Numpy 0.4.0 and update the theme. (27839be)

Fix

• Fixing Pypi's Homepage button link (d1c0e5d)
• Broken links in README.md (0c47668)

Documentation

• Reorganize index page (349c947)

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)
• When we test determinism, it is fine if there is an issue in the underlying Concrete Numpy (6595495)
• Change the md5, even if the licence hasn't changed (182084f)
• Decision tree bug (84a65e4)
• Remove gpl lib + update sphinx-zama-theme ^2.2.0 (65aa1b2)
• Remove Hardsigmoid and Tanhshrink for a moment, since there are issues with precision (51c0bc5)
• Remove fc comparison fhe vs quantization (1c527be)
• Use right imports in docs (9fe43bf)
• Change qvalues to values in quantized module and fix iris notebook mistake (11c5616)
• Wrong fixture for a list + flaky test for decision tree + add fixture for model check is good execution (cc3c0b6)
• Add missing docstrings (0c164f5)
• Fix docstrings which are incomplete thanks to darglint (45d4fca)

Documentation

• Refresh notebooks (ff771aa)
• Update the theme (0d1e672)
• Update simple example readme (21d9a77)
• Readme (029237a)
• Update compute with quantization (b836811)
• Rewrite the developer section for Quantization, show how to to work with quantized operators (436e71e)
• Add Pruning docs (33b044f)
• Add info on skorch (6b3ca04)
• Adding documentation (ed9ee3f)
• Adding documentation (c4e73ec)
• Improve quantization explanation in the User Guide (4508282)
• Add a summary of our results (1046cc2)
• Write Virtual Lib documentation for release (4f68f3f)
• Add hummingbird usage (05103b3)
• Update docs for release (95a1669)
• Update our project setup doc (beef6c9)
• Update README (51ed1be)
• Add automatic ToC to README (4d51c96)
• Add source in docs (37227c6)
• Small update to the docker set up instructions (833d6e4)
• Update contributing to mention make conformance (bff86ca)
• No need to update releasing.md (179e235)
• Add a pruning section. (c977a32)
• No RF or SVM dedicated notebook (6307508)
• Warn the user that GLM and PoissonRegression are currently not natively in the package (e3e0234)
• Add Random Forest to our classifier comparison (858f193)
• Add XGBClassifier to our classifier comparison (eff1b15)
• Update our documentation (2b16560)
• Add a comparison of our classifiers (ce0d24b)
• Make the plan for the documentation (0306cee)
• Add a sentence about quantized module 237 (a440de3)
• Use 2.1.0 theme (4fb1445)
• Add starter docs for how ONNX is used internally (16978b6)
• Add relevant docs from concrete-numpy (235322a)
• Check mdformat (c29504a)