CrypTen Documentation

CrypTen is a Privacy Preserving Machine Learning framework written using PyTorch that allows researchers and developers to train models using encrypted data. CrypTen currently supports Secure multi-party computation as its encryption mechanism.

Installation on Linux and Mac

We recommend installing CrypTen in its own conda environment. Please install Anaconda Python 3.7 before doing the following steps

For Linux or Mac

$ pip install crypten

To check if your installation is working, you can run the unit tests by cloning the repo then

$ python3 -m unittest discover test

We do not support Windows yet. For contributing to the latest development version, please see Contributing.


To run the examples in the examples directory, you additionally need to do the following

$ pip install -r requirements.examples.txt

We have the following examples, covering a range of models

  • The linear SVM example, mpc_linear_svm, generates random data and trains a SVM classifier on encrypted data.

  • The LeNet example, mpc_cifar, trains an adaptation of LeNet on CIFAR in cleartext and encrypts the model and data for inference

  • The TFE benchmark example, tfe_benchmarks, trains three different network architectures on MNIST in cleartext, and encrypts the trained model and data for inference

  • The bandits example, bandits, trains a contextual bandits model on encrypted data (MNIST)

  • The imagenet example, mpc_imagenet, does inference on pretrained model from torchvision

For examples that train in the cleartext, we also provide pre-trained models in cleartext in model subdirectory of each example.

You can check all example specific command line options by doing the following; shown here for tfe_benchmarks

$ python3 examples/tfe_benchmarks/ --help

Some MPC specific options are

  • --world_size Number of peers in MPC

  • --multiprocess Run in multiprocess mode on one machine, where each peer is a separate process

Examples on AWS

CrypTen also provides a script aws_launcher to launch examples with encrypted data on multiple AWS instances. See Launch on AWS.

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