Concrete ML: Privacy Preserving ML framework built on top of Concrete, with bindings to traditional ML frameworks.
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Add support for encrypted data-frames, approximate rounding and PyTorch's Conv1D
operator. Fix AvgPool's count_include_pad missing parameter error.
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
9ef890e
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)d3bf5ac
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)85cb962
)b617740
)df81aca
)1696799
)0306c65
)57dbdff
)9252f57
)Published by zama-bot 7 months ago
Add support for encrypted data-frames and approximate rounding. Fix AvgPool's count_include_pad
missing parameter error.
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
9ef890e
)d2d6250
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)fa3ef88
)e5661c1
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)1295ea9
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)d3bf5ac
)cfb862e
)85cb962
)b617740
)df81aca
)1696799
)0306c65
)57dbdff
)9252f57
)Published by zama-bot 8 months ago
Update Concrete-Python to 2.5.1 and fixes AvgPool's missing parameters.
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
5863a4b
)f41c65c
)8bef8e5
)559d99c
)Published by zama-bot 8 months ago
Add support to torch's Conv1d and Unfold operators
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
81de55c
)899b9f1
)1295ea9
)15a8340
)ba1fdad
)b617740
)df81aca
)1696799
)0306c65
)57dbdff
)9252f57
)Published by zama-bot 9 months ago
1.4.0 release
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
0893718
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)064eb82
)fef23a9
)0b57c71
)111c7e3
)1dc547e
)1a592d7
)4f67883
)e4984d6
)Published by zama-bot 10 months ago
1.4.0 - Release Candidate - 2
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
0893718
)cf3ce49
)064eb82
)fef23a9
)0b57c71
)111c7e3
)1dc547e
)1a592d7
)4f67883
)e4984d6
)Published by zama-bot 10 months ago
Please fill here with information about the main features in this release, or the main reason for having a delivery (e.g., fixing an annoying bug)
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
cf3ce49
)064eb82
)fef23a9
)0b57c71
)111c7e3
)1dc547e
)1a592d7
)4f67883
)e4984d6
)Published by zama-bot 12 months ago
Adds SGDRegressor built-in model and some bugfixes.
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
abb143c
)6de7c6e
)Published by zama-bot about 1 year ago
Bug fix for XGBoostRegressor.
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
Published by zama-bot about 1 year ago
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.
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
771c7ff
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)3bad62e
)e4db376
)f2822d1
)68a0b4c
)f80c90b
)6e3d003
)39480ef
)0cf1174
)Published by zama-bot about 1 year ago
Trying out the new release process.
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
be6aa6e
)3da6408
)3bad62e
)Published by zama-bot over 1 year ago
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.
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
Published by zama-bot over 1 year ago
Expose training parameters from scikit-learn in built-in models and add new advanced examples.
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
6e1315f
)34e5f68
)37ab8c0
)33312a8
)Published by zama-bot over 1 year ago
Fix for the Gather operator to handle fancy indexing.
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
Published by zama-bot over 1 year ago
Fixing minor things, like few typos or rewording the documentation.
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
Published by zama-bot over 1 year ago
This version features a stable API, better inference performance, and user-friendly error reporting. Most importantly, tools have been added to make your model deployment in cloud environments hassle-free. Support for Apple Silicon was also added.
Docker Image: zamafhe/concrete-ml:v1.0.0
pip: https://pypi.org/project/concrete-ml/1.0.0
Documentation: https://docs.zama.ai/concrete-ml
70bff38
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)9177058
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)3eb86c1
)a0c22fa
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)5454620
)d298459
)ef6355f
)b7f56d5
)d4681fb
)Published by bcm-at-zama almost 2 years ago
This Concrete-ML release adds support for:
New tutorials show how to train large neural networks either from scratch or by transfer learning, how to convert them into an FHE-friendly models and finally how to evaluate them in FHE and with simulation. The release adds tools that leverage FHE simulation to select optimal parameters that speed up the inference of neural networks. Python 3.10 support is included in this release.
Docker Image: zamafhe/concrete-ml:v0.6.1
pip: https://pypi.org/project/concrete-ml/0.6.1
Documentation: https://docs.zama.ai/concrete-ml
4f112ca
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)a52d917
)b54fcac
)1780cd5
)52e87b7
)b7fa8c1
)f2dfc3e
)1495214
)5196c68
)Published by fd0r almost 2 years ago
The main objective of this release is to fix some issues with recent updates in dependencies, and to extend the support from python 3.7.14 to python 3.7.1.
Docker Image: zamafhe/concrete-ml:v0.5.1
pip: https://pypi.org/project/concrete-ml/0.5.1
Documentation: https://docs.zama.ai/concrete-ml
eb212bf
)ebd06b4
)Published by fd0r almost 2 years ago
The main objective of this release is to add python 3.7 support.
Docker Image: zamafhe/concrete-ml:v0.5.0
pip: https://pypi.org/project/concrete-ml/0.5.0
Documentation: https://docs.zama.ai/concrete-ml
fef90d1
)89668bf
)24e8f88
)14502f0
)Published by fd0r about 2 years ago
This version of Concrete-ML adds more support for quantization-aware training neural networks, adds decision tree-ensemble regressors, and includes additional linear regression models. For custom models, first-class support for Brevitas quantization aware training neural networks was added: a dedicated function imports models containing Brevitas layers directly. Design rules for these neural networks using Brevitas are detailed in the documentation. Moreover, quantization aware training is now the default for built-in neural networks, giving good accuracy out-of-the-box, with low bit-widths for weights, activations, and accumulators. Tree-based RandomForest and XGBoost regression models are now supported, while the linear regressors are complemented by the Ridge, Lasso, and ElasticNet models. Many usage example notebooks were added, showing how to use the new models, and also showing more complex use-cases such as sentiment analysis, MNIST classification, and Kaggle Titanic dataset classification.
Docker Image: zamafhe/concrete-ml:v0.4.0
pip: https://pypi.org/project/concrete-ml/0.4.0
Documentation: https://docs.zama.ai/concrete-ml/v/0.4/
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