v1.8.0

Summary

Concrete ML v1.8 improves the functionality for fine-tuning LLMs on encrypted data. This release adds a new, GPU-enabled high-performance cryptographic back-end specialized for this task, improving the speed and usability of hybrid model compilation and testing. Furthermore, the developer experience was improved by using the GPU to speed up LLM hybrid model compilation and testing. Additionally, Concrete ML v1.8 adds support for Python 3.12, aligning with the latest development environment.

What's Changed

New features

• Support Python 3.12 (#847) (0edf038)
• Integrate a faster GPU backend for hybrid model (#868) ([3be0d42](3be0d42d49565df04abff8e332fbad37>
• Speed up hybrid model compilation with GPU (#963)

Fix

• Faster rounding test for weekly builds (#957) ([5b9466c](5b9466ccbe782a9a1bbb0466f6440be>
• Remove custom QAT test (#956) ([1ccaceb](1ccaceb67f420b2fcafd24d2f66e396b53e70cc>
• Add Python 3.12 test in CI pipeline (#950) (96a94c4)
• Propagate GLWE configuration on hybrid MLP unit test (#935) ([664cbd4](664cbd472d044b43>

Resources

Demo & Examples

• Add LORA fine tuning demo for LLAMA 3.2 (#958) ([ef602d9](ef602d9d00a600fcc9edd5a9b3f>

v1.7.0

Summary

Concrete ML 1.7 adds functionality to fine-tune LLMs and neural networks on encrypted data using low rank approximation parameter efficient fine-tuning. This allows users to securely outsource large weight matrix computations to remote servers while keeping a small set of private fine-tuned parameters locally. Additionally, this release includes GPU support, providing a 1-2x speed-up for large neural networks on server-grade GPUs like the NVIDIA H100. Concrete ML now also supports Python 3.11 and PyTorch 2.

What's Changed

New features

• Lora fine-tuning in FHE with MLP tutorial and gpt2 use case example (#823) (4d2f2e6)
• Add GPU support (#849) (945aead)
• Add support for Python3.11 (#701) (819dca7)
• Support for embedding layers (#778) (296bc8c)
• Support for encrypted multiplication and division (#690) (a1bd9b8)
• Upgrade PyTorch to 2.3.1, and Brevitas to 0.10 (#788) (c3d7c81)

Improvements

• Relax Python version restrictions for deployment (#853) (040c308)
• Remove Protobuf 2GB limit when checking ONNX (#811) (c8908fa)
• Always using evaluation key compression (#726) (b4e1060)

Fixes

• Fix dtype check in quantizer dequant (77ced60)
• Dynamic import of transformers in hybrid model (829b68b)
• Use correct torch version for Intel Mac (#798) (a8eab89)

Resources

• Documentation:

• Add encrypted LORA fine-tuning documentation (#887) (3f65ec2)
• Add GPU usage guide (#786) (1988936)

• Demo & Examples:
• Encrypted DNA Ancestry : https://huggingface.co/spaces/zama-fhe/encrypted_dna

v1.6.1

Summary

Minor fixes.

Links

Docker Image: zamafhe/concrete-ml:v1.6.1
Docker Hub: https://hub.docker.com/r/zamafhe/concrete-ml/tags
pip: https://pypi.org/project/concrete-ml/1.6.1
Documentation: https://docs.zama.ai/concrete-ml

v1.6.1

Fix

• Dynamic import of transformers in hybrid model (270efbe)

v1.6.0

Summary

Concrete ML 1.6 includes the following enhancements:

• Latency improvements on large neural networks
• Support for pre-trained tree-based models such as those trained using Federated Learning
• Enhanced collaborative computation
  • Introduction of DataFrame schemas
  • Deployment of logistic regression training

What's Changed

New features

• Enable non-interactive encrypted training for logistic regression (#660) (ec58bca)
• Support pre-trained tree-based models using from_sklearn (5ca282b)
• Add FHE training deployment (#665) (b718629)
• Support approximate rounding to speed up neural networks (9ef890e)
• Allow users to define a schema for dataframe encryption (‘ccd6641’)

Fixes

• Fix fhe-training classes behavior (a88d704)
• Update qgpt2_class.py to fix typo (d376d85)
• Fix post-processing shape mismatches for linear models (#585) (b097022)
• Disable overflow protection in rounding (4db0157)
• Make skorch import fail without error (81de55c)

Improvements

• Replace python release install with setup-python (899b9f1)
• Add support to AvgPool's missing parameters (15a8340)

Resources

• Documentation:

  • Add schema example for encrypted data-frames (#715) (b174509)
  • Add a license FAQ in README (#711) (f937c9f)
  • Update documentation on client / server API (#663) (f864407)
  • Document n_bits for compile torch functions (0306c65)
  • Add examples for the impact of feature scaling on linear models (9252f57)

• Demo & Examples:

  • Add NN-20 and NN-50 deep MLPs for MNIST classification (1b5ce84)

v1.6.0-rc3

Summary

1.6.0 - Release Candidate - 3

v1.6.0-rc0

Summary

1.6.0 - Release Candidate - 0

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)