HE Transformer for nGraph
This project has moved to https://github.com/IntelAI/he-transformer
The Intel® HE transformer for nGraph™ is a Homomorphic Encryption (HE) backend to the Intel® nGraph Compiler, Intel's graph compiler for Artificial Neural Networks.
Homomorphic encryption is a form of encryption that allows computation on encrypted data, and is an attractive remedy to increasing concerns about data privacy in the field of machine learning. For more information, see our original paper. Our updated paper showcases many of the recent advances in he-transformer.
This project is meant as a proof-of-concept to demonstrate the feasibility of HE on local machines. The goal is to measure performance of various HE schemes for deep learning. This is not intended to be a production-ready product, but rather a research tool.
Currently, we support the CKKS encryption scheme, implemented by the Simple Encrypted Arithmetic Library (SEAL) from Microsoft Research.
Additionally, we integrate with the Intel® nGraph™ Compiler and runtime engine for TensorFlow to allow users to run inference on trained neural networks through Tensorflow.
Examples
The examples folder contains a deep learning example which depends on the Intel® nGraph™ Compiler and runtime engine for TensorFlow.
Building HE Transformer
Dependencies
- Operating system: Ubuntu 16.04, Ubuntu 18.04.
- CMake >= 3.12
- Compiler: g++ version >= 6.0, clang >= 5.0
- OpenMP is strongly suggested, though not strictly necessary. You may experience slow runtimes without OpenMP
- python3 and pip3
- virtualenv v16.1.0
- bazel v0.25.2
For a full list of dependencies, see the docker containers, which build he-transformer on a reference OS.
The following dependencies are built automatically
- nGraph - v0.27.0-rc.0
- nGraph-tf - v0.21.0-rc0
- SEAL - v3.4.2
- TensorFlow - v1.14.0
- Boost v1.69
- Google protobuf v3.10.1
To install bazel
wget https://github.com/bazelbuild/bazel/releases/download/0.25.2/bazel-0.25.2-installer-linux-x86_64.sh
bash bazel-0.25.2-installer-linux-x86_64.sh --user
Add and source the bin path to your ~/.bashrc file to call bazel
export PATH=$PATH:~/bin
source ~/.bashrc
1. Build HE-Transformer
Before building, make sure you deactivate any active virtual environments (i.e. run deactivate)
git clone https://github.com/NervanaSystems/he-transformer.git
cd he-transformer
export HE_TRANSFORMER=$(pwd)
mkdir build
cd $HE_TRANSFORMER/build
cmake .. -DCMAKE_CXX_COMPILER=clang++-6.0
make install
source external/venv-tf-py3/bin/activate
Note, you may need sudo permissions to install he_seal_backend to the default location. To set a custom installation prefix, add the -DCMAKE_INSTALL_PREFIX=~/my_install_prefix flag to the cmake command.
1a. To build documentation
First install doxygen, i.e.
sudo apt-get install doxygen
Then add the following CMake flag
cmake .. -DNGRAPH_HE_DOC_BUILD_ENABLE=ON
and call
make docs
to create doxygen documentation in $HE_TRANSFORMER/build/doc/doxygen.
1b. Python bindings for client
To build a client-server model with python bindings (recommended for running neural networks through TensorFlow):
cd $HE_TRANSFORMER/build
source external/venv-tf-py3/bin/activate
make install python_client
This will create python/dist/pyhe_client-*.whl. Install it using
pip install python/dist/pyhe_client-*.whl
To check the installation worked correctly, run
python3 -c "import pyhe_client"
This should run without errors.
2. Run C++ unit-tests
cd $HE_TRANSFORMER/build
# To run single HE_SEAL unit-test
./test/unit-test --gtest_filter="HE_SEAL.add_2_3_cipher_plain_real_unpacked_unpacked"
# To run all C++ unit-tests
./test/unit-test
3. Run python examples
See examples/README.md for examples of running he-transformer for deep learning inference on encrypted data.
Code formatting
Please run maint/apply-code-format.sh before submitting a pull request.