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 paper.
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.
The examples directory contains a deep learning example which depends on the Intel® nGraph™ Compiler and runtime engine for TensorFlow.
- We currently only support Ubuntu 16.04
- CMake >= 3.10, although different versions may work
- GCC version 7, although different versions may work
- OpenMP is strongly suggested, though not strictly necessary. You may experience slow runtimes without OpenMP
- python3.5 and pip3
- virtualenv v16.1.0
- bazel v0.21.0
- nGraph - v0.19.1
- nGraph-tf - v0.14.0
- SEAL - v3.3
- TensorFlow - v1.13.1
- Boost 1.69
wget https://github.com/bazelbuild/bazel/releases/download/0.21.0/bazel-0.21.0-installer-linux-x86_64.sh
chmod +x bazel-0.21.0-installer-linux-x86_64.sh
./bazel-0.21.0-installer-linux-x86_64.sh --user
Make sure to add and source the bin path to your ~/.bashrc
file in order to be able to call bazel from the user's installation we set up:
export PATH=$PATH:~/bin
source ~/.bashrc
Before building, make sure you deactivate any active virtual environments (i.e. run deactivate
)
git clone https://github.com/NervanaSystems/he-transformer.git
cd he-transformer
export HE_TRANSFORMER=$(pwd)
mkdir build
cd $HE_TRANSFORMER/build
cmake .. -DCMAKE_CXX_COMPILER=g++-7 -DCMAKE_C_COMPILER=gcc-7
make install
source external/venv-tf-py3/bin/activate
The first build will compile Tensorflow. To speed up subsequent builds, you can avoid compiling Tensorflow by calling
cmake .. -DUSE_PREBUILT_TF=ON -DCMAKE_CXX_COMPILER=g++-7 -DCMAKE_C_COMPILER=gcc-7
To build an experimental client-server model with python bindings, see the python
folder.
Note: This feature is experimental. For best experience, you should skip this step.
Ensure the virtual environment is active, i.e. run source $HE_TRANSFORMER/build/external/venv-tf-py3/bin/activate
cd $HE_TRANSFORMER/build
# To run single HE_SEAL unit-test
./test/unit-test --gtest_filter="HE_SEAL.add_2_3"
# To run all C++ unit-tests
./test/unit-test
Ensure the virtual environment is active, i.e. run source $HE_TRANSFORMER/build/external/venv-tf-py3/bin/activate
cd $HE_TRANSFORMER/examples
# Run with CPU
python axpy.py
# To run CKKS unit-test
NGRAPH_TF_BACKEND=HE_SEAL python axpy.py
For a deep learning example, see examples/cryptonets/.
Please run maint/apply-code-format.sh
before submitting a pull request.