Skip to main content

Train PyTorch models with Differential Privacy

Project description

Opacus


CircleCI

Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the client, has little impact on training performance and allows the client to online track the privacy budget expended at any given moment.

Target audience

This code release is aimed at two target audiences:

  1. ML practitioners will find this to be a gentle introduction to training a model with differential privacy as it requires minimal code changes.
  2. Differential Privacy scientists will find this easy to experiment and tinker with, allowing them to focus on what matters.

Installation

The latest release of Opacus can be installed via pip:

pip install opacus

You can also install directly from the source for the latest features (along with its quirks and potentially ocassional bugs):

git clone https://github.com/pytorch/opacus.git
cd opacus
pip install -e .

Getting started

To train your model with differential privacy, all you need to do is to declare a PrivacyEngine and attach it to your optimizer before running, eg:

model = Net()
optimizer = SGD(model.parameters(), lr=0.05)
privacy_engine = PrivacyEngine(
    model,
    batch_size,
    sample_size,
    alphas=[10, 100],
    noise_multiplier=1.3,
    max_grad_norm=1.0,
)
privacy_engine.attach(optimizer)
# Now it's business as usual

The MNIST example shows an end-to-end run using opacus. The examples folder contains more such examples.

FAQ

Checkout the FAQ page for answers to some of the most frequently asked questions about Differential Privacy and Opacus.

Contributing

See the CONTRIBUTING file for how to help out.

References

License

This code is released under Apache 2.0, as found in the LICENSE file.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

opacus-0.9.1.tar.gz (55.0 kB view details)

Uploaded Source

Built Distribution

opacus-0.9.1-py3-none-any.whl (76.4 kB view details)

Uploaded Python 3

File details

Details for the file opacus-0.9.1.tar.gz.

File metadata

  • Download URL: opacus-0.9.1.tar.gz
  • Upload date:
  • Size: 55.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/50.3.0 requests-toolbelt/0.9.1 tqdm/4.49.0 CPython/3.6.9

File hashes

Hashes for opacus-0.9.1.tar.gz
Algorithm Hash digest
SHA256 715c1f38994a408806af814ec4fc0cbd69f17f026941cb69d75cdf4b57d3685a
MD5 548f399af555498b397b6537bee474a5
BLAKE2b-256 1cc30306b7c576310e4e311492826eae3703cd21d6ee5e4fe6f3b53a20388d84

See more details on using hashes here.

Provenance

File details

Details for the file opacus-0.9.1-py3-none-any.whl.

File metadata

  • Download URL: opacus-0.9.1-py3-none-any.whl
  • Upload date:
  • Size: 76.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/50.3.0 requests-toolbelt/0.9.1 tqdm/4.49.0 CPython/3.6.9

File hashes

Hashes for opacus-0.9.1-py3-none-any.whl
Algorithm Hash digest
SHA256 6b9b0748005a39666b451282536522f274c6fcb397d77ffd8619b5b0c0c27473
MD5 169267de406b755905a72b7e56724ff9
BLAKE2b-256 e2236628d4135046eacc3a216b7c2e89e5466a7a48a709d8f6a026c71e8e6703

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page